A Data Clustering Algorithm for Detecting Selective Forwarding Attack in Cluster-Based Wireless Sensor Networks

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.

[1]  Yuanming Wu.  An energy-balanced loop-free routing protocol for distributed wireless sensor networks , 2017, Int. J. Sens. Networks.

[2]  Ronnie Johansson,et al.  Choosing DBSCAN Parameters Automatically using Differential Evolution , 2014 .

[3]  Hong Wang,et al.  Shared-nearest-neighbor-based clustering by fast search and find of density peaks , 2018, Inf. Sci..

[4]  Cheng Wang,et al.  A fast clustering algorithm based on pruning unnecessary distance computations in DBSCAN for high-dimensional data , 2018, Pattern Recognit..

[5]  H. T. Mouftah,et al.  Mitigating False Negative intruder decisions in WSN-based Smart Grid monitoring , 2017, 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC).

[6]  Zhuo Lu,et al.  Queuing the trust: Secure backpressure algorithm against insider threats in wireless networks , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[7]  Inderjit S. Dhillon,et al.  Refining clusters in high dimensional text data , 2003 .

[8]  Peng Liu,et al.  VDBSCAN: Varied Density Based Spatial Clustering of Applications with Noise , 2007, 2007 International Conference on Service Systems and Service Management.

[9]  Yuanming Wu,et al.  Detection of Insider Selective Forwarding Attack Based on Monitor Node and Trust Mechanism in WSN , 2014 .

[10]  Gong Shufeng Zhang Yanfeng,et al.  EDDPC: An Efficient Distributed Density Peaks Clustering Algorithm , 2016 .

[11]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[12]  Abhijit Das,et al.  An algorithm to detect malicious nodes in wireless sensor network using enhanced LEACH protocol , 2015, 2015 International Conference on Advances in Computer Engineering and Applications.

[13]  Aidong Zhang,et al.  An adaptive density-based clustering algorithm for spatial database with noise , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[14]  Hai Zhou,et al.  A Security Mechanism for Cluster-Based WSN against Selective Forwarding , 2016, Sensors.

[15]  Peter J. Rousseeuw,et al.  Clustering by means of medoids , 1987 .

[16]  Yuanming Wu,et al.  A Key Pre-distribution Scheme based on Sub-regions for Multi-Hop Wireless Sensor Networks , 2019, Wirel. Pers. Commun..

[17]  Guangzhong Xie,et al.  A novel stable selection and reliable transmission protocol for clustered heterogeneous wireless sensor networks , 2010, Comput. Commun..

[18]  Yuanming Wu,et al.  The Diffusion Clustering Scheme and Hybrid Energy Balanced Routing Protocol (DCRP) in Multi-hop Wireless Sensor Networks , 2019, Ad Hoc Sens. Wirel. Networks.

[19]  C. Karlof,et al.  Secure routing in wireless sensor networks: attacks and countermeasures , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..

[20]  Hae-Sang Park,et al.  A simple and fast algorithm for K-medoids clustering , 2009, Expert Syst. Appl..

[21]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[22]  Gang Qu,et al.  Insider Threats against Trust Mechanism with Watchdog and Defending Approaches in Wireless Sensor Networks , 2012, 2012 IEEE Symposium on Security and Privacy Workshops.

[23]  Simon Fong,et al.  DBSCAN: Past, present and future , 2014, The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014).

[24]  Ravi Sankar,et al.  A Survey of Intrusion Detection Systems in Wireless Sensor Networks , 2014, IEEE Communications Surveys & Tutorials.

[25]  Karl N. Levitt,et al.  A specification-based intrusion detection system for AODV , 2003, SASN '03.

[26]  Mohammad Zulkernine,et al.  Random-Forests-Based Network Intrusion Detection Systems , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[27]  Pavel Berkhin,et al.  A Survey of Clustering Data Mining Techniques , 2006, Grouping Multidimensional Data.

[28]  Sean Hughes,et al.  Clustering by Fast Search and Find of Density Peaks , 2016 .

[29]  Nikita V. Mahajan,et al.  Performance Evaluation of Wireless Sensor Network under Black Hole Attack , 2014 .

[30]  Erkki Oja,et al.  Rival penalized competitive learning for clustering analysis, RBF net, and curve detection , 1993, IEEE Trans. Neural Networks.

[31]  Yan Lindsay Sun,et al.  Trust Management for Defending On-Off Attacks , 2015, IEEE Transactions on Parallel and Distributed Systems.

[32]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[33]  D.K. Bhattacharyya,et al.  An improved sampling-based DBSCAN for large spatial databases , 2004, International Conference on Intelligent Sensing and Information Processing, 2004. Proceedings of.

[34]  Yasuhisa Takizawa,et al.  Cooperative detection for falsification and isolation of malicious nodes for wireless sensor networks in open environment , 2017, 2017 IEEE Asia Pacific Microwave Conference (APMC).

[35]  Younghwan Yoo,et al.  A security framework for Cluster-based Wireless Sensor Networks against the selfishness problem , 2015, 2015 Seventh International Conference on Ubiquitous and Future Networks.

[36]  Ge Yu,et al.  Efficient Distributed Density Peaks for Clustering Large Data Sets in MapReduce , 2016, IEEE Transactions on Knowledge and Data Engineering.

[37]  Jiong Yang,et al.  STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[38]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[39]  Mary Baker,et al.  Mitigating routing misbehavior in mobile ad hoc networks , 2000, MobiCom '00.

[40]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[41]  Yufei Tao,et al.  DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation , 2015, SIGMOD Conference.