An octopus-inspired intrusion deterrence model in distributed computing system

• Autorzy: Olajubu E. A. , Akinwale A. , Ogundoyin K. I.

Identyfikatory

DOI

10.7494/csci.2016.17.4.483

• Języki publikacji: EN

Abstrakty

EN

The study formulated and evaluated a model for effective management of ma- licious nodes in mobile Ad-hoc network based on Ad-Hoc on- demand distance vector routing protocol. A collaborative injection model called Collaborative Injection Deterrence Model (CIDM) was formulated using stochastic theory. The definition of the model was presented using graph theory. CIDM was simulated using three different scenarios. The three scenarios were then compared using packets delivery ratio (PDR), routing load, throughput and delay as performance metrics. The simulation result showed that CIDM reduce considerably the rate of packets dropped caused by malicious nodes in MANET network. CIDM did not introduce additional load to the network and, yet produce higher throughput. Lastly, the access delay in CIDM is minimal compared with convectional OADV. The study developed a model to mete out a punitive measure to rogue nodes as a form of intrusion deterrence without degrading the overall performance of the network. The well known CRAWDAD dataset was used in the simulation.

Słowa kluczowe

EN

CIDM; MANET; intrusion detection; mobility; routing load; throughput; 1-hop neighbors; wireless; malicious; injection

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2016
• Tom: Vol. 17 (4)
• Strony: 483--501
• Opis fizyczny: Bibliogr. 33 poz., rys., wykr., tab.

Twórcy

autor

Olajubu E. A.

• Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

autor

Akinwale A.

• Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

autor

Ogundoyin K. I.

• Department of Computer Science and Engineering, Obafemi Awolowo University, Ile-Ife, Nigeria

Bibliografia

• [1] Agah A., Das S.K., Basu K., Asadi M.: Intrusion detection in sensor networks: a non-cooperative game approach. In: Network Computing and Applications, 2004. (NCA 2004). Proceedings. Third IEEE International Symposium on , pp. 343–346, 2004.
• [2] Atassi A., Sayegh N., Elhajj I., Chehab A., Kayssi A.: Malicious Node Detection in Wireless Sensor Networks. In: Advanced Information Networking and Applications Workshops (WAINA), 2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA) , pp. 456–461, 2013.
• [3] Balajinath B., Raghavan S.V.: Intrusion Detection through Learning Model. Computer Communications , vol. 24(12), pp. 1202–1212, 2001.
• [4] Bayne C.J.: Molluscan internal defense mechanism: The fate of C 14 -labelled bacteria in the land snail Helix pomatia (L.). Journal of comparative physiology , vol. 86(1), pp. 17–25, 1973, http://dx.doi.org/10.1007/BF00694474 .
• [5] Boping Q., Xianwei Z., Jun Y., Cunyi S.: Grey-theory based intrusion detection model. Journal of Systems Engineering and Electronics , vol. 17(1), pp. 230–235, 2006.
• [6] Boukerche A., Machado R.B., Juca K.R., Sobral J.B.M., Notare M.S.: An agent based and biological inspired real-time intrusion detection and security model for computer network operations. Computer Communications , vol. 30(13), pp. 2649– 2660, 2007.
• [7] Cai J., Poch U.: Allocate Fair Payoff for Cooperation in Wireless and Ad-hoc Networks Using Shapley Value. In: Proceedings of the 18th International Parallel and Distributed Processing Symposium, IEEE , pp. 219–226, 2004.
• [8] Farrahi V.S., Ahmadzadeh M.: KCMC: a hybrid Learning Approach for Network Intrusion Detection using K-means Clustering and Multiple Classiffiers. International Journal of Computer Applications , vol. 124(9), pp. 18–23, 2015.
• [9] Geers K.: The challenge of cyber attack deterrence. Computer Law and Security Review , vol. 26(3), pp. 298–303, 2010.
• [10] Gray R.S., Kotz D., Newport C., Dubrovsky N., Fiske A., Liu J., Masone C., McGrath S., Yuan Y.: CRAWDAD data set dartmouth/outdoor. http: //crawdad.org .
• [11] Gupt G.K., Singh J.: Truth of D-DoS Attacks in MANET. Global Journal of Computer Science and Technology , vol. 10(15), pp. 15–22, 2010.
• [12] Harper J.G.: Traffic violation detection and deterrence: implications for automatic policing. Applied Ergonomics , vol. 22(3), pp. 189–197, 1991.
• [13] Hoath P., Mulhall T.: Hacking: Motivation and deterrence, part II. Computer Fraud and Security , vol. 5, pp. 17–19, 1998.
• [14] Kendall J.R.: Deterrence by Presence to Effective Response: Japan’s Shift South- ward. Orbis , vol. 54(4), pp. 603–614, 2010, http://www.sciencedirect.com/ science/article/pii/S0030438710000463 .
• [15] Kodialam M., Lakshman T.V.: Detecting network intrusions via sampling: a game theoretic approach. In: INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications. IEEE Societies , vol. 3, pp. 1880–1889, 2003.
• [16] Kolias C., Kambourakis G., Maragoudakis M.: Swarm intelligence in intrusion detection: A survey. Computers and Security , vol. 30(8), pp. 625–642, 2011.
• [17] Larsson T., Hedman N.: Routing protocols in wireless ad-hoc networks – a simulation study. Lulea University of Technology , 1998.
• [18] Liu Y., Comaniciu C., Man H.: A Bayesian Game Approach for Intrusion Detection in Wireless Ad Hoc Networks. In: Proceeding from the 2006 Workshop on Game Theory for Communications and Networks , GameNets ’06, ACM, New York, NY, USA, 2006, http://doi.acm.org/10.1145/1190195.1190198 .
• [19] Lui K., Dang J.V.P.K., Balakrishan K.: An Acknowledgement Based Approach for the Detection of Routing Misbehaviour in MANETs. IEEE Transactions on Mobile Computing , vol. 6(5), pp. 536–550, 2007.
• [20] Mishra A., Nadkarni K., Patcha A.: Intrusion Detection in Wireless Ad-hoc Networks. IEEE Wireless Communication , vol. 11(1), pp. 48–60, 2004.
• [21] Ning P., Cui Y., Reeves D.S.: Constructing Attack Scenarios Through Correlation of Intrusion Alerts. In: Proceedings of the 9th ACM Conference on Computer and Communications Security , CCS ’02, pp. 245–254, ACM, New York, NY, USA, 2002, http://doi.acm.org/10.1145/586110.586144 .
• [22] Obaidat M.S., Woungang I.: Pervasive Computing and Networking. John Wiley and Sons, 2011.
• [23] Orfila A., Carbo J., Ribagorda A.: Autonomous Decision on Intrusion Detection with Trained BDI Agents. Computer Communications , vol. 31(9), pp. 1803–1813, 2008.
• [24] Polla M.L., Martinelli F., Sgandurra D.: A Survey on Security for Mobile Devices. IEEE Communications Surveys Tutorials , vol. 15(1), pp. 446–471, 2013.
• [25] Saxena H., Richariya V.: Intrusion Detection in KDD99 Dataset Using SVM-PSO and Feature Reduction with Information Gain. International Journal of Computer Applications , vol. 98(6), pp. 25–29, 2014.
• [26] Senbel S.A., Ibrahim A., Zaki E.A.: Solution to Black Hole Attack in Ad Hoc on Demand Distance Vector Routing Protocol. Journal of Computer Sciences and Applications, vol. 3(4), pp. 90–93, 2015.
• [27] Senthilnayaki B., Venkatalashmi K., Kannan A.: Intrusion Detection System us- ing Feature Selection and Classification Techique. International Journal of Computer Science and Application (IJCSA) , vol. 3(4), pp. 145–151, 2014.
• [28] Shakshuki M., Kang N., Sheltami T.R.: EAAK – A Secure Intrusion-Detection System for MANETs. IEEE Transactions on Industrial Electronics, vol. 60(3), pp. 1039–1098, 2013.
• [29] Shamshirband S., Anuar N.B., Kiah L.M., Patel A.: An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique.Engineering Applications of Artificial Intelligence, vol. 26(9), pp. 2105–2127, 2013.
• [30] Subba B., Biswas S., Karmakar S.: Intrusion detection in Mobile Ad-hoc Net- works: Bayesian game formulation. Engineering Science and Technology an International Journal, vol. 19, pp. 782–799, 2016.
• [31] Thaseen I.S., Kumar C.A.: Intrusion detection model using fusion of chi-square feature selection and multi class { SVM } . Journal of King Saud University – Computer and Information Sciences , 2016, http://www.sciencedirect.com/ science/article/pii/S1319157816300076.
• [32] Wang Q., Borisov N.: Octopus: A Secure and Anonymous DHT Lookup. In: Distributed Computing Systems (ICDCS), 2012 IEEE 32nd International Conference on Distributed Computing Systems (ICDCS) , pp. 325–334, 2012.
• [33] Wang W., Guan X., Zhang X.: Processing of massive audit data streams for real-time anomaly intrusion detection. Computer Communications, vol. 31(1), pp. 58–72, 2008.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.