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Wireless Personal Communications
Erschienen in: Wireless Personal Communications 3/2020

25.11.2019

A Novel SVM Based IDS for Distributed Denial of Sleep Strike in Wireless Sensor Networks

verfasst von: Noor Mohd, Annapurna Singh, H. S. Bhadauria

Erschienen in: Wireless Personal Communications | Ausgabe 3/2020

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Abstract

It is well known that the safety, reliability and energy efficiencies are the prime concerns while designing and deploying wireless sensor networks. Therefore, unattained features and inability to recharge the nodes makes wireless sensor networks more susceptible to attack. To successfully deploy efficient usage of power resources, sensor networks from time to time keep nodes to sleep. They awake when some activity is noticed. MAC protocols playa very crucial role to save the power consumption during communication. Attacker is not adhering the communication protocols by taking the advantage of unattended scenario. The communication protocols are designed in such a manner that they are assuring the optimal consumption of battery or other power sources. By indulging the nodes in unnecessary communication adversary applies the repudiation of sleep attack by applying repudiation of service attack. This work implements the effect of denial of service attack which leads to the denial of sleep attack in wireless sensor networks using support vector machine learning. To achieve better result, model is developed and tested with the help of various kernel functions (linear, sigmoidal and redial functions). After the intensive execution of experiments, final outcomes have been drawn. On the basis of achieved results and validation of model we can conclude that the developed system performs outstanding throughput for detecting denial of sleep strike attack in WSN.
Vorheriger Artikel An Octagonal Shaped Monopole Antenna for UWB Applications with Band Notch Characteristics
Nächster Artikel Integrated 4G/5G Multiservice MIMO Antenna for Hand-Held Devices

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Literatur
1.
Manju, V. C., Lekha, S. S., & Kumar, M. S. (2013). Mechanisms for detecting and preventing denial of sleep attacks on wireless sensor networks. In 2013 IEEE conference on information & communication technologies (pp. 74–77). IEEE.
2.
Raymond, D. R., Marchany, R. C., Brownfield, M. I., & Midkiff, S. F. (2008). Effects of denial-of-sleep attacks on wireless sensor network MAC protocols. IEEE Transactions on Vehicular Technology,58(1), 367–380.CrossRef
3.
Frunza, M., & Scripcariu, L. (2007). Improved RSA encryption algorithm for increased security of wireless networks. In 2007 International symposium on signals, circuits and systems (Vol. 2, pp. 1–4). IEEE.
4.
Udgata, S. K., Mubeen, A., & Sabat, S. L. (2011). Wireless sensor network security model using zero knowledge protocol. In 2011 IEEE international conference on communications (ICC) (pp. 1–5). IEEE.
5.
Noroozi, E., & Kadivar, J. (2010). Energy analysis for wireless sensor networks. In 2010 2nd international conference on mechanical and electronics engineering (Vol. 2, pp. V2–382). IEEE.
6.
Raymond, D. R., & Midkiff, S. F. (2007). Clustered adaptive rate limiting: Defeating denial-of-sleep attacks in wireless sensor networks. In MILCOM 2007-IEEE military communications conference (pp. 1–7). IEEE.
7.
Vasserman, E. Y., & Hopper, N. (2011). Vampire attacks: Draining life from wireless ad hoc sensor networks. IEEE Transactions on Mobile Computing,12(2), 318–332.CrossRef
8.
Hu, Y. C., Perrig, A., & Johnson, D. B. (2003). Packet leashes: a defense against wormhole attacks in wireless networks. In IEEE INFOCOM 2003. Twenty-second annual joint conference of the ieee computer and communications societies (IEEE Cat. No. 03CH37428) (Vol. 3, pp. 1976–1986). IEEE.
9.
Peres, M., Chalouf, M. A., & Krief, F. (2011). On optimizing energy consumption: An adaptative authentication level in wireless sensor networks. In Global information infrastructure symposium-GIIS 2011 (pp. 1–8). IEEE.
10.
Hsueh, C. T., Wen, C. Y., & Ouyang, Y. C. (2015). A secure scheme against power exhausting attacks in hierarchical wireless sensor networks. IEEE Sensors Journal,15(6), 3590–3602.CrossRef
11.
Ebinger, P., & Bißmeyer, N. (2009). TEREC: Trust evaluation and reputation exchange for cooperative intrusion detection in MANETs. In 2009 seventh annual communication networks and services research conference (pp. 378–385). IEEE.
12.
Zheng, S., & Baras, J. S. (2011). Trust-assisted anomaly detection and localization in wireless sensor networks. In 2011 8th annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks (pp. 386–394). IEEE.
13.
Mao, Y. (2010). A semantic-based intrusion detection framework for wireless sensor network. In INC2010: 6th international conference on networked computing (pp. 1–5). IEEE.
14.
Misra, S., Krishna, P. V., & Abraham, K. I. (2010). Energy efficient learning solution for intrusion detection in wireless sensor networks. In 2010 2nd international conference on communication systems and networks (COMSNETS 2010) (pp. 1–6). IEEE.
15.
Crosby, G. V., Hester, L., & Pissinou, N. (2011). Location-aware, trust-based detection and isolation of compromised nodes in wireless sensor networks. IJ Network Security,12(2), 107–117.
16.
Chen, R. C., Hsieh, C. F., & Huang, Y. F. (2010). An isolation intrusion detection system for hierarchical wireless sensor networks. JNW,5(3), 335–342.
17.
Chen, R. C., Haung, Y. F., & Hsieh, C. F. (2010). Ranger intrusion detection system for wireless sensor networks with Sybil attack based on ontology. In New aspects of applied informatics, biomedical electronics and informatics and communications.
18.
Mamun, M. S. I., & Kabir, A. S. (2010). Hierarchical design based intrusion detection system for wireless ad hoc sensor network. International Journal of Network Security & Its Applications (IJNSA),2(3), 102–117.CrossRef
19.
Yan, K. Q., Wang, S. C., & Liu, C. W. (2009). A hybrid intrusion detection system of cluster-based wireless sensor networks. In Proceedings of the international multiconference of engineers and computer scientists (Vol. 1, pp. 18–20).
20.
Atakli, I. M., Hu, H., Chen, Y., Ku, W. S., & Su, Z. (2008). Malicious node detection in wireless sensor networks using weighted trust evaluation. In Proceedings of the 2008 Spring simulation multiconference (pp. 836–843). Society for Computer Simulation International.
21.
Huo, G., & Wang, X. (2008). DIDS: A dynamic model of intrusion detection system in wireless sensor networks. In 2008 international conference on information and automation (pp. 374–378). IEEE.
22.
Techateerawat, P., & Jennings, A. (2006). Energy efficiency of intrusion detection systems in wireless sensor networks. In 2006 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology workshops (pp. 227–230). IEEE.
23.
Zhang, Y., & Lee, W. (2000). Intrusion detection in wireless ad-hoc networks. In Proceedings of the 6th annual international conference on mobile computing and networking (pp. 275–283). ACM.
24.
Bhargava, S., & Agrawal, D. P. (2001). Security enhancements in AODV protocol for wireless ad hoc networks. In IEEE 54th vehicular technology conference. VTC Fall 2001. Proceedings (Cat. No. 01CH37211) (Vol. 4, pp. 2143–2147). IEEE.
25.
Huang, Y. A., & Lee, W. (2003). A cooperative intrusion detection system for ad hoc networks. In Proceedings of the 1st ACM workshop on security of ad hoc and sensor networks (pp. 135–147). ACM.
26.
Hamid, Y., Shah, F. A., & Sugumaran, M. (2019). Wavelet neural network model for network intrusion detection system. International Journal of Information Technology,11(2), 251–263.CrossRef
27.
Song, C., & Zhang, Q. (2009). OMH–suppressing selfish behavior in ad hoc networks with one more hop. Mobile Networks and Applications,14(2), 178–187.CrossRef
28.
Yan, Z., Zhang, P., & Virtanen, T. (2003). Trust evaluation based security solution in ad hoc networks. In Proceedings of the 7th Nordic workshop on secure IT systems (Vol. 14).
29.
Razak, S. A., Furnell, S., Clarke, N., & Brooke, P. (2006). A two-tier intrusion detection system for mobile ad hoc networks–a friend approach. In International conference on intelligence and security informatics (pp. 590–595). Springer, Berlin.
30.
Rebahi, Y., Mujica-V, V. E., & Sisalem, D. (2005). A reputation-based trust mechanism for ad hoc networks. In 10th IEEE symposium on computers and communications (ISCC’05) (pp. 37–42). IEEE.
31.
Razak, S. A., Furnell, S. M., Clarke, N. L., & Brooke, P. J. (2008). Friend-assisted intrusion detection and response mechanisms for mobile ad hoc networks. Ad Hoc Networks,6(7), 1151–1167.CrossRef
32.
Eschenauer, L., Gligor, V. D., & Baras, J. (2002). On trust establishment in mobile ad-hoc networks. In International workshop on security protocols (pp. 47–66). Springer, Berlin.
33.
Razak, S. A., Samian, N., & Maarof, M. A. (2008). A friend mechanism for mobile ad hoc networks. In 2008 The 4th international conference on information assurance and security (pp. 243–248). IEEE.
34.
Mundinger, J., & Le Boudec, J. Y. (2008). Analysis of a reputation system for mobile ad-hoc networks with liars. Performance Evaluation,65(3–4), 212–226.CrossRef
35.
Agarwal, M., Purwar, S., Biswas, S., & Nandi, S. (2016). Intrusion detection system for PS-Poll DoS attack in 802.11 networks using real time discrete event system. IEEE/CAA Journal of Automatica Sinica,4(4), 792–808.CrossRefMathSciNet
36.
Shen, L., Ma, J., Liu, X., Wei, F., & Miao, M. (2016). A secure and efficient id-based aggregate signature scheme for wireless sensor networks. IEEE Internet of Things Journal,4(2), 546–554.CrossRef
37.
Michalski, R. S., Bratko, I., & Bratko, A. (1998). Machine learning and data mining; methods and applications. New York: Wiley.
38.
Tsai, C. F., Hsu, Y. F., Lin, C. Y., & Lin, W. Y. (2009). Intrusion detection by machine learning: A review. Expert Systems with Applications,36(10), 11994–12000.CrossRef
39.
Theodoridis, S., & Koutroumbas, K. (2006). Pattern recognition (3rd ed.). Orlando, FL: Academic Press Inc.MATH
40.
Mitchell, T. M. (1997). Does machine learning really work? AI Magazine,18(3), 11.MathSciNet
41.
Manocha, S., & Girolami, M. A. (2007). An empirical analysis of the probabilistic K-nearest neighbour classifier. Pattern Recognition Letters,28(13), 1818–1824.CrossRef
42.
Hassoun, M. H. (1996). Fundamentals of artificial neural networks. In Proceedings of the IEEE, New York.
43.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks,10(5), 988–999.CrossRef
44.
Maglaras, L. A. (2015). A novel distributed intrusion detection system for vehicular ad hoc networks. International Journal of Advanced Computer Science and Applications (IJACSA),6(4), 101–106.
45.
Tiwari, A. (2019). Real-time intrusion detection system using computational intelligence and neural network: Review, analysis and anticipated solution of machine learning. In Information technology and applied mathematics (pp. 153–161). Singapore: Springer.
46.
Narudin, F. A., Feizollah, A., Anuar, N. B., & Gani, A. (2016). Evaluation of machine learning classifiers for mobile malware detection. Soft Computing,20(1), 343–357.CrossRef
47.
Mao, Q., Hu, F., & Hao, Q. (2018). Deep learning for intelligent wireless networks: A comprehensive survey. IEEE Communications Surveys & Tutorials,20(4), 2595–2621.CrossRef
48.
Al-Qatf, M., Lasheng, Y., Al-Habib, M., & Al-Sabahi, K. (2018). Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. IEEE Access,6, 52843–52856.CrossRef
49.
Kim, J., Kim, J., Thu, H. L. T., & Kim, H. (2016). Long short term memory recurrent neural network classifier for intrusion detection. In 2016 international conference on platform technology and service (PlatCon) (pp. 1–5). IEEE.
50.
Mukkamala, S., & Sung, A. H. (2003). Feature selection for intrusion detection with neural networks and support vector machines. Transportation Research Record,1822(1), 33–39.CrossRef
51.
Mukkamala, S., Sung, A. H., & Abraham, A. (2005). Intrusion detection using an ensemble of intelligent paradigms. Journal of Network and Computer Applications,28(2), 167–182.CrossRef
52.
Ganyun, L. V., Haozhong, C., Haibao, Z., & Lixin, D. (2005). Fault diagnosis of power transformer based on multi-layer SVM classifier. Electric Power Systems Research,74(1), 1–7.CrossRef
53.
Çomak, E., Arslan, A., & Türkoğlu, İ. (2007). A decision support system based on support vector machines for diagnosis of the heart valve diseases. Computers in Biology and Medicine,37(1), 21–27.CrossRef
54.
Zhou, S. M., & Gan, J. Q. (2007). Constructing L2-SVM-based fuzzy classifiers in high-dimensional space with automatic model selection and fuzzy rule ranking. IEEE Transactions on Fuzzy Systems,15(3), 398–409.CrossRef
55.
Chiang, J. H., & Hao, P. Y. (2004). Support vector learning mechanism for fuzzy rule-based modeling: A new approach. IEEE Transactions on Fuzzy Systems,12(1), 1–12.CrossRef
56.
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning,20(3), 273–297.MATH
57.
Shamsuddin, S. B., & Woodward, M. E. (2008). Applying knowledge discovery in database techniques in modeling packet header anomaly intrusion detection systems. Journal of Software,3(9), 68–76.CrossRef
58.
Lazarevic, A., Ertoz, L., Kumar, V., Ozgur, A., & Srivastava, J. (2003). A comparative study of anomaly detection schemes in network intrusion detection. In Proceedings of the 2003 SIAM international conference on data mining (pp. 25–36). Society for Industrial and Applied Mathematics.
59.
Wang, X., Wong, J. S., Stanley, F., & Basu, S. (2009). Cross-layer based anomaly detection in wireless mesh networks. In 2009 ninth annual international symposium on applications and the internet (pp. 9–15). IEEE.
Metadaten
Titel
A Novel SVM Based IDS for Distributed Denial of Sleep Strike in Wireless Sensor Networks
verfasst von
Noor Mohd
Annapurna Singh
H. S. Bhadauria
Publikationsdatum
25.11.2019
Verlag
Springer US
Erschienen in
Wireless Personal Communications / Ausgabe 3/2020
Print ISSN: 0929-6212
Elektronische ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-019-06969-9

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