A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

[1]  Christopher Leckie,et al.  High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning , 2016, Pattern Recognit..

[2]  Albert Y. Zomaya,et al.  DLS: A dynamic local stitching mechanism to rectify transmitting path fragments in wireless sensor networks , 2013, J. Netw. Comput. Appl..

[3]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[4]  D. Morganstein.  Practical Sampling Techniques , 1999 .

[5]  Ling Zhuang,et al.  Parameter Optimization of Kernel-based One-class Classifier on Imbalance Learning , 2006, J. Comput..

[6]  Luca Salgarelli,et al.  Support Vector Machines for TCP traffic classification , 2009, Comput. Networks.

[7]  L. Nachman,et al.  PIPENET: A Wireless Sensor Network for Pipeline Monitoring , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[8]  J. Alex Halderman,et al.  Green Lights Forever: Analyzing the Security of Traffic Infrastructure , 2014, WOOT.

[9]  Chih-Jen Lin,et al.  Training v-Support Vector Classifiers: Theory and Algorithms , 2001, Neural Computation.

[10]  David R. Morganstein,et al.  Practical Sampling Techniques , 1999, Technometrics.

[11]  Doohwan Oh,et al.  A Distributed Signature Detection Method for Detecting Intrusions in Sensor Systems , 2013, Sensors.

[12]  Andrew Y. Ng,et al.  Parsing Natural Scenes and Natural Language with Recursive Neural Networks , 2011, ICML.

[13]  Y.A. Sekercioglu,et al.  Detecting Selective Forwarding Attacks in Wireless Sensor Networks using Support Vector Machines , 2007, 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information.

[14]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[15]  Ronen Feldman,et al.  The Data Mining and Knowledge Discovery Handbook , 2005 .

[16]  Yunqian Ma,et al.  Practical selection of SVM parameters and noise estimation for SVM regression , 2004, Neural Networks.

[17]  Athanassios Boulis,et al.  Performance and scalability evaluation of the Castalia wireless sensor network simulator , 2010, SimuTools.

[18]  Qin Yu,et al.  An Improved ARIMA-Based Traffic Anomaly Detection Algorithm for Wireless Sensor Networks , 2016, Int. J. Distributed Sens. Networks.

[19]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[20]  Denise Dudek.  Lightweight Anomaly Detection for Wireless Sensor Networks , 2015 .

[21]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[22]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[23]  Joan Arnedo-Moreno,et al.  ZigBee/ZigBee PRO Security Assessment Based on Compromised Cryptographic Keys , 2010, 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[24]  Yacine Challal,et al.  Secure and efficient disjoint multipath construction for fault tolerant routing in wireless sensor networks , 2011, J. Netw. Comput. Appl..

[25]  Rosli Salleh,et al.  Overview of Security Issues in Wireless Sensor Networks , 2011, 2011 Third International Conference on Computational Intelligence, Modelling & Simulation.

[26]  Koen Langendoen,et al.  An adaptive energy-efficient MAC protocol for wireless sensor networks , 2003, SenSys '03.

[27]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[28]  Lior Rokach,et al.  Data Mining And Knowledge Discovery Handbook , 2005 .

[29]  Fang Liu,et al.  Insider Attacker Detection in Wireless Sensor Networks , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.

[30]  Adam Dunkels,et al.  Cross-Level Sensor Network Simulation with COOJA , 2006, Proceedings. 2006 31st IEEE Conference on Local Computer Networks.

[31]  Qi Qi,et al.  Wireless Sensor-Based Traffic Light Control , 2008, 2008 5th IEEE Consumer Communications and Networking Conference.

[32]  Biming Tian,et al.  Anomaly detection in wireless sensor networks: A survey , 2011, J. Netw. Comput. Appl..

[33]  Deborah Estrin,et al.  Advances in network simulation , 2000, Computer.

[34]  Caroline Petitjean,et al.  One class random forests , 2013, Pattern Recognit..

[35]  Ling Liu,et al.  Encyclopedia of Database Systems , 2009, Encyclopedia of Database Systems.

[36]  Kurt Hornik,et al.  Misc Functions of the Department of Statistics, ProbabilityTheory Group (Formerly: E1071), TU Wien , 2015 .

[37]  Y. Zhang,et al.  – 20 Statistics-based outlier detection for wireless sensor networks , 2012 .

[38]  Anant Kr. Jaiswal,et al.  A Novel Analysis of T Mac and S Mac Protocol for Wireless Sensor Networks Using Castalia , 2012 .

[39]  Saad B. Qaisar,et al.  Characteristics and classification of outlier detection techniques for wireless sensor networks in harsh environments: a survey , 2012, Artificial Intelligence Review.

[40]  Dae-Wha Seo,et al.  A Lightweight and Effective Jamming Detection in Electronic Shelf Label Systems , 2009, Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications.

[41]  M. Tahar Kechadi,et al.  CupCarbon: a multi-agent and discrete event wireless sensor network design and simulation tool , 2014, SimuTools.

[42]  Hans-Peter Kriegel,et al.  LOF: identifying density-based local outliers , 2000, SIGMOD '00.

[43]  Fionn Murtagh,et al.  Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? , 2011, Journal of Classification.

[44]  Daowei Liu,et al.  Anomaly Detection of Single Sensors Using OCSVM_KNN , 2015, BigCom.

[45]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[46]  Luís Torgo,et al.  Data Mining with R: Learning with Case Studies , 2010 .

[47]  Young-Joo Kim,et al.  SSL-Based Lightweight Security of IP-Based Wireless Sensor Networks , 2009, 2009 International Conference on Advanced Information Networking and Applications Workshops.

[48]  Jaideep Srivastava,et al.  A Comparative Study of Anomaly Detection Schemes in Network Intrusion Detection , 2003, SDM.

[49]  A. Varga,et al.  THE OMNET++ DISCRETE EVENT SIMULATION SYSTEM , 2003 .

[50]  Charalampos Konstantopoulos,et al.  Defending Wireless Sensor Networks from Jamming Attacks , 2007, 2007 IEEE 18th International Symposium on Personal, Indoor and Mobile Radio Communications.

[51]  T. Kavitha,et al.  Security Vulnerabilities In Wireless Sensor Networks: A Survey , 2010 .