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1
Lee, S., J. Yang, S. Nam, J. Lee, W. Kim, J. Choi, G. Kim, and G. Kim. "Triple detection method for sentinel lymph node detection." Journal of Clinical Oncology 27, no. 15_suppl (May 20, 2009): e11605-e11605. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.e11605.
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e11605 Background: Sentinel lymph node biopsy is widely accepted method to determine nodal stage of breast cancer. There are several reported method for detecting sentinel lymph node. The aim of this study was to show the new detection method of sentinel lymph node and show the effectiveness of this method. Methods: We did prospective study and enrolled 25 patients who underwent partial mastectomy and sentinel lymph node biopsy. We injected indigocyanine green (green dye) at peritumoral lesion, indigocarmine dye (blue dye) in subareolar area and radioisotope (Tc-99m) injection. Sentinel lymph nodes are identified by color change or radioisotope uptake, and classified by each color (blue or green) and radioisotope uptake. We compared the detection rate from our study with that from the previous studies. Results: Sentinel lymph nodes were detected in all patients (25/25). Green color stained sentinel lymph nodes were identified in 18 patients (18/25), blue color stained sentinel lymph nodes were identified in 15 patients (15/25) and radioactive lymph nodes were identified in 19 patients (19/25). Conclusions: The triple mapping method showed higher detection rate than the previous studies and this method is recommendable to detect sentinel lymph node. No significant financial relationships to disclose.
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Imam Rahmani, Mohammad Khalid, Moizuddin Mohammed, Reyazur Rashid Irshad, Sadaf Yasmin, Swati Mishra, Pooja Asopa, Asharul Islam, Sultan Ahmad, and Aleem Ali. "Design a Secure Routing and Monitoring Framework Based on Hybrid Optimization for IoT-Based Wireless Sensor Networks." Journal of Nanoelectronics and Optoelectronics 18, no. 3 (March 1, 2023): 338–46. http://dx.doi.org/10.1166/jno.2023.3397.
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Wireless Sensor Networks (WSNs) have employed in recent years for many different applications and functions. But, it has the critical task to detect the malicious node because node malicious attacks are dangerous attacks, and the concept of a malicious attack is opponents enter the network, search accidentally, and capture one or more normal nodes. A lot of research developed to overcome this problem, but no precise results are found. In this paper, design a Hybrid Vulture and African Buffalo with Node Identity Verification (HVAB-NIV) model to predict the malicious nodes in the WSN. The fitness functions of the HVAB-NIV have operated to recognize the energy level of each node and improve the performance of node detection. The developed replica includes three stages that monitor each node, calculate the energy level and detect the malicious node. More than 100 node inputs were initialized in the proposed technique and implemented in the MATLAB tool. The suggested mechanism enhances the performance of malicious node detection and gains good accuracy for detecting nodes also, it saves running time and power consumption. The experimental results of the developed model has validated with other existing replicas to running time, False Prediction Rate (FPR), detection accuracy, True Prediction Rate (TPR), and power consumption. The developed methods achieve better results by gaining a high rate of accuracy detection, less running time, and false rate detection.
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Lei, Yang, and Ying Jiang. "Anomaly Detection for Nodes Under the Cloud Computing Environment." International Journal of Distributed Systems and Technologies 12, no. 1 (January 2021): 30–48. http://dx.doi.org/10.4018/ijdst.2021010103.
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Due to the services diversity and dynamic deployment, the anomalies will occur on nodes under cloud computing environment. If a single node generates an anomaly, the associated nodes are affected by the abnormal node, which will result in anomaly propagation and nodes failure. In this paper, a method of anomaly detection for nodes under the cloud computing environment is proposed. Firstly, the node monitoring model is established by the agents deployed on each node. Secondly, the comprehensive score is used to identify abnormal data. The anomaly of the single node is judged by the time window-based method. Then, the status of directly associated nodes is detected through normalized mutual information and the status of indirectly associated nodes is detected through the node attributes in the case of a single node anomaly. Finally, other associated nodes affected by the abnormal node are detected. The experimental results showed that the method in this paper can detect the anomalies of single node and associated node under the cloud computing environment effectively.
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Masoud, Mohammad Z., Yousef Jaradat, Ismael Jannoud, and Mustafa A. Al Sibahee. "A hybrid clustering routing protocol based on machine learning and graph theory for energy conservation and hole detection in wireless sensor network." International Journal of Distributed Sensor Networks 15, no. 6 (June 2019): 155014771985823. http://dx.doi.org/10.1177/1550147719858231.
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In this work, a new hybrid clustering routing protocol is proposed to prolong network life time through detecting holes and edges nodes. The detection process attempts to generate a connected graph without any isolated nodes or clusters that have no connection with the sink node. To this end, soft clustering/estimation maximization with graph metrics, PageRank, node degree, and local cluster coefficient, has been utilized. Holes and edges detection process is performed by the sink node to reduce energy consumption of wireless sensor network nodes. The clustering process is dynamic among sensor nodes. Hybrid clustering routing protocol–hole detection converts the network into a number of rings to overcome transmission distances. We compared hybrid clustering routing protocol–hole detection with four different protocols. The accuracy of detection reached 98%. Moreover, network life time has prolonged 10%. Finally, hybrid clustering routing protocol–hole detection has eliminated the disconnectivity in the network for more than 80% of network life time.
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Ezeh, Chinenye, Ren Tao, Li Zhe, Wang Yiqun, and Qu Ying. "Multi-Type Node Detection in Network Communities." Entropy 21, no. 12 (December 17, 2019): 1237. http://dx.doi.org/10.3390/e21121237.
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Patterns of connectivity among nodes on networks can be revealed by community detection algorithms. The great significance of communities in the study of clustering patterns of nodes in different systems has led to the development of various methods for identifying different node types on diverse complex systems. However, most of the existing methods identify only either disjoint nodes or overlapping nodes. Many of these methods rarely identify disjunct nodes, even though they could play significant roles on networks. In this paper, a new method, which distinctly identifies disjoint nodes (node clusters), disjunct nodes (single node partitions) and overlapping nodes (nodes binding overlapping communities), is proposed. The approach, which differs from existing methods, involves iterative computation of bridging centrality to determine nodes with the highest bridging centrality value. Additionally, node similarity is computed between the bridge-node and its neighbours, and the neighbours with the least node similarity values are disconnected. This process is sustained until a stoppage criterion condition is met. Bridging centrality metric and Jaccard similarity coefficient are employed to identify bridge-nodes (nodes at cut points) and the level of similarity between the bridge-nodes and their direct neighbours respectively. Properties that characterise disjunct nodes are equally highlighted. Extensive experiments are conducted with artificial networks and real-world datasets and the results obtained demonstrate efficiency of the proposed method in distinctly detecting and classifying multi-type nodes in network communities. This method can be applied to vast areas such as examination of cell interactions and drug designs, disease control in epidemics, dislodging organised crime gangs and drug courier networks, etc.
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Feng, You Bing, and Rong Biao Zhang. "Fault Detection of WSN Based on Spatial Correlation." Applied Mechanics and Materials 58-60 (June 2011): 1504–10. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.1504.
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Regarding application with smooth variation of detection, spatial correlation of sensors’ data within a small field was applied to sensor nodes’ fault diagnosis. The data were sorted into several continuous sequences by sink node. Sequence with minimum variance was regarded as normal data to determine normal nodes. For undetermined nodes, it can be determined via calculation on deviation to normal nodes’ data of vicinity area. If deviation does not exceed the threshold, the node is normal; otherwise, it is regarded as a fault node. The research on WSN in a greenhouse shows that fault node can be effectively detected in time by this method.
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Li, Feng, Yali Si, Ning Lu, Zhen Chen, and Limin Shen. "A Security and Efficient Routing Scheme with Misbehavior Detection in Delay-Tolerant Networks." Security and Communication Networks 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/2761486.
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Due to the unique network characteristics, the security and efficient routing in DTNs are considered as two great challenges. In this paper, we design a security and efficient routing scheme, called SER, which integrates the routing decision and the attacks detection mechanisms. In SER scheme, each DTNs node locally maintains a one-dimensional vector table to record the summary information about the contact with other nodes and the trust degree of other nodes. To obtain the global status and the contact relationship among all nodes, the trusted routing table consisting of vectors of all nodes is built in each DTNs node. The method for detecting malicious nodes and selfish nodes is proposed, which exploits the global summary information to analyze the history forwarding behavior of node and judge whether it is a malicious node or selfish node. The routing decision method is proposed based on trust degree of forwarding messages between nodes, which adopts trust degree as relay node selection strategy. Simulation results show that compared with existing schemes SER scheme could detect the attacks behavior of malicious nodes and selfish nodes, at the same time, with higher delivery rate and lower average delivery delay.
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M, Sajitha, D. Kavitha, and P. Chenna Reddy. "Clone Node Detection in Heterogeneous WSN with Low Memory Overhead." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (September 30, 2022): 21–26. http://dx.doi.org/10.35940/ijrte.c7206.0911322.
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In unattended areas, the wireless sensor network is deployed and the nodes are always open to attacks. An adversary can capture a node and can deploy many nodes, which are clone of the captured, in the network called clone node or replicated node by using the credential information retrieved from captured node. These clone nodes can damage the network directly or indirectly. This attack is called node replication o attack or clone node attack. In this area, so many works are introduced and all of these methods use a random key or code, or location information, to detect clone nodes. This paper presents a method that does not use any of this information. The simulation results show that it performs better than previous methods.
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Ganesh, P., G. B. S. R. Naidu, Korla Swaroopa, R. Rahul, Ahmad Almadhor, C. Senthilkumar, Durgaprasad Gangodkar, A. Rajaram, and Alazar Yeshitla. "Implementation of Hidden Node Detection Scheme for Self-Organization of Data Packet." Wireless Communications and Mobile Computing 2022 (March 26, 2022): 1–9. http://dx.doi.org/10.1155/2022/1332373.
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The mobile nodes are infrequent movement in nature; therefore, its packet transmission is also infrequent. Packet overload occurred for routing process, and data are lossed by receiver node, since hackers hide the normal routing node. Basically, the hidden node problem is created based on the malicious nodes that are planned to hide the vital relay node in the specific routing path. The packet transmission loss occurred for routing; so, it minimizes the packet delivery ratio and network lifetime. Then, proposed enhanced self-organization of data packet (EAOD) mechanism is planned to aggregate the data packet sequencially from network structure. The hacker node present in routing path is easy to separate from network with trusty nodes. In order to secure the regular characteristics of organizer node from being confirmed as misbehaving node, the hidden node detection technique is designed for abnormal routing node identification. This algorithm checks the neighboring nodes that are hacker node, which hide the trust node in the routing path. And that trust nodes are initially found based on strength value of every node and assign path immediately. It increases network lifetime and minimizes the packet loss rate.
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Sindhuja, L. S., and G. Padmavathi. "Replica Node Detection Using Enhanced Single Hop Detection with Clonal Selection Algorithm in Mobile Wireless Sensor Networks." Journal of Computer Networks and Communications 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/1620343.
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Security of Mobile Wireless Sensor Networks is a vital challenge as the sensor nodes are deployed in unattended environment and they are prone to various attacks. One among them is the node replication attack. In this, the physically insecure nodes are acquired by the adversary to clone them by having the same identity of the captured node, and the adversary deploys an unpredictable number of replicas throughout the network. Hence replica node detection is an important challenge in Mobile Wireless Sensor Networks. Various replica node detection techniques have been proposed to detect these replica nodes. These methods incur control overheads and the detection accuracy is low when the replica is selected as a witness node. This paper proposes to solve these issues by enhancing the Single Hop Detection (SHD) method using the Clonal Selection algorithm to detect the clones by selecting the appropriate witness nodes. The advantages of the proposed method include (i) increase in the detection ratio, (ii) decrease in the control overhead, and (iii) increase in throughput. The performance of the proposed work is measured using detection ratio, false detection ratio, packet delivery ratio, average delay, control overheads, and throughput. The implementation is done using ns-2 to exhibit the actuality of the proposed work.
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Lu, Peng, Guang Wei Zhang, and Fang Chun Yang. "Node Capture Attack Detection in Dynamic WSNs Based on New Node Tracking." Advanced Materials Research 945-949 (June 2014): 2372–79. http://dx.doi.org/10.4028/www.scientific.net/amr.945-949.2372.
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Since the nodes of WSNs are always deployed on the outside, nodes are easy to be captured. The traditional detection approaches of capture attack can be categorized as approaches based on time of absence and approaches based on target tracking. The former only suitable in static WSNs and the latter usually requires a large communication cost. In this paper, a novel node capture attack detection approach is proposed in dynamic WSNs. Through this approach, every node record its neighbors and detect new nodes in real-time, if new nodes join in the network, a new node tracking algorithm is performed in WSNs and find out which of them are captured by adversaries. Simulation results show that, this method can greatly improve the detection accuracy in dynamic WSNs, and the communication cost is low.
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Kumari, Shabnam, and Sumit Dalal. "Malicious Node Detection in WSN Using WTE." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2386–90. http://dx.doi.org/10.31142/ijtsrd14611.
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Lin, Y. S., C. C. Tzeng, K. F. Huang, C. Y. Kang, C. C. Chia, and J. F. Hsieh. "Sentinel node detection with radiocolloid lymphatic mapping in early invasive cervical cancer." International Journal of Gynecologic Cancer 15, no. 2 (2005): 273–77. http://dx.doi.org/10.1136/ijgc-00009577-200503000-00014.
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We assessed the feasibility of sentinel lymph node detection using technicium-99 radiocolloid lymphatic mapping for predicting lymph node metastases in early invasive cervical cancer. Thirty patients with cervical cancer (stages IA2–IIA) underwent preoperative lymphoscintigraphy using technicium-99 intracervical injection and intraoperative lymphatic mapping with a handheld gamma probe. After dissection of the sentinel nodes, the standard procedure of pelvic lymph node dissection and radical hysterectomy was performed as usual. The sentinel node detection rate was 100% (30/30). There were seven (23.3%) cases of microscopic lymph node metastases on pathologic analysis. All of them had sentinel node involvement. Therefore, the sensitivity of sentinel node identification for prediction of lymph node metastases was 100%, and no false negative was found. Preoperative lymphoscintigraphy, coupled with intraoperative lymphatic mapping, located the sentinel nodes accurately in our study patients. This sentinel node detection method appears to be feasible for predicting lymph node metastases
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Nikitenko, R. P. "Efficiency of detection of lymph nodes in breast cancer." Reproductive health of woman, no. 6 (October 27, 2022): 13–17. http://dx.doi.org/10.30841/2708-8731.6.2022.267679.
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The objective: improve the quality of sentinel lymph nodes detection in patients with malignant neoplasms of the mammary glands.Materials and methods. At the period from 2009 to 2016, 400 patients with T1-T3N0M0 breast cancer were operated in Odessa Regional Clinical Hospital, using two dyes Patent Blue and ICG.The patients who had mastectomy with sentinel lymph node biopsy were diagnosed T2-T3N0M0 breast cancer more frequently. The exceptions were T3-T4 tumors, tumor diameter > 5 cm, invasion into the skin and chest wall, palpable axillary lymph nodes, 3 or more affected lymph nodes during sentinel lymph node biopsy.100 patients in the first group had sentinel lymph node biopsy. Lymph node staining was performed using Patent Blue dye.In the patients in the second group, sentinel lymph node biopsy was performed using Patent Blue dye and another fluorescent ICG dye, which was injected intravenously into the arm on the affected side of the mammary gland, along the outflow from the arm to the mammary gland.Results. The total five-year survival after axillary lymph node dissection and sentinel lymph node biopsy was 91 % and 92 %, respectively. The five-year recurrence-free survival after axillary lymph node dissection was approximately 82.2 %, and after the sentinel lymph node biopsy – 83.9 %. Regional recurrence in the sentinel lymph nodes on the affected side was determined only in 1.1 %. The time of observation of the patients was from 60 to 180 months. The recurrence was registered in 0.2 % patients as isolated metastases into the axillary lymph nodes. Not a single case of lymphostasis of the upper limbs from the side of the biopsy was registered. Conclusions. The simplicity of fluorescent dyes usage makes it possible to implement this method in the everyday work of oncologists-surgeons, the advantages of which are the absence of radiation exposure and quick intraoperative detection of lymph nodes.
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Sideri, Mario, Concetta De Cicco, Angelo Maggioni, Nicoletta Colombo, Luca Bocciolone, Giuseppe Trifirò, Maria De Nuzzo, Costantino Mangioni, and Giovanni Paganelli. "Detection of Sentinel Nodes by Lymphoscintigraphy and Gamma Probe Guided Surgery in Vulvar Neoplasia." Tumori Journal 86, no. 4 (July 2000): 359–63. http://dx.doi.org/10.1177/030089160008600431.
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Background Pathologic lymph node status is the most important prognostic factor in vulvar cancer; however, complete inguinofemoral node dissection is associated with significant morbidity. Intraoperative lymphoscintigraphy associated with gamma detecting probe-guided surgery has proved to be reliable in the detection of sentinel node (SN) involvement in melanoma and breast cancer patients. The present study evaluates the feasibility of the surgical identification of inguinal sentinel nodes using lymphoscintigraphy and a gamma detecting probe in patients with early vulvar cancer. Methods Technetium-99-labeled colloid human albumin was administered perilesionally in 44 patients. Twenty patients had T1 and 23 had T2 invasive epidermoid vulvar cancer; one patient had a lower-third vaginal cancer. An intraoperative gamma detecting probe was used to identify SNs during surgery. Complete inguinofemoral node dissection was subsequently performed. SNs underwent separate pathologic evaluation. Results A total of 77 groins were dissected in 44 patients. SNs were identified in all the studied groins. Thirteen cases had positive nodes: the SN was positive in all of them; in 10 cases the SN was the only positive node. Thirty-one patients showed negative SNs: all of them were negative for lymph node metastasis. Conclusions Lymphoscintigraphy and SN biopsy under gamma detecting probe guidance proved to be an easy and reliable method for detection of SNs in early vulvar cancer. If these preliminary data will be confirmed, the technique would represent a real progress towards less aggressive treatment in patients with vulvar cancer.
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Hua, Jiwei, Bo Zhang, Jinao Wang, Xin Shao, and Jinqi Zhu. "Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles." Applied Sciences 13, no. 2 (January 4, 2023): 695. http://dx.doi.org/10.3390/app13020695.
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Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes.
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Shao, Qing, Jia Lin Ma, and Yun Wei. "Study of Fault Detection Based on Benzene Ring in Wireless Sensor Networks." Advanced Engineering Forum 6-7 (September 2012): 1167–72. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.1167.
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Large scale and limited resources of wireless sensor networks(WSNs) increase the need to cope with node failures. This paper proposes a novel mechanism for the detection of node failures in WSNs. We designed a structure of benzene ring with symmetry. A benzene ring consists of a center node and several child nodes. Reliable communication links among these nodes. Each node maintains a list of its neighbors. The center node delegates detection service and make decisions about faulty nodes based on its child nodes’ available resources. Compared with other solutions, our mechanism can reduce energy consumption, decrease the number of neighborhoods and extend network lifetime. Simulation experiments show the performance and viability of the mechanism.
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Merisio, C., R. Berretta, M. Gualdi, D. C. Pultrone, S. Anfuso, G. Agnese, C. Aprile, et al. "Radioguided sentinel lymph node detection in vulvar cancer." International Journal of Gynecologic Cancer 15, no. 3 (2005): 493–97. http://dx.doi.org/10.1136/ijgc-00009577-200505000-00013.
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Lymph node status is the most important prognostic factor in vulvar cancer. Histologically, sentinel nodes may be representative of the status of the other regional nodes. Identification and histopathologic evaluation of sentinel nodes could then have a significant impact on clinical management and surgery. The aim of this study was to evaluate the feasibility and diagnostic accuracy of sentinel lymph node detection by preoperative lymphoscintigraphy with technetium-99 m–labeled nanocolloid, followed by radioguided intraoperative detection. Nine patients with stage T1, N0, M0, and 11 patients with stage T2, N0, M0 squamous cell carcinoma of the vulva were included in the study. Only three cases had lesions exceeding 3.5 cm in diameter. Sentinel nodes were detected in 100% of cases. A total of 30 inguinofemoral lymphadenectomies were performed, with a mean of 10 surgically removed nodes. Histological examination revealed 17 true negative sentinel nodes, 2 true positive, and 1 false negative. In our case series, sentinel lymph node detection had a 95% diagnostic accuracy, with only one false negative. Based on literature evidence, the sentinel node procedure is feasible and reliable in vulvar cancer; however, the value of sentinel node dissection in the treatment of early-stage vulvar cancer still needs to be confirmed.
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Lalar, Sachin, Shashi Bhushan, Surender Jangra, Mehedi Masud, and Jehad F. Al-Amri. "An Efficient Three-Phase Fuzzy Logic Clone Node Detection Model." Security and Communication Networks 2021 (April 26, 2021): 1–17. http://dx.doi.org/10.1155/2021/9924478.
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Wireless sensor networks have been deployed in the open and unattended environment where the attacker can capture the sensors and create the replica of captured nodes. As the clone nodes have been considered legitimate nodes, clone nodes can initiate different network attacks. We have designed a three-phase clone node detection method named fuzzy logic clone node detection (FLCND). The first phase of FLCND checks whether any node is missing from the network or not. In the next phase, FLCND finds out whether any missing node has arisen in the network in a stipulated time. If any missing node is alive, there is a possibility the node may be cloned. The information of suspected nodes is entered into the Hot-List, which has been maintained in the network. Phase III uses the suspected list and finds out the possibility of clone node using fuzzy logic. Two different scenarios have been simulated in NS2 to evaluate FLCND. The simulation result shows that the proposed method increases the packet delivery ratio (PDR) and reduces packet loss, end-to-end delay, and energy consumption. The simulation results illustrate that the FLCND method reduces the average power consumption by 27% and increases the detection rate by 46% compared to the existing techniques.
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Mishra, Alekha Kumar, Asis Kumar Tripathy, Arun Kumar, and Ashok Kumar Turuk. "A Replica Detection Scheme Based on the Deviation in Distance Traveled Sliding Window for Wireless Sensor Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/8457616.
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Node replication attack possesses a high level of threat in wireless sensor networks (WSNs) and it is severe when the sensors are mobile. A limited number of replica detection schemes in mobile WSNs (MWSNs) have been reported till date, where most of them are centralized in nature. The centralized detection schemes use time-location claims and the base station (BS) is solely responsible for detecting replica. Therefore, these schemes are prone to single point of failure. There is also additional communication overhead associated with sending time-location claims to the BS. A distributed detection mechanism is always a preferred solution to the above kind of problems due to significantly lower communication overhead than their counterparts. In this paper, we propose a distributed replica detection scheme for MWSNs. In this scheme, the deviation in the distance traveled by a node and its replica is recorded by the observer nodes. Every node is an observer node for some nodes in the network. Observers are responsible for maintaining a sliding window of recent time-distance broadcast of the nodes. A replica is detected by an observer based on the degree of violation computed from the deviations recorded using the time-distance sliding window. The analysis and simulation results show that the proposed scheme is able to achieve higher detection probability compared to distributed replica detection schemes such as Efficient Distributed Detection (EDD) and Multi-Time-Location Storage and Diffusion (MTLSD).
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Zhou, Yong, Guibin Sun, Yan Xing, Ranran Zhou, and Zhixiao Wang. "Local Community Detection Algorithm Based on Minimal Cluster." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3217612.
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In order to discover the structure of local community more effectively, this paper puts forward a new local community detection algorithm based on minimal cluster. Most of the local community detection algorithms begin from one node. The agglomeration ability of a single node must be less than multiple nodes, so the beginning of the community extension of the algorithm in this paper is no longer from the initial node only but from a node cluster containing this initial node and nodes in the cluster are relatively densely connected with each other. The algorithm mainly includes two phases. First it detects the minimal cluster and then finds the local community extended from the minimal cluster. Experimental results show that the quality of the local community detected by our algorithm is much better than other algorithms no matter in real networks or in simulated networks.
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Abdelhamid, Ashraf, Mahmoud Said Elsayed, Anca D. Jurcut, and Marianne A. Azer. "A Lightweight Anomaly Detection System for Black Hole Attack." Electronics 12, no. 6 (March 8, 2023): 1294. http://dx.doi.org/10.3390/electronics12061294.
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Mobile ad hoc networks (MANETs) are now key in today’s new world. They are critically needed in many situations when it is crucial to form a network on the fly while not having the luxury of time or resources to configure devices, build infrastructure, or even have human interventions. Ad hoc networks have many applications. For instance, they can be used in battlefields, education, rescue missions, and many other applications. Such networks are characterized by high mobility, low resources of power, storage, and processing. They are infrastructure-less; this means that they don’t use infrastructure equipment for communication. These networks rely instead on each other for routing and communication. MANETs use a hopping mechanism where each node in a network finds another node within its communication range and use it as a hop for delivering the message through another node and so on. In standard networks, there is dedicated equipment for specific functions such as routers, servers, firewalls, etc., while in ad hoc networks, every node performs multiple functions. For example, the routing function is performed by nodes. Hence, they are more vulnerable to attacks than standard networks. The main goal of this paper is to propose a solution for detecting black hole attacks using anomaly detection based on a support vector machine (SVM). This detection system aims at analyzing the traffic of the network and identifying anomalies by checking node behaviors. In the case of black hole attacks, the attacking nodes have some behavioral characteristics that are different from normal nodes. These characteristics can be effectively detected using our lightweight detection system. To experiment with the effectiveness of this solution, an OMNET++ simulator is used to generate traffic under a black hole attack. The traffic is then classified into malicious and non-malicious based on which the malicious node is identified. The results of the proposed solution showed very high accuracy in detecting black hole attacks.
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Muruganandam, D., and J. Martin Leo Manickam. "Detection and Countermeasure of Packet Misrouting in Wireless Adhoc Networks." Sensor Letters 17, no. 9 (September 1, 2019): 696–700. http://dx.doi.org/10.1166/sl.2019.4127.
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A MANET is an infrastructure-less type network, which consists of number of mobile nodes connected through wireless network interfaces. The Communication among nodes is made successfully when the nodes dynamically set up route among one another. The open nature and infrastructureless type of such networks causes the attacker's interest to penetrate through the network and decrease the network performance. Thus Security becomes a major concern for protected communication between mobile nodes. Packet misrouting stops the packet from reaching the destination by a malicious intermediate node. But the malicious node makes the intuition to its neighbors that it has done the genuine packet forwarding action. Moreover the malicious node makes the neighbours to suspect the normal node as malicious one. The proposed work ensures the detection of malicious nodes and avoids suspecting the trustworthy.
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Guo, Lin, and Miao Zhang. "Overlapping Community Detection Based on Strong Tie Detection and Non-Overlapping Link Clustering." Mathematical Problems in Engineering 2022 (December 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/5931727.
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Many clustering algorithms are in favour of node-based methods, but a link between nodes has one single feature, so link-based clustering is sometimes easier than node-based methods. Being dependent on the characteristics of links, a detection algorithm for a non-overlapping link community is put forward in this paper. The method proposed also distinguishes the differences between nodes with a high degree of accuracy and detects communities with a minimal number of overlapping nodes. On the basis of three different datasets, experiments were conducted to compare the proposed algorithm with different non-overlapping and overlapping clustering algorithms, and the results show that our algorithm generates the least number of overlapping nodes and achieves a good community partition.
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Euscher, Elizabeth. "Pathology of sentinel lymph nodes: historical perspective and current applications in gynecologic cancer." International Journal of Gynecologic Cancer 30, no. 3 (February 19, 2020): 394–401. http://dx.doi.org/10.1136/ijgc-2019-001022.
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Efforts to reduce surgical morbidity related to en bloc lymph node removal associated with cancer surgery led to the development of targeted lymph node sampling to identify the lymph node(s) most likely to harbor a metastasis. Through identification of one or only a few lymph nodes at highest risk, the overall number of lymph nodes removed could be markedly reduced. Submission of fewer lymph nodes affords more detailed pathologic examination than would otherwise be practical with a standard lymph node dissection. Such enhanced pathologic examination techniques (ie, ultra-staging) have contributed to increased detection of lymph node metastases, primarily by detection of low volume metastatic disease. Based on the success of sentinel lymph node mapping and ultra-staging in breast cancer and melanoma, such techniques are increasingly used for other organ systems including the gynecologic tract. This review addresses the historical aspects of sentinel lymph node evaluation and reviews current ultra-staging protocols as well as the implications associated with increased detection of low volume metastases.
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Wang, Biao, Hai Bin Zheng, Ying Jue Fang, and Jun Jie Wei. "Overlapping Community Detection Algorithm Based on Seed Diffusion." Applied Mechanics and Materials 556-562 (May 2014): 3300–3304. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3300.
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Thinking applied to the seed dispersal weighted network using node strength to find seed node, and through seed nodes for each node fitness Looking node's home societies, and update the node in the iterative process of fitness makes societies divided stabilized. The experimental results show that the network based on the weighted overlap Societies seed dispersal algorithm can be found in weighted social networks effectively divided and divided more tends to be refined.
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Daniel, G. Victor, Kandasamy Chandrasekaran, Venkatesan Meenakshi, and Prabhavathy Paneer. "Robust Graph Neural-Network-Based Encoder for Node and Edge Deep Anomaly Detection on Attributed Networks." Electronics 12, no. 6 (March 22, 2023): 1501. http://dx.doi.org/10.3390/electronics12061501.
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The task of identifying anomalous users on attributed social networks requires the detection of users whose profile attributes and network structure significantly differ from those of the majority of the reference profiles. GNN-based models are well-suited for addressing the challenge of integrating network structure and node attributes into the learning process because they can efficiently incorporate demographic data, activity patterns, and other relevant information. Aggregate operations, such as sum or mean pooling, are utilized by Graph Neural Networks (GNNs) to combine the representations of neighboring nodes within a graph. However, these aggregate operations can cause problems in detecting anomalous nodes. There are two main issues to consider when utilizing aggregate operations in GNNs. Firstly, the presence of anomalous neighboring nodes may affect the representation of normal nodes, leading to false positives. Secondly, anomalous nodes may be overlooked as their representation is flattened during the aggregate operation, leading to false negatives. The proposed approach, AnomEn, is a robust graph neural network developed for anomaly detection. It addresses the challenges of false positives and false negatives using a weighted aggregate mechanism. This mechanism is designed to differentiate between a node’s own features and the features of its neighbors by placing greater emphasis on a node’s own features and less emphasis on its neighbors’ features. The system can preserve the node’s original characteristics, whether the node is normal or anomalous. This work proposes not only a robust graph neural network, namely, AnomEn, but also specific anomaly detection structures for nodes and edges. The proposed AnomEn method serves as the encoder in the node and edge anomaly detection architectures and was tested on multiple datasets. Experiments were conducted to validate the effectiveness of the proposed method as a graph neural network encoder. The findings demonstrated the robustness of the proposed method in detecting anomalies. The proposed method outperforms other existing methods in node anomaly detection tasks by 5.63% and edge anomaly detection tasks by 7.87%.
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Sujihelen, L., Rajasekhar Boddu, S. Murugaveni, Ms Arnika, Anandakumar Haldorai, Pundru Chandra Shaker Reddy, Suili Feng, and Jiayin Qin. "Node Replication Attack Detection in Distributed Wireless Sensor Networks." Wireless Communications and Mobile Computing 2022 (May 31, 2022): 1–11. http://dx.doi.org/10.1155/2022/7252791.
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Wireless sensor network (WSN) is an emerging technology used in emergency scenarios. There are a number of possible threats to WSNs because they use unsupervised IP addresses. Securing networks with unattended sensors is a real challenge nowadays. Sensor nodes lack power and storage, making them incompatible with normal security checks. It will be vital to make advancements in sensor network architecture and protocol design. There will be more vulnerability to attack if there is a lack of security. Especially, one key attack is node replication which induces the sensor node to acts as an original node, collecting data from the network and sending it to the attacker. In dynamic WSN, detecting an assault is difficult to find replica nodes. Therefore, this paper proposes a Strategic Security System (SSS) to discover replica nodes in static and dynamic distributed WSNs. It is mainly focused on enhancing detection accuracy, time delay, and communication overhead. The present system includes Single Stage Memory Random Walk with Network Division (SSRWND) and a Random-walk-based approach to detect clone attacks (RAWL). The proposed system has less memory and better detection accuracy.
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Wang, Zhixiao, Ya Zhao, Zhaotong Chen, and Qiang Niu. "An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/121609.
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Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.
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Pelosi, E., V. Arena, B. Baudino, M. Bellò, R. Giani, D. Lauro, A. Ala, R. Bussone, and G. Bisi. "Sentinel Node Detection in Breast Carcinoma." Tumori Journal 88, no. 3 (May 2002): S10—S11. http://dx.doi.org/10.1177/030089160208800323.
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Aims and Background The standard procedure for the evaluation of axillary nodal involvement in patients with breast cancer is still complete lymph node dissection. However, about 70% of patients are found to be free of metastatic disease while axillary node dissection may cause significant morbidity. Lymphatic mapping and sentinel lymph node (SLN) biopsy are changing this situation. Methods and Study Design In a period of 18 months we studied 201 patients with breast cancer, excluding patients with palpable axillary nodes, tumors >2.5 cm in diameter, multifocal or multicentric cancer, pregnant patients and patients over 80 years of age. Before surgery 99mTc-labeled colloid and vital blue dye were injected into the breast to identify the SLN. In lymph nodes dissected during surgery the metastatic status was examined by sections at reduced intervals. Only patients with SLNs that were histologically positive for metastases underwent axillary dissection. Results We localized one or more SLNs in 194 of 201 (96.5%) patients; when both techniques were utilized the success rate was 100%. Histologically, 21% of patients showed SLN metastases (7.8% micrometastases) and 68% of these had metastases also in other axillary nodes. None of the patients with negative SLNs developed metastases during follow-up. Conclusions At present there is no definite evidence that negative SLN biopsy is invariably correlated with negative axillary status; however, our study and those of others demonstrate that SLN biopsy is an accurate method of axillary staging.
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Lewandowski, Marcin, and Bartłomiej Płaczek. "Data Transmission Reduction in Wireless Sensor Network for Spatial Event Detection." Sensors 21, no. 21 (October 31, 2021): 7256. http://dx.doi.org/10.3390/s21217256.
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Wireless sensor networks have found many applications in detecting events such as security threats, natural hazards, or technical malfunctions. An essential requirement for event detection systems is the long lifetime of battery-powered sensor nodes. This paper introduces a new method for prolonging the wireless sensor network’s lifetime by reducing data transmissions between neighboring sensor nodes that cooperate in event detection. The proposed method allows sensor nodes to decide whether they need to exchange sensor readings for correctly detecting events. The sensor node takes into account the detection algorithm and verifies whether its current sensor readings can impact the event detection performed by another node. The data are transmitted only when they are found to be necessary for event detection. The proposed method was implemented in a wireless sensor network to detect the instability of cargo boxes during transportation. Experimental evaluation confirmed that the proposed method significantly extends the network lifetime and ensures the accurate detection of events. It was also shown that the introduced method is more effective in reducing data transmissions than the state-of-the-art event-triggered transmission and dual prediction algorithms.
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Zhang, Bo, Qianqian Song, Tao Yang, Zhonghua Zheng, and Huan Zhang. "A Fuzzy Collusive Attack Detection Mechanism for Reputation Aggregation in Mobile Social Networks: A Trust Relationship Based Perspective." Mobile Information Systems 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/5185170.
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While the mechanism of reputation aggregation proves to be an effective scheme for indicating an individual’s trustworthiness and further identifying malicious ones in mobile social networks, it is vulnerable to collusive attacks from malicious nodes of collaborative frauds. To conquer the challenge of detecting collusive attacks and then identifying colluders for the reputation system in mobile social networks, a fuzzy collusive attack detection mechanism (FCADM) is proposed based on nodes’ social relationships, which comprises three parts: trust schedule, malicious node selection, and detection traversing strategy. In the first part, the trust schedule provides the calculation method of interval valued fuzzy social relationships and reputation aggregation for nodes in mobile social networks; further, a set of fuzzy valued factors, that is, item judgment factor, node malicious factor, and node similar factor, is given for evaluating the probability of collusive fraud happening and identifying single malicious nodes in the second part; and moreover, a detection traversing strategy is given based on random walk algorithm under the perspectives of fuzzy valued nodes’ trust schedules and proposed malicious factors. Finally, our empirical results and analysis show that the proposed mechanism in this paper is feasible and effective.
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Venu, Nookala, M. Revanesh, M. Supriya, Manjunath B. Talawar, A. Asha, Lydia D. Isaac, and Alachew Wubie Ferede. "Energy Auditing and Broken Path Identification for Routing in Large-Scale Mobile Networks Using Machine Learning." Wireless Communications and Mobile Computing 2022 (August 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/9418172.
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In mobile computing, all nodes are movable nodes, which causes many problems for transmitting data packets in a sequence manner; since the mobile nodes are connected with each other, during movement, nodes make the connection fail or damaged. This kind of link damage is caused by nodes that travel out of range from the network limit and also affect the packet success rate. This reduces the network lifetime and detection efficiency and increases the communication overhead. Every mobile node in mobile computing is an unstable node, causing numerous problems for broadcasting data packets in a series method. When the mobile nodes are connected to each other, relay nodes cause the link to break or else sustain damage. This type of connection failure is brought on by nodes that leave the network’s permitted range, which also lowers the packet success rate. The link failure cannot be recovered by the multipath routing algorithm. As a result, the communication overhead is increased while the network lifetime and detection effectiveness are reduced. Then, the novel energy routing (NER) method that has been proposed is employed to support the energetic routing path across the middle nodes. It is challenging to locate the failed channels and carry on with the successful packet transfer. The master node selection algorithm is intended to identify the best relaying node, fault-free packet transmission process among the network structure’s relaying nodes. The master node is efficiently chosen in this manner. The master node, also known as the energy-based important node, is employed in the mobile network to carry out error-free packet transmission procedures. The other nodes are lower energy nodes that do not participate in packet forwarding, and this algorithm only detects the higher energy successful nodes. This lengthens the network’s lifespan, improves detection effectiveness, and lowers communication overhead. The energy-based heavy node is also known as the master node, which is used to perform a problem-free communication process in the mobile network. This algorithm only accepts the higher energy successful node; the remaining nodes are lower energy nodes which do not perform the communication process. This increases the network lifetime and detection efficiency and reduces the communication overhead. The performance metrics for the proposed system is evaluated by end to end delay, communication overhead, throughput, detection efficiency, network lifetime, and packet drop rate.
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Yu, Wenjin, Yong Li, and Yuangeng Xu. "Research on Pseudo-Node Detection Algorithm in Wireless Sensor Networks." International Journal of Online Engineering (iJOE) 13, no. 03 (March 28, 2017): 113. http://dx.doi.org/10.3991/ijoe.v13i03.6864.
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<span style="font-family: 'Times New Roman',serif; font-size: 12pt; mso-fareast-font-family: SimSun; mso-fareast-theme-font: minor-fareast; mso-ansi-language: EN-US; mso-fareast-language: ZH-CN; mso-bidi-language: AR-SA;">With the wide application of the wireless sensor network, the security of the sensor network is becoming increasingly important. In this paper, based on node ranging, a new intrusion node detection algorithm has been proposed for external pseudo-node detection in wireless sensor networks. The presence of the nodes under copying-attack and the pseudo-nodes in the network can be detected through inter-node ranging with appropriate use of various sensors of nodes themselves and comprehensive analysis of ranging results. Operating in a stand-alone or embedded manner, this method has remedied the defects in the traditional principle of attack detection. The simulation results show that the proposed method has excellent applicability in wireless sensor security detection.</span>
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Wang, Jiang-Tao, and Zhi-Xiong Liu. "An active detection of compromised nodes based on en-route trap in wireless sensor network." International Journal of Distributed Sensor Networks 17, no. 8 (August 2021): 155014772110403. http://dx.doi.org/10.1177/15501477211040367.
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With the development and wide use of wireless sensor network, security arises as an essential issue since sensors with restrict resources are deployed in wild areas in an unattended manner. Most of current en-route filtering schemes could filter false data effectively; however, the compromised nodes could take use of the filtering scheme to launch Fictitious False data Dropping attack, the detection of this attack is extremely difficult since the previous hop node is unable to distinguish whether the forwarding node dropt a false data report with incorrect Message Authentication Codes or a legitimate report. This is the first attempt to address the Fictitious False data Dropping attack; in this article, we propose an Active Detection of compromised nodes based on En-route Trap to trap compromised nodes in the scenario of a false data dropping. A trust model is used to evaluate trust level of neighbor nodes with respect to their authentication behaviors. A detecting algorithm of compromised node is used to detect compromised nodes. Simulation results showed that our scheme can address the Fictitious False data Dropping attack and detect 60% of compromised nodes with a low false positive rate; consequently, the packet accuracy of an Active Detection of compromised nodes based on En-route Trap increases rapidly and reaches to 86%.
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Huang, Bingyang, Chaokun Wang, and Binbin Wang. "NMLPA: Uncovering Overlapping Communities in Attributed Networks via a Multi-Label Propagation Approach." Sensors 19, no. 2 (January 10, 2019): 260. http://dx.doi.org/10.3390/s19020260.
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With the enrichment of the entity information in the real world, many networks with attributed nodes are proposed and studied widely. Community detection in these attributed networks is an essential task that aims to find groups where the intra-nodes are much more densely connected than the inter-nodes. However, many existing community detection methods in attributed networks do not distinguish overlapping communities from non-overlapping communities when designing algorithms. In this paper, we propose a novel and accurate algorithm called Node-similarity-based Multi-Label Propagation Algorithm (NMLPA) for detecting overlapping communities in attributed networks. NMLPA first calculates the similarity between nodes and then propagates multiple labels based on the network structure and the node similarity. Moreover, NMLPA uses a pruning strategy to keep the number of labels per node within a suitable range. Extensive experiments conducted on both synthetic and real-world networks show that our new method significantly outperforms state-of-the-art methods.
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Luo, Hairu, Peng Jia, Anmin Zhou, Yuying Liu, and Ziheng He. "Bridge Node Detection between Communities Based on GNN." Applied Sciences 12, no. 20 (October 13, 2022): 10337. http://dx.doi.org/10.3390/app122010337.
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In a complex network, some nodes are relatively concentrated in topological structure, thus forming a relatively independent node group, which we call a community. Usually, there are multiple communities on a network, and these communities are interconnected and exchange information with each other. A node that plays an important role in the process of information exchange between communities is called an inter-community bridge node. Traditional methods of defining and detecting bridge nodes mostly quantify the bridging effect of nodes by collecting local structural information of nodes and defining index operations. However, on the one hand, it is often difficult to capture the deep topological information in complex networks based on a single indicator, resulting in inaccurate evaluation results; on the other hand, for networks without community structure, such methods may rely on community partitioning algorithms, which require significant computing power. In this paper, considering the multi-dimensional attributes and structural characteristics of nodes, a deep learning-based framework named BND is designed to quickly and accurately detect bridge nodes. Considering that the bridging function of nodes between communities is abstract and complex, and may be related to the multi-dimensional information of nodes, we construct an attribute graph on the basis of the original graph according to the features of the five dimensions of the node to meet our needs for extracting bridging-related attributes. In the deep learning model, we overlay graph neural network layers to process the input attribute graph and add fully connected layers to improve the final classification effect of the model. Graph neural network algorithms including GCN, GAT, and GraphSAGE are compatible with our proposed framework. To the best of our knowledge, our work is the first application of graph neural network techniques in the field of bridge node detection. Experiments show that our designed framework can effectively capture network topology information and accurately detect bridge nodes in the network. In the overall model effect evaluation results based on indicators such as Accuracy and F1 score, our proposed graph neural network model is generally better than baseline methods. In the best case, our model has an Accuracy of 0.9050 and an F1 score of 0.8728.
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Vermeeren, L., W. M. C. Klop, M. W. M. van den Brekel, A. J. M. Balm, O. E. Nieweg, and R. A. Valdés Olmos. "Sentinel Node Detection in Head and Neck Malignancies: Innovations in Radioguided Surgery." Journal of Oncology 2009 (2009): 1–10. http://dx.doi.org/10.1155/2009/681746.
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Sentinel node mapping is becoming a routine procedure for staging of various malignancies, because it can determine lymph node status more precisely. Due to anatomical problems, localizing sentinel nodes in the head and neck region on the basis of conventional images can be difficult. New diagnostic tools can provide better visualization of sentinel nodes. In an attempt to keep up with possible scientific progress, this article reviews new and innovative tools for sentinel node localization in this specific area. The overview comprises a short introduction of the sentinel node procedure as well as indications in the head and neck region. Then the results of SPECT/CT for sentinel node detection are described. Finally, a portable gamma camera to enable intraoperative real-time imaging with improved sentinel node detection is described.
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VOKOROKOS, Liberios, Michal ENNERT, Zuzana DUDLÁKOVÁ, and Olympia FORTOTIRA. "A CONTROL NODE FOR INTRUSION DETECTION SYSTEMS MANAGEMENT." Acta Electrotechnica et Informatica 14, no. 3 (September 1, 2014): 28–31. http://dx.doi.org/10.15546/aeei-2014-0025.
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Lu, Biao, and Wansu Liu. "Nonuniform Clustering of Wireless Sensor Network Node Positioning Anomaly Detection and Calibration." Journal of Sensors 2021 (October 16, 2021): 1–10. http://dx.doi.org/10.1155/2021/5733308.
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In order to detect and correct node localization anomalies in wireless sensor networks, a hierarchical nonuniform clustering algorithm is proposed. This paper designs a centroid iterative maximum likelihood estimation location algorithm based on nonuniformity analysis, selects the nonuniformity analysis algorithm, gives the flowchart of node location algorithm, and simulates the distribution of nodes with MATLAB. Firstly, the algorithm divides the nodes in the network into different network levels according to the number of hops required to reach the sink node. According to the average residual energy of nodes in each layer, the sink node selects the nodes with higher residual energy in each layer of the network as candidate cluster heads and selects a certain number of nodes with lower residual energy as additional candidate cluster heads. Then, at each level, the candidate cluster heads are elected to produce the final cluster heads. Finally, by controlling the communication range between cluster head and cluster members, clusters of different sizes are formed, and clusters at the level closer to the sink node have a smaller scale. By simulating the improved centroid iterative algorithm, the values of the optimal iteration parameters α and η are obtained. Based on the analysis of the positioning errors of the improved centroid iterative algorithm and the maximum likelihood estimation algorithm, the value of the algorithm conversion factor is selected. Aiming at the problem of abnormal nodes that may occur in the process of ranging, a hybrid node location algorithm is further proposed. The algorithm uses the ℓ 2 , 1 norm to smooth the structured anomalies in the ranging information and realizes accurate positioning while detecting node anomalies. Experimental results show that the algorithm can accurately determine the uniformity of distribution, achieve good positioning effect in complex environment, and detect abnormal nodes well. In this paper, the hybrid node location algorithm is extended to the node location problem in large-scale scenes, and a good location effect is achieved.
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Jiang, Weinuo, and Shihong Wang. "Detecting hidden nodes in networks based on random variable resetting method." Chaos: An Interdisciplinary Journal of Nonlinear Science 33, no. 4 (April 2023): 043109. http://dx.doi.org/10.1063/5.0134953.
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Reconstructing network connections from measurable data facilitates our understanding of the mechanism of interactions between nodes. However, the unmeasurable nodes in real networks, also known as hidden nodes, introduce new challenges for reconstruction. There have been some hidden node detection methods, but most of them are limited by system models, network structures, and other conditions. In this paper, we propose a general theoretical method for detecting hidden nodes based on the random variable resetting method. We construct a new time series containing hidden node information based on the reconstruction results of random variable resetting, theoretically analyze the autocovariance of the time series, and finally provide a quantitative criterion for detecting hidden nodes. We numerically simulate our method in discrete and continuous systems and analyze the influence of main factors. The simulation results validate our theoretical derivation and illustrate the robustness of the detection method under different conditions.
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Pagliarulo, Vincenzo, Debra Hawes, Frank H. Brands, Susan Groshen, Jie Cai, John P. Stein, Gary Lieskovsky, Donald G. Skinner, and Richard J. Cote. "Detection of Occult Lymph Node Metastases in Locally Advanced Node-Negative Prostate Cancer." Journal of Clinical Oncology 24, no. 18 (June 20, 2006): 2735–42. http://dx.doi.org/10.1200/jco.2005.05.4767.
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Purpose The purpose of this study was to determine the incidence and clinical significance of occult metastases in the lymph nodes of patients with prostate cancer originally considered node negative by routine histologic evaluation. Methods Two hundred seventy four patients with pT3 prostate carcinoma treated by radical prostatectomy and bilateral lymph node dissection were included in this study. One hundred eighty patients were staged node negative (N0), while 94 patients were lymph node positive (N+), based on routine histologic evaluation. All lymph nodes from the 180 N0 patients were evaluated for occult metastases by immunohistochemistry using antibodies to cytokeratins and, if positive, prostate-specific antigen. Recurrence and overall survival were compared among patients with occult tumor cells (OLN+), with patients whose lymph nodes remained negative (OLN−), and with the 94 N+ patients. Results A total of 3,914 lymph nodes were evaluated from 180 N0 patients (average, 21.7 lymph nodes per patient). Occult tumor cells were found in 24 of 180 patients (13.3%). The presence of OLN+ was significantly associated with increased recurrence and decreased survival compared with OLN− patients (P < .001 and P = .019, respectively; relative risk of recurrence, 2.27; relative risk of death 2.07, respectively). The presence of occult lymph node metastases was an independent predictor of recurrence and death in a multivariable analysis. The outcome for patients with OLN+ disease was similar to that for patients with N+ disease. Conclusion The detection of occult lymph node metastases in patients with pT3N0 prostate cancer identifies those with significantly increased risk of prostate cancer recurrence and death.
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Liu, Jiaxi, Weizhong Gao, Jian Dong, Na Wu, and Fei Ding. "Low-Power Failure Detection for Environmental Monitoring Based on IoT." Sensors 21, no. 19 (September 28, 2021): 6489. http://dx.doi.org/10.3390/s21196489.
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Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms.
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Wang, Guishen, Yuanwei Wang, Kaitai Wang, Zhihua Liu, Lijuan Zhang, Yu Zhou, and Qinan Yao. "An overlapping community detection algorithm based on node distance of line graph." Modern Physics Letters B 33, no. 26 (September 20, 2019): 1950322. http://dx.doi.org/10.1142/s0217984919503226.
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Overlapping community detection is a hot topic in research of complex networks. Link community detection is a popular approach to discover overlapping communities. Line graph is a widely used model in link community detection. In this paper, we propose an overlapping community detection algorithm based on node distance of line graph. Considering topological structure of links in graphs, we use line graph to transform links of graph into nodes of line graph. Then, we calculate node distance of line graph according to their dissimilarity. After getting distance matrix, we proposed a new [Formula: see text] measure based on nodes of line graph and combine it with clustering algorithm by fast search and density peak to identify node communities of line graph. Finally, we acquire overlapping node communities after transforming node communities of line graph back to graph. The experimental results show that our algorithm achieves a higher performance on normalized mutual information metric.
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Zhang, Yajing, Kai Wang, and Jinghui Zhang. "Adaptive and Lightweight Abnormal Node Detection via Biological Immune Game in Mobile Multimedia Networks." Algorithms 14, no. 12 (December 20, 2021): 368. http://dx.doi.org/10.3390/a14120368.
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Considering the contradiction between limited node resources and high detection costs in mobile multimedia networks, an adaptive and lightweight abnormal node detection algorithm based on artificial immunity and game theory is proposed in order to balance the trade-off between network security and detection overhead. The algorithm can adapt to the highly dynamic mobile multimedia networking environment with a large number of heterogeneous nodes and multi-source big data. Specifically, the heterogeneous problem of nodes is solved based on the non-specificity of an immune algorithm. A niche strategy is used to identify dangerous areas, and antibody division generates an antibody library that can be updated online, so as to realize the dynamic detection of the abnormal behavior of nodes. Moreover, the priority of node recovery for abnormal nodes is decided through a game between nodes without causing excessive resource consumption for security detection. The results of comparative experiments show that the proposed algorithm has a relatively high detection rate and a low false-positive rate, can effectively reduce consumption time, and has good level of adaptability under the condition of dynamic nodes.
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Wang, Qizhou, Guansong Pang, Mahsa Salehi, Wray Buntine, and Christopher Leckie. "Cross-Domain Graph Anomaly Detection via Anomaly-Aware Contrastive Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (June 26, 2023): 4676–84. http://dx.doi.org/10.1609/aaai.v37i4.25591.
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Cross-domain graph anomaly detection (CD-GAD) describes the problem of detecting anomalous nodes in an unlabelled target graph using auxiliary, related source graphs with labelled anomalous and normal nodes. Although it presents a promising approach to address the notoriously high false positive issue in anomaly detection, little work has been done in this line of research. There are numerous domain adaptation methods in the literature, but it is difficult to adapt them for GAD due to the unknown distributions of the anomalies and the complex node relations embedded in graph data. To this end, we introduce a novel domain adaptation approach, namely Anomaly-aware Contrastive alignmenT (ACT), for GAD. ACT is designed to jointly optimise: (i) unsupervised contrastive learning of normal representations of nodes in the target graph, and (ii) anomaly-aware one-class alignment that aligns these contrastive node representations and the representations of labelled normal nodes in the source graph, while enforcing significant deviation of the representations of the normal nodes from the labelled anomalous nodes in the source graph. In doing so, ACT effectively transfers anomaly-informed knowledge from the source graph to learn the complex node relations of the normal class for GAD on the target graph without any specification of the anomaly distributions. Extensive experiments on eight CD-GAD settings demonstrate that our approach ACT achieves substantially improved detection performance over 10 state-of-the-art GAD methods. Code is available at https://github.com/QZ-WANG/ACT.
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Mahapatro, Arunanshu, and Pabitra Mohan Khilar. "Detection of Node Failure in Wireless Image Sensor Networks." ISRN Sensor Networks 2012 (January 29, 2012): 1–8. http://dx.doi.org/10.5402/2012/342514.
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A sequenced process of fault detection followed by dissemination of decision made at each node characterizes the sustained operations of a fault-tolerant wireless image sensor network (WISN). This paper presents a distributed self-fault diagnosis model for WISN where fault diagnosis is achieved by disseminating decision made at each node. Architecture of fault-tolerant wireless image sensor nodes is presented. Simulation results show that sensor nodes with hard and soft faults are identified with high accuracy for a wide range of fault rate. Both time and message complexity of the proposed algorithm are for an -node WISN.
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Ramalakshmi, R., S. Subash Prabhu, and C. Balasubramanian. "Detection of Compromised Nodes in Wireless Sensor Networks using GPSR Protocol and Iterative Filtering Algorithm." APTIKOM Journal on Computer Science and Information Technologies 1, no. 3 (November 1, 2016): 141–48. http://dx.doi.org/10.11591/aptikom.j.csit.122.
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The sensor network is used to observe surrounding area gathered and spread the information to other sink. The advantage of this network is used to improve life time and energy. The first sensor node or group of sensor nodes in the network runs out of energy. The aggregator node can send aggregate value to the base station. The sensor node can be used to assign initial weights for each node. This sensor node calculates weight for each node. Which sensor node weight should be lowest amount they can act as a cluster head. The joint node can send false data to the aggregator node and then these node controls to adversary. The dependability at any given instant represents an comprehensive behavior of participate to be various types of defects and misconduct. The adversary can send information to aggregator node then complexity will be occurred. These nodes are used to reduce the energy and band width.
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Kaur, Kamaljit, and Amandeep Kaur. "Detection of Sybil Attack in VANETs." International Journal of Emerging Research in Management and Technology 6, no. 6 (June 29, 2018): 296. http://dx.doi.org/10.23956/ijermt.v6i6.285.
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The vehicular adhoc network is the decentralized type of network. The vehicle nodes can join or leave the network when they want. In the such type of network security, routing and quality of service are the three major issues of the network. In the network malicious node is present which is responsible to trigger various types of active and passive attacks. The Sybil attack is the active types of attack in which malicious node change its identification multiple times. In this, paper various techniques are reviewed which are used to isolate malicious nodes from the network.
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Thanigaivelan, Nanda Kumar, Ethiopia Nigussie, Seppo Virtanen, and Jouni Isoaho. "Hybrid Internal Anomaly Detection System for IoT: Reactive Nodes with Cross-Layer Operation." Security and Communication Networks 2018 (August 15, 2018): 1–15. http://dx.doi.org/10.1155/2018/3672698.
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We present a hybrid internal anomaly detection system that shares detection tasks between router and nodes. It allows nodes to react instinctively against the anomaly node by enforcing temporary communication ban on it. Each node monitors its own neighbors and if abnormal behavior is detected, the node blocks the packets of the anomaly node at link layer and reports the incident to its parent node. A novel RPL control message, Distress Propagation Object (DPO), is formulated and used for reporting the anomaly and network activities to the parent node and subsequently to the router. The system has configurable profile settings and is able to learn and differentiate between the nodes normal and suspicious activities without a need for prior knowledge. It has different subsystems and operation phases that are distributed in both the nodes and router, which act on data link and network layers. The system uses network fingerprinting to be aware of changes in network topology and approximate threat locations without any assistance from a positioning subsystem. The developed system was evaluated using test-bed consisting of Zolertia nodes and in-house developed PandaBoard based gateway as well as emulation environment of Cooja. The evaluation revealed that the system has low energy consumption overhead and fast response. The system occupies 3.3 KB of ROM and 0.86 KB of RAM for its operations. Security analysis confirms nodes reaction against abnormal nodes and successful detection of packet flooding, selective forwarding, and clone attacks. The system’s false positive rate evaluation demonstrates that the proposed system exhibited 5% to 10% lower false positive rate compared to simple detection system.