Bio-inspired Error Detection for Complex Systems

In a number of areas, for example, sensor networks and systems of systems, complex networks are being used as part of applications that have to be dependable and safe. A common feature of these networks is they operate in a de-centralised manner and are formed in an ad-hoc manner and are often based on individual nodes that were not originally developed specifically for the situation that they are to be used. In addition, the nodes and their environment will have different behaviours over time, and there will be little knowledge during development of how they will interact. A key challenge is therefore how to understand what behaviour is normal from that which is abnormal so that the abnormal behaviour can be detected, and be prevented from affecting other parts of the system where appropriate recovery can then be performed. In this paper we review the state of the art in bio-inspired approaches, discuss how they can be used for error detection as part of providing a safe dependable sensor network, and then provide and evaluate an efficient and effective approach to error detection.

[1]  Theodore S. Rappaport,et al.  Wireless communications - principles and practice , 1996 .

[2]  Helena Szczerbicka,et al.  An Immuno-Inspired Approach to Misbehavior Detection in Ad Hoc Wireless Networks , 2010, ArXiv.

[3]  Miao Xie,et al.  Anomaly Detection in Wireless Sensor Networks , 2013 .

[4]  Hazel M. Dockrell.  Roitt's essential immunology, 10th edition. I. M. Roitt & P. J. Delves. Oxford: Blackwell Science, 2001. xi+481pp. Price £24.95. ISBN 0-632-05902-8 , 2002 .

[5]  David A. Maltz,et al.  Dynamic Source Routing in Ad Hoc Wireless Networks , 1994, Mobidata.

[6]  Nong Ye,et al.  A Markov Chain Model of Temporal Behavior for Anomaly Detection , 2000 .

[7]  Sang Hyuk Son,et al.  Run time assurance of application-level requirements in wireless sensor networks , 2009, SenSys '09.

[8]  Ingo Mierswa,et al.  YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.

[9]  Christos Faloutsos,et al.  FastMap: a fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets , 1995, SIGMOD '95.

[10]  Sung-Bae Cho,et al.  Evolutionary neural networks for anomaly detection based on the behavior of a program , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Jingyuan Li,et al.  Automatic and robust breadcrumb system deployment for indoor firefighter applications , 2010, MobiSys '10.

[12]  Helena Szczerbicka,et al.  Priming: making the reaction to intrusion or fault predictable , 2011, Natural Computing.

[13]  Jonathan Timmis,et al.  Artificial Immune Systems: A New Computational Intelligence Approach , 2003 .

[14]  Kamin Whitehouse,et al.  Towards Stable Network Performance in Wireless Sensor Networks , 2009, 2009 30th IEEE Real-Time Systems Symposium.

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

[16]  Robbert van Renesse,et al.  JiST: an efficient approach to simulation using virtual machines , 2005, Softw. Pract. Exp..

[17]  T.Y. Lin,et al.  Anomaly detection , 1994, Proceedings New Security Paradigms Workshop.

[18]  Yang Xue,et al.  Study of Immune Control Computing in Immune Detection Algorithm for Information Security , 2008, ICIC.

[19]  Rogério de Lemos,et al.  Immune-Inspired Adaptable Error Detection for Automated Teller Machines , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[20]  Jonathan Timmis,et al.  Application Areas of AIS: The Past, The Present and The Future , 2005, ICARIS.

[21]  Eleazar Eskin,et al.  Anomaly Detection over Noisy Data using Learned Probability Distributions , 2000, ICML.

[22]  Insup Lee,et al.  Opportunities and Obligations for Physical Computing Systems , 2005, Computer.

[23]  Gang Zhou,et al.  Achieving Long-Term Surveillance in VigilNet , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.