Sensor Networks Security Based on Sensitive Robots Agents: A Conceptual Model

Multi-agent systems are currently applied to solve complex problems. From this class of problem the security of networks is a very important and sensitive problem. We propose in this paper a new conceptual model Hybrid Sensitive Robot Metaheuristic for Intrusion Detection. The proposed technique could be used with machine learning based intrusion detection techniques. Our novel model uses the reaction of virtual sensitive robots to different stigmergic variables in order to keep the tracks of the intruders when securing a sensor network.

[1]  P.-P. Grasse La reconstruction du nid et les coordinations interindividuelles chezBellicositermes natalensis etCubitermes sp. la théorie de la stigmergie: Essai d'interprétation du comportement des termites constructeurs , 1959, Insectes Sociaux.

[2]  Petrica C. Pop,et al.  A Sensitive Metaheuristic for Solving a Large Optimization Problem , 2008, SOFSEM.

[3]  Stephen Northcutt,et al.  Network intrusion detection , 2003 .

[4]  Camelia-Mihaela Pintea Combinatorial optimization with bio-inspired computing , 2008 .

[5]  Camelia-Mihaela Pintea,et al.  Learning Sensitive Stigmergic Agents for Solving Complex Problems , 2010, Comput. Informatics.

[6]  Ajith Abraham,et al.  IDEAS: intrusion detection based on emotional ants for sensors , 2005, 5th International Conference on Intelligent Systems Design and Applications (ISDA'05).

[7]  Barna Iantovics,et al.  Intelligent Complex Evolutionary Agent‐Based Systems , 2009 .

[8]  Camelia-Mihaela Pintea,et al.  Sensitive Ants: Inducing Diversity in the Colony , 2008, NICSO.

[9]  Belén Melián-Batista,et al.  Nature Inspired Cooperative Strategies for Optimization, NICSO 2008, Puerto de La Cruz, Tenerife, Spain, 12-14 November 2008 , 2009, NICSO.

[10]  Mike Preuss,et al.  Support vector machine learning with an evolutionary engine , 2009, J. Oper. Res. Soc..

[11]  S. Selvakani,et al.  Feature Selection of Intrusion Detection Data using a Hybrid Genetic Algorithm/KNN Approach , 2003, HIS.

[12]  Gildas A. Deograt-Lumy,et al.  Intrusion Prevention Systems , 2011, Encyclopedia of Information Assurance.

[13]  Salvatore J. Stolfo,et al.  Mining Audit Data to Build Intrusion Detection Models , 1998, KDD.

[14]  Jingtao Yao,et al.  A study on fuzzy intrusion detection , 2005, SPIE Defense + Commercial Sensing.

[15]  Mária Bieliková,et al.  SOFSEM 2008: Theory and Practice of Computer Science, 34th Conference on Current Trends in Theory and Practice of Computer Science, Nový Smokovec, Slovakia, January 19-25, 2008, Proceedings , 2008, SOFSEM.

[16]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[17]  I. Ramesh Babu Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms , 2008 .

[18]  Tony White,et al.  Expert Assessment of Stigmergy: A Report for the Department of National Defence , 2005 .

[19]  Camelia-Mihaela Pintea,et al.  Sensitive Stigmergic Agent Systems - A Hybrid Approach to Combinatorial Optimization , 2008, Innovations in Hybrid Intelligent Systems.

[20]  Camelia-Mihaela Pintea,et al.  Cooperative Learning Sensitive Agent System for Combinatorial Optimization , 2007, NICSO.

[21]  Karen A. Scarfone,et al.  Guide to Intrusion Detection and Prevention Systems (IDPS) , 2007 .

[22]  Risto Miikkulainen,et al.  Intrusion Detection with Neural Networks , 1997, NIPS.

[23]  Jianxiong Luo INTEGRATING FUZZY LOGIC WITH DATA MINING METHODS FOR INTRUSION DETECTION , 1999 .

[24]  Seungyong Yoon,et al.  Multi-hash based Pattern Matching Mechanism fo r High-Performance Intrusion Detection , 2009 .

[25]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..