Adopting Flocks of Birds Approach to Predator for Anomalies Detection on Industrial Control Systems
Industrial Control Systems (ICS) such as Supervisory Control And Data Acquisition (SCADA) can be seen in many different critical infrastructures, from nuclear management to utility, medical equipment, power, waste and engine management on ships and planes. The role SCADA plays in critical infrastructure has resulted in a call to secure them. Many lives depend on it for daily activities and the attack vectors are becoming more sophisticated. Hence, the security of ICS is vital as malfunction of it might result in huge risk. This paper describes how the application of Prey Predator (PP) approach in flocks of birds could enhance the detection of malicious activities on ICS. The PP approach explains how these animals in groups or flocks detect predators by following some simple rules. They are not necessarily very intelligent animals but their approach in solving complex issues such as detection through corporation, coordination and communication worth emulating. This paper will emulate flocking behavior seen in birds in detecting predators. The PP approach will adopt six nearest bird approach in detecting any predator. Their local and global bests are based on the individual detection as well as group detection. The PP algorithm was designed following MapReduce methodology that follows a Split Detection Convergence (SDC) approach.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125341Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 764
 R. McClanahan, “The Benefit of Networked SCADA Systems Utilizing IP-Enabled Networks”. Arkansas, USA, 2002.
 B. Schneier, “Liars and outliers: Enabling the Trust That Society Needs To Thrive”: Technological Advances, USA, John Wiley and Sons, 2012.
 R. Langner, “Stuxnet: Dissecting Cyberwarfare Weapon” IEEE Security & Privacy, Vol.9 (3), PP. 49-51, 2011.
 Sungard, “Big Data Challenges and Opportunities for the Energy Industry” 2015.
 K. Chakraborty, S. Jana, and T. Kar, “Global Dynamics and Bifurcation in a Stage Structured Prey-Predator Fishery Model With Harvesting": Applied mathematics and Computation, Vol.218 (18), 2012, PP. 9271-9290.
 K. Chakraborty, and T. Kar, “Effort Dynamics in a Prey-Predator Model with Harvesting”: International Journal of Information Systems Science, Vol 6 (3), 2010, PP.318-332
 C. Chen, and C. Hsui, “Fishery Policy Considering the Future Opportunity of Harvesting”: Mathematical Bioscience, vol. 207, 2007, PP. 138-160
 F. Zoratto, D. Santucci, and E, Alleva, “Theories Commonly Adopted to Explain The Anti-Predatory Benefits of the Group Life”: The Case of Starling (Sturnus Vulgaries). Rendiconti Lincei, 2009, PP.1-14.
 C. Lenzen, and T. Radeva, “The Power of Pheromones in Ant Foragin”, 1st Workshop on Biological Distributed Algorithm (BDA), 2013
 M. Derigo, and T. Stutzle, “Ant Colony Optimization. The MIT Press”, 2004.
 A. Gupta, O. J. Pandey, M. Shukla, A. Dadhich, A. Ingle, and V. Ambhore, “Intelligent Perpetual Echo Attack Detection on User Datagram Protocol Port 7 Using Ant Colony Optimization”. In Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on, 2014, pp. 419-424. IEEE.
 C. Tsang, and S. kwong, ‘Multi-Agent Intrusion Setection System in Industrial Network using Ant Colony Clustering Approach and Unsupervised Feature Extraction’. In IEEE International Conference on Industrial Technology, ICIT, 2005, pp.51-56.
 D. Karaboga, “An Idea Based On Honey Bee Swarm for Numerical Optimization”, Technical report-TR06, Erciyes University, Faculty of Computing and Engineering, 2005.
 P. Amudha, S. Karthik, and Sivakumari “A Hybrid Swarm Intelligence Algorithm for Intrusion Detection Using Significant Features”, Scientific World Journal, vol. 2015, 2015, PP.1-16.
 I. Couzin, J. Krause, R. James, G. Ruxton, and N. Franks, “Collective Memory and Spatial Sorting in Animal Froups”, 2002.
 W. Potts, “The chorus-line hypotheses of manoeuvre coordination in a avaian flocks”: Nature 309, 1984, pp. 344-345.
 A. Rosen, and M. Hedenstrom, “Predator Vesus Prey: On Aerial Hunting and Escape Strategies in Birds”, Oxford Journals, Behavioural Ecology, Vol. 12 (2), 2000, PP. 150-156.
 G. Powell, “Experimental Analysis of Social value of Flocking by Starlings (Sturnus Vulgaris) in Relation to Predation and Foraging", Amin Behav , 1974, 22:501-505.
 E. Fernandez-Juricic, S. Siller and A. Kacelnik “Flock Density, Social Foraging and Scanning” An Experiment With Starlings. Behav Ecol 15, 2004, PP. 371-379.
 E. Glueck “An Experimental Study of Feeding, Vigilance and Predator Avoidance in a Single Bird”. Oecologia, 1987, PP. 268-272.
 G. Martins, “The Eye of a Passeriform Bird, The European Starling Eye Movement Amplitude, Visual Fields and Schematics” Optics, J Comp Physiol A, 1986, PP. 545-557
 M. Delm, Vigilance for Predators: detection and Dilution Effects. Behavioural Ecology and Sociobiology, 1990, p. 337-342.
 H. Pomeroy, and F. Heppner, “Structure of Turning in Airborne Rock Dove (Columba Livia) Flocks”, The Auk 109 (2) 1992, PP.256-267.
 M. Ballerini, N. Calbibbo, R. Candeleir, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, and V. Zdravkovic. “Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study”. Proceedings of the National Academy of Sciences of the United States of America 105: 2008, 1232–1237.
 E. Lawlor, “Discover Nature Close to Home: Things to Know and Things to do”. STACKPOLE BOOKS, Harrisburg, 1993, PP.61.
 C. Devereux, M. Whittingham, E. Fernandez-Juricic, and J. Vickery, “Predator Detection and Avoidance by Starlings Under Differing Scenarios of Predation Risk”: Behaviour Ecology, 2005, PP. 303-309.
 A. Cavagna, A. Cimarelli, I. Giardina, G. Parisi, R. Santagati, F. Stefanini, and R. Tavarone, “From Emperical Data to Inter-Individual Interactions”: Unveiling the Rules of Collective Animal Behavior: Mathematical Models and Methods in Applied Science, Vol.20, 2010, PP.1491-1510.
 W. Bialek, et al “Statistical Mechanics for Natural Flocks of Birds, In Proceedings of the National Academy of Science of the United State of America”, PNAS, Vol. 109 (13) 2012, PP. 4786-4791.
 A. Ratnaparkhi, “A Simple Introduction to Maximum Entropy Models for Natural language Processing”: Institute for Research in Cognitive Science, 1997.
 E. Babovic and J. Velagic “Lowering SCADA development and implementation costs using PtP concept”, Information, Communication and Automation Technologies, 2009. ICAT 2009. XXII International Symposium on 29-31 oct.2009. PP.1-7, Bosnia.
 S. Boyer “Scada: Supervisory Control And Data Acquisition” 4th Edition, International Society of Automation, 2009, USA
 C. Lam, “Hadoop in Action” Manning publishing, 2011, Stanford, United State
 I. Aljarah, and S. Ludwig, “MapReduce Intrusion Detection System based on a Particle Swarm Optimisation Clustering Algorithm”, In proceeding of 2013 IEEE Congress on Evolutionary Computation, 2013, PP. 955-962, Cacun Mexico
 ICS-CERT, “Incident Response/Vulnerability Coordination in 2014” 2015.