Journal articles: '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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Yim, Sung-Jib, and Yoon-Hwa Choi. "Neighbor-Based Malicious Node Detection in Wireless Sensor Networks." Wireless Sensor Network 04, no. 09 (2012): 219–25. http://dx.doi.org/10.4236/wsn.2012.49032.

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Chen, Miao, Bao Peng, and Liang Ma. "A New Security Localization Method for Detecting Malicious Beacon Nodes in Wireless Sensor Networks." Advanced Materials Research 186 (January 2011): 428–32. http://dx.doi.org/10.4028/www.scientific.net/amr.186.428.

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In this paper, we address wireless sensor network localization problems that have high reliability in an environment where physical node destruction is possible. We propose a range-independent localization algorithm called security localization based on Detecting malicious beacon nodes (DMBSL) that allows sensors to passively determine their location with high reliability, without increasing the number of reference points, or the complexity of the hardware of each reference point or node. In DMBSL, constraints of wireless sensor network are used to find and remove the malicious beacon nodes, then the maximum likelihood method is used to calculate the location of unknown nodes, so that the location calculation is very robust and is able to resist malicious attacks. In this paper, the location performance of DMBSL algorithm is deeply analyzed. The results of the simulation show the algorithm can get lower average positioning error, meantime malicious attacks have little side effects to location performance.

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Jianming, Zhou, Liu Fan, and Lu Qiuyuan. "Data Fusion Based on Node Trust Evaluation in Wireless Sensor Networks." Journal of Sensors 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/391401.

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Abnormal behavior detection and trust evaluation mode of traditional sensor node have a single function without considering all the factors, and the trust value algorithm is relatively complicated. To avoid these above disadvantages, a trust evaluation model based on the autonomous behavior of sensor node is proposed in this paper. Each sensor node has the monitoring privilege and obligation. Neighboring sensor nodes can monitor each other. Their direct and indirect trust values can be achieved by using a relatively simple calculation method, the synthesis trust value of which could be got according to the composition rule of D-S evidence theory. Firstly, the cluster head assigns different weighted value for the data from each sensor node, then the weight vector is set according to the synthesis trust value, the data fusion processing is executed, and finally the cluster head sensor node transmits the fused result to the base station. Simulation experiment results demonstrate that the trust evaluation model can rapidly, exactly, and effectively recognize malicious sensor node and avoid malicious sensor node becoming cluster head sensor node. The proposed algorithm can greatly increase the safety and accuracy of data fusion, improve communication efficiency, save energy of sensor node, suit different application fields, and deploy environments.

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Singh, Neha, Deepali Virmani, and Xiao-Zhi Gao. "A Fuzzy Logic-Based Method to Avert Intrusions in Wireless Sensor Networks Using WSN-DS Dataset." International Journal of Computational Intelligence and Applications 19, no. 03 (August 19, 2020): 2050018. http://dx.doi.org/10.1142/s1469026820500182.

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Intrusion is one of the biggest problems in wireless sensor networks. Because of the evolution in wired and wireless mechanization, various archetypes are used for communication. But security is the major concern as networks are more prone to intrusions. An intrusion can be dealt in two ways: either by detecting an intrusion in a wireless sensor network or by preventing an intrusion in a wireless sensor network. Many researchers are working on detecting intrusions and less emphasis is given on intrusion prevention. One of the modern techniques for averting intrusions is through fuzzy logic. In this paper, we have defined a fuzzy rule-based system to avert intrusions in wireless sensor network. The proposed system works in three phases: feature extraction, membership value computation and fuzzified rule applicator. The proposed method revolves around predicting nodes in three categories as “red”, “orange” and “green”. “Red” represents that the node is malicious and prevents it from entering the network. “Orange” represents that the node “might be malicious” and marks it suspicious. “Green” represents that the node is not malicious and it is safe to enter the network. The parameters for the proposed FzMAI are packet send to base station, energy consumption, signal strength, a packet received and PDR. Evaluation results show an accuracy of 98.29% for the proposed system. A detailed comparative analysis concludes that the proposed system outperforms all the other considered fuzzy rule-based systems. The advantage of the proposed system is that it prevents a malicious node from entering the system, thus averting intrusion.

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Santhosh, J., A. Rajivkannan, and R. Vijayarajeswari. "Energy Efficient Malicious Node Detection System in Wireless Sensor Networks." Asian Journal of Research in Social Sciences and Humanities 7, no. 2 (2017): 624. http://dx.doi.org/10.5958/2249-7315.2017.00115.0.

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Ranjan Vikas, Shweta, B. Priyalakshmi, Nikita Gautam, and Sairam Potti. "Co-operative Detection for Malicious Nodes in Under-Attack WSN." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 489. http://dx.doi.org/10.14419/ijet.v7i2.24.12143.

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The network security must be taken into consideration in wireless sensor networks. In our project, we take sensor node data falsification (SNDF) attack using malicious nodes and co-operative detection is used. Fusioncentre collects information from the nodes created in a cluster environment and makes a global decision. The protocol used here is Ad-hoc-on demand distance vector[5] (AODV) and the performance analysis is done using parameters such as throughput and End-to-end delay. The stimulation is done in NS2 using network animator and graphical results are taken.The throughput will be increased compared to the existing system whereas End-to-End delay will be decreased.

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Sunitha R. and Chandrika J. "Malevolent Node Detection Based on Network Parameters Mining in Wireless Sensor Networks." International Journal of Digital Crime and Forensics 13, no. 5 (September 2021): 130–44. http://dx.doi.org/10.4018/ijdcf.20210901.oa8.

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The exponential growth of the internet of things and united applications have renewed the scholarly world to grow progressively proficient routing strategies. Quality of service (QoS) and reduced power consumption are the major requirements for effective data transmission. The larger part of the applications nowadays including internet of things (IoT) communication request power effective and QoS-driven WSN configuration. In this paper, an exceptionally strong and effective evolutionary computing allied WSN routing convention is designed for QoS and power effectiveness. The proposed routing convention includes proficient capacity called network condition-based malicious node detection. It adventures or mines the dynamic node/network parameters to recognize malignant nodes. Experimentation is done using network simulator tool NS2. Results ensure that the proposed routing model accomplishes higher throughput, low energy utilization, and low delay that sustains its suitability for real-time WSN.

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Dr. M. Pushparani, Dr M. Pushparani, and Komathi A. Komathi A. "Malicious Node Detection using Belief based Protected Routing (BPR) in Autonomous Wireless Sensor Network." Global Journal For Research Analysis 3, no. 8 (June 15, 2012): 119–22. http://dx.doi.org/10.15373/22778160/august2014/39.

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Ramírez Gómez, Julián, Héctor Fernando Vargas Montoya, and Álvaro León Henao. "Implementing a Wormhole Attack on Wireless Sensor Networks with XBee S2C Devices." Revista Colombiana de Computación 20, no. 1 (May 28, 2019): 41–58. http://dx.doi.org/10.29375/25392115.3606.

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One of the most dangerous threats to Wireless Sensor Networks (WSN) are wormhole attacks, due to their capacity to manipulate routing and application data in real time and cause important damages to the integrity, availability, and confidentiality of network data. An empirical method to launch a successful attack on IEEE 802.15.4/Zigbee devices with source routing enabled is adopted in this work to find signatures for detecting wormhole attacks in real environments. It uses the KillerBee framework with algorithms for packet manipulation through a malicious node to capture and inject malicious packets in victim nodes. Besides, a reverse variant of wormhole attack is presented and executed. To evidence the realization of this threat by the attacking software, the experimental framework includes XBee S2C nodes. The results include recommendations, detection signatures and future work to face wormhole attacks involving source routing protocols like DSR.

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Fu, Hao, Yinghong Liu, Zhe Dong, and Yuanming Wu. "A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks." Sensors 20, no. 1 (December 19, 2019): 23. http://dx.doi.org/10.3390/s20010023.

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In cluster-based wireless sensor networks, cluster heads (CHs) gather and fuse data packets from sensor nodes; then, they forward fused packets to the sink node (SN). This helps wireless sensor networks balance energy effectively and efficiently to prolong their lifetime. However, cluster-based WSNs are vulnerable to selective forwarding attacks. Compromised CHs would become malicious and launch selective forwarding attacks in which they drop part of or all the packets from other nodes. In this paper, a data clustering algorithm (DCA) for detecting a selective forwarding attack (DCA-SF) is proposed. It can capture and isolate malicious CHs that have launched selective forwarding attacks by clustering their cumulative forwarding rates (CFRs). The DCA-SF algorithm has been strengthened by changing the DCA parameters (Eps, Minpts) adaptively. The simulation results show that the DCA-SF has a low missed detection rate of 1.04% and a false detection rate of 0.42% respectively with low energy consumption.

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Lim, Sung, and Yoon-Hwa Choi. "Malicious Node Detection Using a Dual Threshold in Wireless Sensor Networks." Journal of Sensor and Actuator Networks 2, no. 1 (February 5, 2013): 70–84. http://dx.doi.org/10.3390/jsan2010070.

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Ram Prabha, V., and P. Latha. "Fuzzy Trust Protocol for Malicious Node Detection in Wireless Sensor Networks." Wireless Personal Communications 94, no. 4 (September 10, 2016): 2549–59. http://dx.doi.org/10.1007/s11277-016-3666-1.

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She, Wei, Qi Liu, Zhao Tian, Jian-Sen Chen, Bo Wang, and Wei Liu. "Blockchain Trust Model for Malicious Node Detection in Wireless Sensor Networks." IEEE Access 7 (2019): 38947–56. http://dx.doi.org/10.1109/access.2019.2902811.

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Fanaeepour, Maryam, and Mohammad Reza Kangavari. "Malicious node detection system in wireless sensor networks: a decentralised approach." International Journal of Internet Technology and Secured Transactions 2, no. 1/2 (2010): 88. http://dx.doi.org/10.1504/ijitst.2010.031473.

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Hu, Hongbing, Yu Chen, Wei Shinn Ku, Zhou Su, and Chung Han J. Chen. "Weighted trust evaluation-based malicious node detection for wireless sensor networks." International Journal of Information and Computer Security 3, no. 2 (2009): 132. http://dx.doi.org/10.1504/ijics.2009.028810.

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Hyun Oh, Seo, Chan O. Hong, and Yoon Hwa Choi. "A Malicious and Malfunctioning Node Detection Scheme for Wireless Sensor Networks." Wireless Sensor Network 04, no. 03 (2012): 84–90. http://dx.doi.org/10.4236/wsn.2012.43012.

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Ho, Jun-Won, Matthew Wright, and Sajal K. Das. "Distributed detection of mobile malicious node attacks in wireless sensor networks." Ad Hoc Networks 10, no. 3 (May 2012): 512–23. http://dx.doi.org/10.1016/j.adhoc.2011.09.006.

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Jayashree, Devasagayam, V. Uma Rani, and K. Soma Sundaram. "Trust Based Misbehavior Detection in Wireless Sensor Networks." Applied Mechanics and Materials 622 (August 2014): 191–98. http://dx.doi.org/10.4028/www.scientific.net/amm.622.191.

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Due to emerging technology Wireless Sensor Network (WSN), it is necessary to monitor the behavior of sensor nodes and establish the secure communication in network. Security is a challenging task in wireless environment. Several encryption mechanisms are available to prevent outsider attacks, but no mechanism available for insider attacks. A trust model is a collection of rules used to establish co-operation or collaboration among nodes as well as monitoring misbehavior of wireless sensor networks. Trust model is necessary to enhance secure localization, communication or routing, aggregation, collaboration among nodes. In this paper, proposed a behavior based distributed trust model for wireless sensor network to effectively deal with self-ish or malicious nodes. Here, take multidimensional trust attributes derived from communications and networks to evaluate the overall trust of sensor nodes. It monitors the behavior of nodes and establishes secure communication among networks.

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Amarasimha T. and V. Srinivasa Rao. "Efficient Energy Conservation and Faulty Node Detection on Machine Learning-Based Wireless Sensor Networks." International Journal of Grid and High Performance Computing 13, no. 2 (April 2021): 1–20. http://dx.doi.org/10.4018/ijghpc.2021040101.

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Wireless sensor networks are used in machine learning for data communication and classification. Sensor nodes in network suffer from low battery power, so it is necessary to reduce energy consumption. One way of decreasing energy utilization is reducing the information transmitted by an advanced machine learning process called support vector machine. Further, nodes in WSN malfunction upon the occurrence of malicious activities. To overcome these issues, energy conserving and faulty node detection WSN is proposed. SVM optimizes data to be transmitted via one-hop transmission. It sends only the extreme points of data instead of transmitting whole information. This will reduce transmitting energy and accumulate excess energy for future purpose. Moreover, malfunction nodes are identified to overcome difficulties on data processing. Since each node transmits data to nearby nodes, the misbehaving nodes are detected based on transmission speed. The experimental results show that proposed algorithm provides better results in terms of reduced energy consumption and faulty node detection.

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Agarkhed, Jayashree, and Gauri Kalnoor. "Intrusion detection using ant colony approach in wireless sensor networks." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 657. http://dx.doi.org/10.14419/ijet.v7i4.5.21180.

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Design of an intrusion detection system in the sensor network to improve the behavior of the network is the major challenge is theVariety of intrusion detection mechanisms are being used now a days, to provide security in Wireless Sensor networks (WSN). Since WSN works with set of tiny nodes called as sensor nodes, there are high chances of intrusions for malicious attacks. WSN is deployed in medium open to many users wherever possible. A multiple sensing environment of WSN consists of sensors which acts as agents called as multi agents system for detecting an intruder. Ant colony is an effective approach where each agent communicate with each other for updating the information of intruder to the colony administration. The multi agents based system is best phenomenon suitable for optimization of ant colony. In this approach, the ants form a colony where it goes for search continuously until an intruder is found and once searched, it returns back with the best shortest path available with path traces stored in its database for its future reference. An optimized multi agent approach using ant colony is proposed for detection of lightweight intruders for WSN to protect against harmful malicious attacks.

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Sundararajan, Ranjeeth Kumar, and Umamakeswari Arumugam. "Intrusion Detection Algorithm for Mitigating Sinkhole Attack on LEACH Protocol in Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/203814.

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In wireless sensor network (WSN), the sensors are deployed and placed uniformly to transmit the sensed data to a centralized station periodically. So, the major threat of the WSN network layer is sinkhole attack and it is still being a challenging issue on the sensor networks, where the malicious node attracts the packets from the other normal sensor nodes and drops the packets. Thus, this paper proposes an Intrusion Detection System (IDS) mechanism to detect the intruder in the network which uses Low Energy Adaptive Clustering Hierarchy (LEACH) protocol for its routing operation. In the proposed algorithm, the detection metrics, such as number of packets transmitted and received, are used to compute the intrusion ratio (IR) by the IDS agent. The computed numeric or nonnumeric value represents the normal or malicious activity. As and when the sinkhole attack is captured, the IDS agent alerts the network to stop the data transmission. Thus, it can be a resilient to the vulnerable attack of sinkhole. Above all, the simulation result is shown for the proposed algorithm which is proven to be efficient compared with the existing work, namely, MS-LEACH, in terms of minimum computational complexity and low energy consumption. Moreover, the algorithm was numerically analyzed using TETCOS NETSIM.

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C, Narmatha. "A New Neural Network-Based Intrusion Detection System for Detecting Malicious Nodes in WSNs." Journal of Computational Science and Intelligent Technologies 1, no. 3 (2020): 1–8. http://dx.doi.org/10.53409/mnaa.jcsit20201301.

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The Wireless Sensor Networks (WSNs) are vulnerable to numerous security hazards that could affect the entire network performance, which could lead to catastrophic problems such as a denial of service attacks (DoS). The WSNs cannot protect these types of attacks by key management protocols, authentication protocols, and protected routing. A solution to this issue is the intrusion detection system (IDS). It evaluates the network with adequate data obtained and detects the sensor node(s) abnormal behavior. For this work, it is proposed to use the intrusion detection system (IDS), which recognizes automated attacks by WSNs. This IDS uses an improved LEACH protocol cluster-based architecture designed to reduce the energy consumption of the sensor nodes. In combination with the Multilayer Perceptron Neural Network, which includes the Feed Forward Neutral Network (FFNN) and the Backpropagation Neural Network (BPNN), IDS is based on fuzzy rule-set anomaly and abuse detection based learning methods based on the fugitive logic sensor to monitor hello, wormhole and SYBIL attacks.

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Amouri, Amar, Vishwa T. Alaparthy, and Salvatore D. Morgera. "A Machine Learning Based Intrusion Detection System for Mobile Internet of Things." Sensors 20, no. 2 (January 14, 2020): 461. http://dx.doi.org/10.3390/s20020461.

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Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

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Ouyang Xi, Li Dan, Zhang Jianyi, Liu Hongwei, Zhu Hongliang, and Xin Yang. "Malicious node detection in wireless sensor networks using time series analysis on node reputation." Journal of Convergence Information Technology 7, no. 15 (August 31, 2012): 8–16. http://dx.doi.org/10.4156/jcit.vol7.issue15.2.

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Balamurugan, P., M. Shyamala Devi, and V. Sharmila. "Detecting malicious nodes using data aggregation protocols in wireless sensor networks." International Journal of Engineering & Technology 7, no. 1.1 (December 21, 2017): 594. http://dx.doi.org/10.14419/ijet.v7i1.1.10365.

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At present scenario, sensor devices are used in various fields for gathering information so all those data should be secured safely. Securing data is an important role in Wireless Sensor Networks (WSN). WSN is extremely essential for the purpose of reducing the complete redundancy and energy consumption during gathering data among sensor nodes. Optimized data aggregation is needed at cluster head and Base Station (BS) for secured data transmission. Data aggregation is performed in all routers while forwarding data from source to destination node. The complete life time of sensor networks is reducing because of using energy inefficient nodes for the purpose of aggregation. So this paper introduces the optimized methods for securing data (OMSD) which is trust based weights and also completely about the attacks and some methods for secured data transmission.

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Morsi, Asmaa M., Tamer M. Barakat, and Ahmed Ali Nashaat. "An Efficient and Secure Malicious Node Detection Model for Wireless Sensor Networks." International journal of Computer Networks & Communications 12, no. 1 (January 31, 2020): 97–108. http://dx.doi.org/10.5121/ijcnc.2020.12107.

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Thakur, Mandeep, and Amninder Kaur Gill. "Detection and Isolation Technique for Blackhole Attack in Wireless Sensor Network." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (August 30, 2017): 25. http://dx.doi.org/10.23956/ijarcsse.v7i8.12.

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A wireless sensor network comprises of countless spread over a particular territory where we need to take care of at the progressions going ahead there. A sensor hub, for the most part, comprises of sensors, actuators, memory, a processor and they do have correspondence capacity. These sorts of networks are much powerless against security attacks. Many kinds of active and passive attacks are conceivable in the sensor network. Among all the conceivable active attacks, sinkhole attack is the most widely recognized and destructive attack. This attack debases network execution and prompts denial of service attack. The attack is triggered by the malicious hub which is available in the network. In this work, a novel strategy has been proposed to recognize and disengage malicious nodes from the network which are in charge of triggering the attack. The novel procedure is based on blacklist technique and clustering technique. The exploratory results will demonstrate that proposed strategy detects and separate the malicious nodes from the network proficiently. It will enhance network effectiveness as far as bundle misfortune, defer and expand throughput of the network. NS2 simulator instrument will be utilized as a part of it.

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Shaukat, Haafizah Rameeza, Fazirulhisyam Hashim, Muhammad Arslan Shaukat, and Kamal Ali Alezabi. "Hybrid Multi-Level Detection and Mitigation of Clone Attacks in Mobile Wireless Sensor Network (MWSN)." Sensors 20, no. 8 (April 17, 2020): 2283. http://dx.doi.org/10.3390/s20082283.

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Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes’ important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead.

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Zhu, Hongliang, Zhihua Zhang, Juan Du, Shoushan Luo, and Yang Xin. "Detection of selective forwarding attacks based on adaptive learning automata and communication quality in wireless sensor networks." International Journal of Distributed Sensor Networks 14, no. 11 (November 2018): 155014771881504. http://dx.doi.org/10.1177/1550147718815046.

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Wireless sensor networks face threats of selective forwarding attacks which are simple to implement but difficult to detect. It is difficult to distinguish between malicious packet dropping and the normal packet loss on unstable wireless channels. For this situation, a selective forwarding attack detection method is proposed based on adaptive learning automata and communication quality; the method can eliminate the impact of normal packet loss on selective forwarding attack detection and can detect ordinary selective forwarding attack and special cases of selective forwarding attack. The current and comprehensive communication quality of nodes are employed to reflect the short- and long-term forwarding behaviors of nodes, and the normal packet loss caused by unstable channels and medium-access-control layer collisions is considered. The adaptive reward and penalty parameters of a detection learning automata are determined by the comprehensive communication quality of the node and the voting of its neighbors to reward normal nodes or punish malicious ones. Simulation results indicate the effectiveness of the proposed method in detecting ordinary selective forwarding attacks, black-hole attacks, on-off attacks, and energy exhaustion attacks. In addition, the communication overhead of the method is lower than that of other methods.

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Wu, Renyong, Xue Deng, Rongxing Lu, and Xuemin (Sherman) Shen. "Trust-Based Anomaly Detection in Emerging Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/363569.

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Wireless sensor networks (WSNs) consist of a large number of small-size, energy-constrained nodes and generally are deployed to monitor surrounding situation or relay generated packets in other devices. However, due to the openness of wireless media and the inborn self-organization feature of WSNs, that is, frequent interoperations among neighbouring nodes, network security has been tightly related to data credibility and/or transmission reliability, thus trust evaluation of network nodes is becoming another interesting issue. Obviously, how to describe node’s behaviors and how to integrate various characteristics to make the final decision are two major research aspects of trust model. In this paper, a new trust model is proposed to detect anomaly nodes based on fuzzy theory and revised evidence theory. By monitoring the behaviors of the evaluated nodes with multidimensional characteristics and integrating these pieces of information, the malicious nodes in a network can be identified and the normal operation of the whole network can be verified. In addition, to accelerate the detection process, a weighting judgment mechanism is adopted to deal with the uncertain states of evaluated nodes. Finally extensive simulations are conducted, and the results demonstrate that the proposed trust model can achieve higher detection ratio of malicious nodes in comparison with the previously reported results.

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Zhang, Zhi Ming, Xiao Yong Xiong, and Jian Gang Deng. "A Novel Secure Data Transport Scheme for Wireless Sensor Networks." Applied Mechanics and Materials 170-173 (May 2012): 3425–30. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.3425.

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Sensor nodes may be deployed in hostile environments, and the sensed data is sent to the destination along the routing path, if a forwarding node in the routing path is compromised by the adversary, the data can not arrive at the destination. There are many studies on detection of malicious or compromised node, and remove the compromised nodes form the routing path, but, efficient, reliable, and secure broadcast are the major problems of the schemes. In this paper, we propose a novel secure data transport scheme for wireless sensor networks. The proposed scheme divide the secure data into n shares pair using Asmuth-Bloom threshold secret sharing scheme, and forwarded the n shares pair along the multi-path to the base station, the base station only receives k distinct shares pair from the n shares pair, he can obtain the secure data. The proposed protocol can resist selective forwarding attack, false data injection attack, replay attack, and even if there are compromised nodes in some routing paths, the base station can still get the correct secure data without removing the compromised nodes from the routing paths.

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Ghugar, Umashankar, Jayaram Pradhan, Sourav Kumar Bhoi, and Rashmi Ranjan Sahoo. "LB-IDS: Securing Wireless Sensor Network Using Protocol Layer Trust-Based Intrusion Detection System." Journal of Computer Networks and Communications 2019 (January 6, 2019): 1–13. http://dx.doi.org/10.1155/2019/2054298.

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Wireless sensor network (WSN) faces severe security problems due to wireless communication between the nodes and open deployment of the nodes. The attacker disrupts the security parameters by launching attacks at different layers of the WSN. In this paper, a protocol layer trust-based intrusion detection system (LB-IDS) is proposed to secure the WSN by detecting the attackers at different layers. The trust value of a sensor node is calculated using the deviation of trust metrics at each layer with respect to the attacks. Mainly, we consider trustworthiness in the three layers such as physical layer trust, media access control (MAC) layer trust, and network layer trust. The trust of a sensor node at a particular layer is calculated by taking key trust metrics of that layer. Finally, the overall trust value of the sensor node is estimated by combining the individual trust values of each layer. By applying the trust threshold, a sensor node is detected as trusted or malicious. The performance of LB-IDS is evaluated by comparing the results of the three performance parameters such as detection accuracy, false-positive rate, and false-negative rate, with the results of Wang’s scheme. We have implemented jamming attack at the physical layer, back-off manipulation attack at the MAC layer, and sinkhole attack at the network layer using simulations. We have also implemented a cross-layer attack using the simulation where an attacker simultaneously attacks the MAC layer and network layer. Simulation results show that the proposed LB-IDS performs better as compared with Wang’s scheme.

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Ko, Jongbin, Jungtaek Seo, Eui-Jik Kim, and Taeshik Shon. "Monitoring Agent for Detecting Malicious Packet Drops for Wireless Sensor Networks in the Microgrid and Grid-Enabled Vehicles." International Journal of Advanced Robotic Systems 9, no. 1 (January 1, 2012): 31. http://dx.doi.org/10.5772/50256.

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Of the range of wireless communication technologies, wireless sensor networks (WSN) will be one of the most appropriate technologies for the Microgrid and Grid-enabled Vehicles in the Smartgrid. To ensure the security of WSN, the detection of attacks is more efficient than their prevention because of the lack of computing power. Malicious packet drops are the easiest means of attacking WSNs. Thus, the sensors used for constructing a WSN require a packet drop monitoring agent, such as Watchdog. However, Watchdog has a partial drop problem such that an attacker can manipulate the packet dropping rate below the minimum misbehaviour monitoring threshold. Furthermore, Watchdog does not consider real traffic situations, such as congestion and collision, and so it has no way of recognizing whether a packet drop is due to a real attack or network congestion. In this paper, we propose a malicious packet drop monitoring agent, which considers traffic conditions. We used the actual traffic volume on neighbouring nodes and the drop rate while monitoring a sending node for specific period. It is more effective in real network scenarios because unlike Watchdog it considers the actual traffic, which only uses the Pathrater. Moreover, our proposed method does not require authentication, packet encryption or detection packets. Thus, there is a lower likelihood of detection failure due to packet spoofing, Man-In-the Middle attacks or Wormhole attacks. To test the suitability of our proposed concept for a series of network scenarios, we divided the simulations into three types: one attack node, more than one attack nodes and no attack nodes. The results of the simulations meet our expectations.

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Vijayarajeswari, R., A. Rajivkannan, and J. Santhosh. "An Efficient Approach for Malicious Node Detection in Wireless Sensor Networks Using Classifier." Asian Journal of Research in Social Sciences and Humanities 6, no. 11 (2016): 81. http://dx.doi.org/10.5958/2249-7315.2016.01177.1.

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Zawaideh, Firas, and Muhammed Salamah. "An efficient weighted trust-based malicious node detection scheme for wireless sensor networks." International Journal of Communication Systems 32, no. 3 (December 14, 2018): e3878. http://dx.doi.org/10.1002/dac.3878.

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Sharmila, S., and G. Umamaheswari. "An Efficient Energy based Detection of Malicious Node in Mobile Wireless Sensor Networks." Journal of The Institution of Engineers (India): Series B 93, no. 1 (March 2012): 25–30. http://dx.doi.org/10.1007/s40031-012-0004-1.

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Ram Prabha, V., and P. Latha. "Enhanced multi-attribute trust protocol for malicious node detection in wireless sensor networks." Sādhanā 42, no. 2 (January 13, 2017): 143–51. http://dx.doi.org/10.1007/s12046-016-0588-2.

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Gupta, Radhika, Dr Sahil Verma, Dr Kavita, and Anup Ial Yadav. "A Comparative Analysis of Trust Based Applications in Wireless Sensor Networks." International Journal of Engineering & Technology 7, no. 4.12 (October 4, 2018): 73. http://dx.doi.org/10.14419/ijet.v7i4.12.20996.

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The wireless sensor network is a type of ad hoc network which is vulnerable to security attacks, specifically insider attacks. In spite of the fact that confidentiality, integrity and authentication helps in forestalling the particular sort of attacks, but they come at an expense. A traditional and evergreen concept of the trust evaluation and management, among the nodes of a network, for communication is a good and effective security measure. Overseeing trust in a distributed wireless sensor network is a challenging task when coordinated effort or participation is must in accomplishing mission and framework objectives. The paper represents a survey of various trust applications which are very helpful for carrying out a secure data transmission in a sensor network. The analysed trust applications malicious attack detection, secure data aggregation, secure node selection and secure routing.

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Ying, Bishan. "CUSUM-Based Intrusion Detection Mechanism for Wireless Sensor Networks." Journal of Electrical and Computer Engineering 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/245938.

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The nature of wireless sensor networks (WSNs) makes them very vulnerable to adversary's malicious attacks. Therefore, network security is an important issue to WSNs. Due to the constraints of WSN, intrusion detection in WSNs is a challengeable task. In this paper, we present a novel intrusion detection mechanism for WSNs, which is composed of a secure data communication algorithm and an intrusion detection algorithm. The major contribution of this paper is that we propose an original secure mechanism to defend WSNs against malicious attacks by using the information generated during data communication. The approach is able to protect the data communication in a WSN even if some sensor nodes are compromised by adversary. The proposed approach is easy to be implemented and performed in resource-constrained WSN. We also evaluate the proposed approach by a simulation experiment and analyze the simulation results in detail.

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Kaur, Karamjeet, and Gianetan Singh Sekhon. "Security based on Received Signal Strength in Localization for Underwater Sensor Networks." Circulation in Computer Science 1, no. 2 (November 24, 2016): 1–7. http://dx.doi.org/10.22632/ccs-2016-251-31.

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Underwater sensor networks are envisioned to enable a broad category of underwater applications such as pollution tracking, offshore exploration, and oil spilling. Such applications require precise location information as otherwise the sensed data might be meaningless. On the other hand, security critical issue as underwater sensor networks are typically deployed in harsh environments. Localization is one of the latest research subjects in UWSNs since many useful applying UWSNs, e.g., event detecting. Now day’s large number of localization methods arrived for UWSNs. However, few of them take place stability or security criteria. In purposed work taking up localization in underwater such that various wireless sensor nodes get localize to each other. RSS based localization technique used remove malicious nodes from the communication intermediate node list based on RSS threshold value. Purposed algorithm improves more throughput and less end to end delay without degrading energy dissipation at each node. The simulation is conducted in MATLAB and it suggests optimal result as comparison of end to end delay with and without malicious node.

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

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