Relevant bibliographies by topics / Malicious node detection in wireless sensor networks

Academic literature on the topic 'Malicious node detection in wireless sensor networks'

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Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Malicious node detection in wireless sensor networks"

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Zhang, Zhao Hui, Ming Ming Hu, Dong Li, and Xiao Gang Qi. "Distributed Malicious Nodes Detection in Wireless Sensor Networks." Applied Mechanics and Materials 519-520 (February 2014): 1243–46. http://dx.doi.org/10.4028/www.scientific.net/amm.519-520.1243.

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Data theft and node attack in wireless sensor networks causes great damage to the networks and the attacker destroys network and obtains the data of the network by malicious nodes distributed in the network. Therefore, it is necessary to detect these malicious nodes and to eliminate their influence. We propose a distributed malicious nodes detection protocol which called BMND based on Bayesian voting, every node determine its suspected malicious nodes by its request message and abnormal behavior. Also, we determine the malicious nodes by Bayesian voting, so that the network can protect itself from such malicious nodes influence. The simulation results show that our algorithm has good performance in both the detection rate and false positive rate.
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Zheng, Guiping, Bei Gong, and Yu Zhang. "Dynamic Network Security Mechanism Based on Trust Management in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2021 (February 27, 2021): 1–10. http://dx.doi.org/10.1155/2021/6667100.

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Wireless sensor network is a key technology in Internet of Things. However, due to the large number of sensor nodes and limited security capability, aging nodes and malicious nodes increase. In order to detect the untrusted nodes in the network quickly and effectively and ensure the reliable operation of the network, this paper proposes a dynamic network security mechanism. Firstly, the direct trust value of the node is established based on its behavior in the regional information interaction. Then, the comprehensive trust value is calculated according to the trust recommendation value and energy evaluation value of other high-trust nodes. Finally, node reliability and management nodes are updated periodically. Malicious nodes are detected and isolated according to the credibility to ensure the dynamic, safe, and reliable operation of the network. Simulation results and analysis show that the node trust value calculated by this mechanism can reflect its credibility truly and accurately. In terms of reliable network operation, the mechanism can effectively detect malicious nodes, with higher detection rate, avoid the risk of malicious nodes as management nodes, reduce the energy consumption of nodes, and also play a defensive role in DOS attacks in wireless sensor networks.
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Zhang, Zhiming, Yu Yang, Wei Yang, Fuying Wu, Ping Li, and Xiaoyong Xiong. "Detection and Location of Malicious Nodes Based on Homomorphic Fingerprinting in Wireless Sensor Networks." Security and Communication Networks 2021 (September 24, 2021): 1–12. http://dx.doi.org/10.1155/2021/9082570.

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The current detection schemes of malicious nodes mainly focus on how to detect and locate malicious nodes in a single path; however, for the reliability of data transmission, many sensor data are transmitted by multipath in wireless sensor networks. In order to detect and locate malicious nodes in multiple paths, in this paper, we present a homomorphic fingerprinting-based detection and location of malicious nodes (HFDLMN) scheme in wireless sensor networks. In the HFDLMN scheme, using homomorphic fingerprint and coding technology, the original data is divided into n packets and sent to the base station along n paths, respectively; the base station determines whether there are malicious nodes in each path by verifying the validity of the packets; if there are malicious nodes in one or more paths, the location algorithm of the malicious node is implemented to locate the specific malicious nodes in the path; if all the packets are valid, the original data is recovered. The HFDLMN scheme does not need any complex evaluation model to evaluate and calculate the trust value of the node, nor any monitoring nodes. Theoretical analysis results show that the HFDLMN scheme is secure and effective. The simulation results demonstrate promising outcomes with respect to key parameters such as the detection probability of the malicious path and the locating probability of the malicious node.
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Sei, Yuichi, and Akihiko Ohsuga. "Malicious Node Detection in Mobile Wireless Sensor Networks." Journal of Information Processing 23, no. 4 (2015): 476–87. http://dx.doi.org/10.2197/ipsjjip.23.476.

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S. Eissa, Nour El Din, and Gamal I. Selim. "Cooperative Intrusion Detection Technique in Wireless Sensor Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 13, no. 3 (March 30, 2014): 4256–64. http://dx.doi.org/10.24297/ijct.v13i3.2756.

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Wireless Sensor Networks (WSN) are becoming more and more popular everyday due to their increasing ability to monitor certain phenomenon over wide regions such as air pollution, natural disaster, industrial monitoring and underwater applications [1]. The simple security capabilities of the nodes in the sensors network makes it an easy target for an intruder to take over some of the node(s) in the sensor network and to start altering the data received or sent by these nodes before forwarding it to the other nodes in effort to prevent the destination from properly decoding or reading the received data. These attacked nodes lead to tremendous amount of unusable data travelling across the network. The objective of this paper aims to detect these malicious nodes and cast them outside the network using a cooperative local voting approach with a dynamic center and study its effect on the amount of aggregated data though the network and the time required to deliver the sensors data.
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Singh, Mandeep, Navjyot Kaur, Amandeep Kaur, and Gaurav Pushkarna. "A Comparative Evaluation of Mining Techniques to Detect Malicious Node in Wireless Sensor Networks." International Journal of Cyber Warfare and Terrorism 7, no. 2 (April 2017): 42–53. http://dx.doi.org/10.4018/ijcwt.2017040103.

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Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.
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Jamshidi, Mojtaba, Milad Ranjbari, Mehdi Esnaashari, Nooruldeen Nasih Qader, and Mohammad Reza Meybodi. "Sybil Node Detection in Mobile Wireless Sensor Networks Using Observer Nodes." JOIV : International Journal on Informatics Visualization 2, no. 3 (May 15, 2018): 159. http://dx.doi.org/10.30630/joiv.2.3.131.

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Sybil attack is one of the well-known dangerous attacks against wireless sensor networks in which a malicious node attempts to propagate several fabricated identities. This attack significantly affects routing protocols and many network operations, including voting and data aggregation. The mobility of nodes in mobile wireless sensor networks makes it problematic to employ proposed Sybil node detection algorithms in static wireless sensor networks, including node positioning, RSSI-based, and neighbour cooperative algorithms. This paper proposes a dynamic, light-weight, and efficient algorithm to detect Sybil nodes in mobile wireless sensor networks. In the proposed algorithm, observer nodes exploit neighbouring information during different time periods to detect Sybil nodes. The proposed algorithm is implemented by J-SIM simulator and its performance is compared with other existing algorithm by conducting a set of experiments. Simulation results indicate that the proposed algorithm outperforms other existing methods regarding detection rate and false detection rate. Moreover, they also showed that the mean detection rate and false detection rate of the proposed algorithm are respectively 99% and less than 2%.
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Grgic, Kresimir, Drago Zagar, and Visnja Krizanovic Cik. "System for Malicious Node Detection in IPv6-Based Wireless Sensor Networks." Journal of Sensors 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/6206353.

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The trend of implementing the IPv6 into wireless sensor networks (WSNs) has recently occurred as a consequence of a tendency of their integration with other types of IP-based networks. The paper deals with the security aspects of these IPv6-based WSNs. A brief analysis of security threats and attacks which are present in the IPv6-based WSN is given. The solution to an adaptive distributed system for malicious node detection in the IPv6-based WSN is proposed. The proposed intrusion detection system is based on distributed algorithms and a collective decision-making process. It introduces an innovative concept of probability estimation for malicious behaviour of sensor nodes. The proposed system is implemented and tested through several different scenarios in three different network topologies. Finally, the performed analysis showed that the proposed system is energy efficient and has a good capability to detect malicious nodes.
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Pan, Ju Long, Ling Long Hu, Wen Jin Li, Hui Cui, and Zi Yin Li. "Weighted K Nearest Neighbour-Based Cooperation Intrusion Detection System for Wireless Sensor Networks." Applied Mechanics and Materials 263-266 (December 2012): 2972–78. http://dx.doi.org/10.4028/www.scientific.net/amm.263-266.2972.

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To identify the malicious nodes timely in wireless sensor networks(WSNs), a cooperation intrusion detection scheme based on weighted k Nearest Neighbour(kNN) is proposed. Given a few types of sensor nodes, the test model extracts the properties of sensor nodes related with the known types of malicious nodes, and establishes sample spaces of all sensor nodes which participate in network activities. According to the known node’s attributes sampled, the unknown type sensor nodes are classified based on weighted kNN. Considering of energy consumption, an intrusion detection system selection algorithm is joined in the sink node. Simulation results show that the scheme has a lower false detection rate and a higher detection rate at the same time, and it can preserve energy of detection nodes compared with an existing intrusion detection scheme.
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G. Murugan, Dr. "Improve secure based multi-path routing to mitigate the intrusion endurance in heterogeneous wireless sensor networks." International Journal of Engineering & Technology 7, no. 4 (September 26, 2018): 2746. http://dx.doi.org/10.14419/ijet.v7i4.17957.

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Wireless Sensor Networks (WSNs) have many potential applications. Multi-path routing is widely used in WSN to achieve reliability and perform Fault Tolerance. Multi-path routing determines and assigns multiple routes from a given sensor node to the sink. The transmission of data among the multi-path brings path redundancy, which increases the reliability and reduces the network congestion. In this research work, a dynamic redundancy management algorithm is proposed. To exploit multi-path routing in order to process the user request with existence of defective and malicious nodes. The objective of this work is to analyze the trade-off between energy consumption and Quality of Service (QoS) gain in security and reliability in order to increase the lifetime. The optimized redundancy level of multipath routing is determined dynamically which is used to improve the query response while extending the network lifetime and also for detecting intrusions and send alert to the system through Intrusion Detection System (IDS). Then, a voting-based distributed Intrusion Detection (ID) algorithm is proposed to detect and remove malicious nodes in a sensor network. The malicious node has been determined through number of voters using voting-based distributed ID algorithm. The efficient redundancy management of a clustered Heterogeneous Wireless Sensor Network (HWSN) is to increase the network lifetime in the presence of unreliable and malicious nodes. Therefore, the reliability improved dramatically.
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Dissertations / Theses on the topic "Malicious node detection in wireless sensor networks"

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Zia, Tanveer. "A Security Framework for Wireless Sensor Networks." University of Sydney, 2008. http://hdl.handle.net/2123/2258.

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Doctor of Philosophy (PhD)
Sensor networks have great potential to be employed in mission critical situations like battlefields but also in more everyday security and commercial applications such as building and traffic surveillance, habitat monitoring and smart homes etc. However, wireless sensor networks pose unique security challenges. While the deployment of sensor nodes in an unattended environment makes the networks vulnerable to a variety of potential attacks, the inherent power and memory limitations of sensor nodes makes conventional security solutions unfeasible. Though there has been some development in the field of sensor network security, the solutions presented thus far address only some of security problems faced. This research presents a security framework WSNSF (Wireless Sensor Networks Security Framework) to provide a comprehensive security solution against the known attacks in sensor networks. The proposed framework consists of four interacting components: a secure triple-key (STKS) scheme, secure routing algorithms (SRAs), a secure localization technique (SLT) and a malicious node detection mechanism. Singly, each of these components can achieve certain level of security. However, when deployed as a framework, a high degree of security is achievable. WSNSF takes into consideration the communication and computation limitations of sensor networks. While there is always a trade off between security and performance, experimental results prove that the proposed framework can achieve high degree of security with negligible overheads.
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Kotari, Ravi Teja. "Node failure detection and data retrieval in wireless sensor networks." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10108190.

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This project presents a method for detecting node failure in a wireless sensor network. The defective node is identified using round-trip delay measurements. Data transfer from the transmitter section to the receiver section is accomplished via the ZigBee protocol. As soon as a node has been identified as defective, the node is removed from the sensor network. Information about the failed node is provided to users with registered mobile device through the Global System for Mobile (GSM) module. The proposed method has been successfully implemented and tested experimentally on a small sensor network using the LPC2148 ARM7 microcontroller.

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Khanapure, Vishal. "Memory efficient distributed detection of node replication attacks in wireless sensor networks." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0025072.

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Al-Riyami, Ahmed. "Towards an adaptive solution to data privacy protection in hierarchical wireless sensor networks." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/towards-an-adaptive-solution-to-data-privacy-protection-in-hierarchical-wireless-sensor-networks(a096db2a-251c-4e9e-a4ff-8bfe4c6f1bf4).html.

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Hierarchical Wireless Sensor networks (WSNs) are becoming attractive to many applications due to their energy efficiency and scalability. However, if such networks are deployed in a privacy sensitive application context such as home utility consumption, protecting data privacy becomes an essential requirement. Our threat analysis in such networks has revealed that PPDA (Privacy Preserving Data Aggregation), NIDA (Node ID Anonymity) and ENCD (Early Node Compromise Detection) are three essential properties for protecting data privacy. The scope of this thesis is on protecting data privacy in hierarchical WSNs byaddressing issues in relation to two of the three properties identified, i.e., NIDA and ENCD, effectively and efficiently. The effectiveness property is achieved by considering NIDA and ENCD in an integrated manner, and the efficiency property is achieved by using an adaptive approach to security provisioning. To this end, the thesis has made the following four novel contributions. Firstly, this thesis presents a comprehensive analysis of the threats to data privacy and literature review of the countermeasures proposed to address these threats. The analysis and literature review have led to the identification of two main areas for improvements: (1) to reduce the resources consumed as the result of protecting data privacy, and (2) to address the compatibility issue between NIDA and ENCD.Secondly, a novel Adaptive Pseudonym Length Estimation (AdaptPLE) method has been proposed. The method allows the determination of a minimum acceptable length of the pseudonyms used in NIDA based on a given set of security and application related requirements and constraints. In this way, we can balance the trade-off between an ID anonymity protection level and the costs (i.e., transmission and energy) incurred in achieving the protection level. To demonstrate its effectiveness, we have evaluated the method by applying it to two existing NIDA schemes, the Efficient Anonymous Communication (EAC) scheme and theCryptographic Anonymous Scheme (CAS).Thirdly, a novel Adaptive Early Node Compromise Detection (AdaptENCD) scheme for cluster-based WSNs has been proposed. This scheme allows early detections of compromised nodes more effectively and efficiently than existing proposals. This is achieved by adjusting, at run-time, the transmission rate of heartbeat messages, used to detect nodes' aliveness, in response to the average message loss ratio in a cluster. This adaptive approach allows us to significantly reduce detection errors while keeping the number of transmitted heartbeat messages as low as possible, thus reducing transmission costs. Fourthly, a novel Node ID Anonymity Preserving Scheme (ID-APS) for clusterbased WSNs has been proposed. ID-APS protects nodes ID anonymity while, at the same time, also allowing the global identification of nodes. This later property supports the identification and removal of compromised nodes in the network, which is a significant improvement over the state-of-the-art solution, the CAS scheme. ID-APS supports both NIDA and ENCD by making a hybrid use of dynamic and global identification pseudonyms. More importantly, ID-APS achieves these properties with less overhead costs than CAS. All proposed solutions have been analysed and evaluated comprehensively to prove their effectiveness and efficiency.
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chen, stanley, and 陳嘉融. "The Malicious Node Detection and Identification Mechanisms for Wireless Sensor Networks." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/63265720978978639169.

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碩士
長庚大學
資訊管理研究所
94
Wireless Sensor Network (WSN) is a new developing type of wireless networks. Comparing with Wireless Local Area Network (WLAN), WSN has more restrictions on hardware specification of sensor node and deployment environment. The application of WSN has been growing in recent years. Many security protocols that designed for this particular network had been developed, simulated, verified and successfully implemented. However, the security challenge and difficulty that WSN has to faced will not be able to be solved completely in a few years. This study will begin with improving the whole of security of WSN to propose a suite of malicious node detection and identification mechanisms for WSN. These mechanisms include key pre-load mechanism, neighbor discovery mechanism, authentication key distribution mechanism and node authentication mechanism. The first three mechanisms may be regard as pre-processing to execute the last mechanism. After pre-processing, node in the network can easily detect whether malicious node in its communication range exists. If malicious node exists, it could be identified simultaneously.
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Li, Wei, and 李威. "A Novel Node Movement Detection Scheme in Wireless Sensor Networks." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/58345508926363398455.

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碩士
國立中央大學
資訊工程研究所
98
The location information of sensors is significant in Wireless Sensor Network (WSN). However, after deploying and localizing the sensors, the location of sensors has to maintain constantly. Sensors may be moved from their original location by the depredation of the enemy or natural phenomena, and further, sensors return the inconsistent information with their incorrect location to sink or spread the incorrect location to the network resulted in arising problem with geographic protocol or applications. Consequently, in this paper, we proposed a new light-weight distributed scheme which is utilized the movement before and after with different topology to detect moved nodes. The simulation results show that our scheme can cost a bit of communication overhead and has high detection rate especially with large scale node movement.
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Nkululeko, Zwane Patrick, and Zwane Patrick Nkululeko. "A Novel Correlated Attributes Model for Malicious Detection in Wireless Sensor Networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/97yunq.

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碩士
國立臺北科技大學
電資學院外國學生專班
105
Wireless Sensor Networks (WSNs) presents a unique impact in our communities today. This technology has many advantages but their security issues have not been given much consideration till now. For that, a need is evident to equip sensor nodes with security mechanisms to defend against attacks. The defense mechanisms will help to detect and isolate the compromised nodes in order to avoid being misled by the fabricated information injected by the attacker through them. Since security issues are a big concern in WSNs, a Correlated Attributes Model (CAM) is proposed. CAM detects malicious activity against Sybil, sinkhole, wormhole, and blackhole attack. The simulation is performed by using NS2, to verify CAMs performance and efficiency using common WSNs evaluation parameters. Ad hoc On-demand Distance Vector (AODV) is use as a routing protocol to examined effect of malicious attacks. Throughput, end to end delay and packet delivery ratio/fraction are used to measure the performance of the solution.
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Chen, Hsin-Hsiu, and 陳新秀. "An improved SPRT detection method for replication node in fault tolerant wireless sensor networks." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/3wq42g.

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碩士
國立交通大學
資訊管理研究所
105
As the Internet of Things came, the application of the wireless sensor networks has increased. Meanwhile, there are also many threats of networks security need to be dealt with. One of the network attacks is the replication attack. The attackers may replicate few of the nodes to be considered as the legitimate nodes. The cloned nodes would integrate into the original network and launch a variety of internal attacks. There are several replica detections in the literature for the mobile environment. Most of the detections are limited by high computation and communication cost. Some of detections based on the Sequential Probability Ratio Test have much lower system overhead. However, these prior works decrease the accuracy when sensors lie in a server environment so that sensors are prone to retransmit the message. This paper proposes a replica detection based on the SPRT in fault tolerant wireless sensor network. In order to improve the accuracy of the judgment, we use the power of nodes and the slope of energy as the appendix and apply the SPRT to adjust the replica detection dynamically in the fault tolerant environment. The experiments show that our proposed scheme achieves better performance on both efficiency of detecting and reduction of error rate than the prior work.
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Lee, Yi-Chang, and 李宜昌. "A Distributed Protocol for the Detection of Node Replication Attacks in Wireless Sensor Networks." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/13353818875293089256.

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碩士
國立中正大學
資訊管理所暨醫療資訊管理所
97
The applications for wireless sensor networks (WSNs) were originally motivated by military applications like battlefield surveillance. Today, wireless sensor networks are widely used in civilian application areas, such as home security monitoring, healthcare applications, traffic control. However, a novel attack, named node replication attacks are proved to be a harmful attack. The node replication attack is an attack that the adversary captures a node and replicates the node in a large number of clones. After that, the adversary will insert the replicated nodes in the network. At last the adversary will control the network gradually. A few solutions have recently been proposed. Nevertheless, these solutions are still unable to solve following issues- First, the large memory overhead and high computational complexity are unsuitable for WSNs. The communication cost and probabilities of detection are important issues as well. Further, a detection could be used in mobile sensor networks is more satisfactory. The contributions of this paper include (1) an efficient memory overhead and communication cost of detection protocol is suggested, (2) mobile sensor nodes are concerned in our protocol. It is possible for the sensor nodes to be mobile, not only stationary. (3) The security analysis and simulation experiments are also proposed. The results show that the probability of deletion is high, our protocol is useful.
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Li, Zhijun. "Efficient Authentication, Node Clone Detection, and Secure Data Aggregation for Sensor Networks." Thesis, 2010. http://hdl.handle.net/10012/5739.

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Sensor networks are innovative wireless networks consisting of a large number of low-cost, resource-constrained sensor nodes that collect, process, and transmit data in a distributed and collaborative way. There are numerous applications for wireless sensor networks, and security is vital for many of them. However, sensor nodes suffer from many constraints, including low computation capability, small memory, limited energy resources, susceptibility to physical capture, and the lack of infrastructure, all of which impose formidable security challenges and call for innovative approaches. In this thesis, we present our research results on three important aspects of securing sensor networks: lightweight entity authentication, distributed node clone detection, and secure data aggregation. As the technical core of our lightweight authentication proposals, a special type of circulant matrix named circulant-P2 matrix is introduced. We prove the linear independence of matrix vectors, present efficient algorithms on matrix operations, and explore other important properties. By combining circulant-P2 matrix with the learning parity with noise problem, we develop two one-way authentication protocols: the innovative LCMQ protocol, which is provably secure against all probabilistic polynomial-time attacks and provides remarkable performance on almost all metrics except one mild requirement for the verifier's computational capacity, and the HB$^C$ protocol, which utilizes the conventional HB-like authentication structure to preserve the bit-operation only computation requirement for both participants and consumes less key storage than previous HB-like protocols without sacrificing other performance. Moreover, two enhancement mechanisms are provided to protect the HB-like protocols from known attacks and to improve performance. For both protocols, practical parameters for different security levels are recommended. In addition, we build a framework to extend enhanced HB-like protocols to mutual authentication in a communication-efficient fashion. Node clone attack, that is, the attempt by adversaries to add one or more nodes to the network by cloning captured nodes, imposes a severe threat to wireless sensor networks. To cope with it, we propose two distributed detection protocols with difference tradeoffs on network conditions and performance. The first one is based on distributed hash table, by which a fully decentralized, key-based caching and checking system is constructed to deterministically catch cloned nodes in general sensor networks. The protocol performance of efficient storage consumption and high security level is theoretically deducted through a probability model, and the resulting equations, with necessary adjustments for real application, are supported by the simulations. The other is the randomly directed exploration protocol, which presents notable communication performance and minimal storage consumption by an elegant probabilistic directed forwarding technique along with random initial direction and border determination. The extensive experimental results uphold the protocol design and show its efficiency on communication overhead and satisfactory detection probability. Data aggregation is an inherent requirement for many sensor network applications, but designing secure mechanisms for data aggregation is very challenging because the aggregation nature that requires intermediate nodes to process and change messages, and the security objective to prevent malicious manipulation, conflict with each other to a great extent. To fulfill different challenges of secure data aggregation, we present two types of approaches. The first is to provide cryptographic integrity mechanisms for general data aggregation. Based on recent developments of homomorphic primitives, we propose three integrity schemes: a concrete homomorphic MAC construction, homomorphic hash plus aggregate MAC, and homomorphic hash with identity-based aggregate signature, which provide different tradeoffs on security assumption, communication payload, and computation cost. The other is a substantial data aggregation scheme that is suitable for a specific and popular class of aggregation applications, embedded with built-in security techniques that effectively defeat outside and inside attacks. Its foundation is a new data structure---secure Bloom filter, which combines HMAC with Bloom filter. The secure Bloom filter is naturally compatible with aggregation and has reliable security properties. We systematically analyze the scheme's performance and run extensive simulations on different network scenarios for evaluation. The simulation results demonstrate that the scheme presents good performance on security, communication cost, and balance.
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Book chapters on the topic "Malicious node detection in wireless sensor networks"

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Yang, Hongyu, Xugao Zhang, and Fang Cheng. "A Novel Wireless Sensor Networks Malicious Node Detection Method." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 697–706. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21373-2_59.

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Roshini, A., K. V. D. Kiran, and K. V. Anudeep. "Hybrid Acknowledgment Scheme for Early Malicious Node Detection in Wireless Sensor Networks." In Advances in Intelligent Systems and Computing, 263–70. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6176-8_29.

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Li, Zhijun, and Guang Gong. "DHT-Based Detection of Node Clone in Wireless Sensor Networks." In Ad Hoc Networks, 240–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11723-7_16.

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Sonekar, Shrikant V., Manali M. Kshirsagar, and Latesh Malik. "Cluster Head Selection and Malicious Node Detection in Wireless Ad Hoc Networks." In Advances in Intelligent Systems and Computing, 547–54. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6005-2_55.

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Prasain, Prakash, and Dong-You Choi. "Nullifying Malicious Users for Cooperative Spectrum Sensing in Cognitive Radio Networks Using Outlier Detection Methods." In Ubiquitous Computing Application and Wireless Sensor, 123–31. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-9618-7_12.

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Sonekar, Shrikant V., and Manali M. Kshirsagar. "Mitigating Packet Dropping Problem and Malicious Node Detection Mechanism in Ad Hoc Wireless Networks." In Advances in Intelligent Systems and Computing, 317–28. New Delhi: Springer India, 2015. http://dx.doi.org/10.1007/978-81-322-2695-6_27.

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Zhi, Hanxiao, Peng Li, He Xu, and Feng Zhu. "Node Fault Detection Algorithm Based on Spatial and Temporal Correlation in Wireless Sensor Networks." In Advances in Intelligent Systems and Computing, 196–205. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93659-8_17.

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Qin, Danyang, Songxiang Yang, Ping Ji, and Qun Ding. "Secure Communication Mechanism Based on Key Management and Suspect Node Detection in Wireless Sensor Networks." In Machine Learning and Intelligent Communications, 692–700. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73564-1_71.

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Chen, Yu, Hao Chen, and Wei-Shinn Ku. "Malicious Node Detection in Wireless Sensor Networks." In Wireless Networks and Mobile Communications. Auerbach Publications, 2009. http://dx.doi.org/10.1201/9781420068405.ch17.

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Singh, Mandeep, Navjyot Kaur, Amandeep Kaur, and Gaurav Pushkarna. "A Comparative Evaluation of Mining Techniques to Detect Malicious Node in Wireless Sensor Networks." In Sensor Technology, 881–94. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-2454-1.ch042.

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Wireless sensor networks have gained attention over the last few years and have significant applications for example remote supervising and target watching. They can communicate with each other though wireless interface and configure a network. Wireless sensor networks are often deployed in an unfriendly location and most of time it works without human management; individual node may possibly be compromised by the adversary due to some constraints. In this manner, the security of a wireless sensor network is critical. This work will focus on evaluation of mining techniques that can be used to find malicious nodes. The detection mechanisms provide the accuracy of the classification using different algorithm to detect the malicious node. Pragmatically the detection accuracy of J48 is 99.17%, Random Forest is 80.83%, NF Tree is 81.67% and BF Tree is 72.33%. J48 have very high detection accuracy as compared with BF Tree, NF Tree Random Forest.
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Conference papers on the topic "Malicious node detection in wireless sensor networks"

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Atassi, A., N. Sayegh, I. Elhajj, A. Chehab, and A. Kayssi. "Malicious Node Detection in Wireless Sensor Networks." In 2013 Workshops of 27th International Conference on Advanced Information Networking and Applications (WAINA). IEEE, 2013. http://dx.doi.org/10.1109/waina.2013.135.

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Padmaja, P., and G. V. Marutheswar. "Detection of Malicious Node in Wireless Sensor Network." In 2017 IEEE 7th International Advance Computing Conference (IACC). IEEE, 2017. http://dx.doi.org/10.1109/iacc.2017.0052.

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Ping Yi, Yue Wu, and Jianhua Li. "Malicious node detection in ad hoc networks using timed automata." In IET Conference on Wireless, Mobile and Sensor Networks 2007 (CCWMSN07). IEE, 2007. http://dx.doi.org/10.1049/cp:20070131.

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Curiac, Daniel-Ioan, Ovidiu Banias, Florin Dragan, Constantin Volosencu, and Octavian Dranga. "Malicious Node Detection in Wireless Sensor Networks Using an Autoregression Technique." In Third International Conference on Networking and Services. ICNS 2007. IEEE, 2007. http://dx.doi.org/10.1109/icns.2007.79.

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Jaint, Bhavnesh, S. Indu, Neeta Pandey, and Khushbu Pahwa. "Malicious Node Detection in Wireless Sensor Networks Using Support Vector Machine." In 2019 3rd International Conference on Recent Developments in Control, Automation & Power Engineering (RDCAPE). IEEE, 2019. http://dx.doi.org/10.1109/rdcape47089.2019.8979125.

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Al-Maslamani, Noora, and Mohamed Abdallah. "Malicious Node Detection in Wireless Sensor Network using Swarm Intelligence Optimization." In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT). IEEE, 2020. http://dx.doi.org/10.1109/iciot48696.2020.9089527.

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Rani, K. Sasi Kala, and R. Vijayalakshmi. "Experimental Evaluations of Malicious Node Detection on Wireless Sensor Network Environment." In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, 2021. http://dx.doi.org/10.1109/iciccs51141.2021.9432131.

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Priyanka, J. Steffi Agino, S. Tephillah, and A. M. Balamurugan. "Malicious node detection using minimal event cycle computation method in wireless sensor networks." In 2014 International Conference on Communications and Signal Processing (ICCSP). IEEE, 2014. http://dx.doi.org/10.1109/iccsp.2014.6949975.

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Kumar, Sumit, and Shabana Mehfuz. "A PSO Based Malicious Node Detection and Energy Efficient Clustering in Wireless Sensor Network." In 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2019. http://dx.doi.org/10.1109/spin.2019.8711585.

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Jaint, Bhavnesh, Vishwamitra Singh, Lalit Kumar Tanwar, S. Indu, and Neeta Pandey. "An Efficient Weighted Trust Method for Malicious Node Detection in Clustered Wireless Sensor Networks." In 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES). IEEE, 2018. http://dx.doi.org/10.1109/icpeices.2018.8897307.

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