Identifying Camouflaging Adversary in MANET Using Cognitive Agents

Mobile ad hoc networks (MANETs) are often prone to variety of attacks like denial of service, impersonation, eavesdropping, camouflaging adversary, blackhole, wormhole, replay, jamming, man in the middle, etc. Among all these attacks camouflaging adversary attack is the attack, launched by an insider and has a devastating effect on network performance. In this paper, we present a cognitive agents (CAs) based security scheme for identifying camouflaging adversaries in MANETs. The proposed scheme uses CAs with observations-belief model to effectively identify camouflaging adversary nodes and the identified nodes will be isolated from the network. The isolation of the camouflaged adversaries enhances the network performance with respect to various performance metrics like bandwidth, throughput, packet drop ratio, reliability, etc.

[1]  P. Goyal,et al.  MANET: Vulnerabilities, Challenges, Attacks, Application , 2011 .

[2]  J. K. Mandal,et al.  A NOVEL TECHNIQUE TO DETECT INTRUSION IN MANET , 2013 .

[3]  Mahalingam Ramkumar,et al.  A Framework for Dual-Agent MANET Routing Protocols , 2008, IEEE GLOBECOM 2008 - 2008 IEEE Global Telecommunications Conference.

[4]  K. Somasundaram,et al.  A comparative study of clusterhead selection algorithms in wireless sensor networks , 2011, ArXiv.

[5]  Rituparna Chaki,et al.  MADSN: Mobile Agent Based Detection of Selfish Node in MANET , 2011 .

[6]  Usha Devi Gandhi,et al.  Enhanced DTLS with CoAP-based authentication scheme for the internet of things in healthcare application , 2017, The Journal of Supercomputing.

[7]  Yogendra Kumar Jain,et al.  Secure Mobile Agent Based IDS for MANET , 2012 .

[8]  Puneet Singhania,et al.  A SURVEY OF DIFFERENT LETHAL ATTACKS ON MANETs , 2014 .

[9]  Guosun Zeng,et al.  Partitioning big graph with respect to arbitrary proportions in a streaming manner , 2018, Future Gener. Comput. Syst..

[10]  Li Xiao,et al.  CENDA: Camouflage Event Based Malicious Node Detection Architecture , 2009, 2009 IEEE 6th International Conference on Mobile Adhoc and Sensor Systems.

[11]  Gunasekaran Manogaran,et al.  Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things , 2017, Cluster Computing.

[12]  Gunasekaran Manogaran,et al.  A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system , 2017, Future Gener. Comput. Syst..

[13]  Shyamala Ramachandran,et al.  Impact of Sybil and Wormhole Attacks in Location Based Geographic Multicast Routing Protocol for Wireless Sensor Networks , 2011 .

[14]  Mohd Faisal,et al.  Attacks in MANET , 2013 .

[15]  Haiyun Luo,et al.  Security in mobile ad hoc networks: challenges and solutions , 2004, IEEE Wireless Communications.

[16]  Usha Devi Gandhi,et al.  A novel three-tier Internet of Things architecture with machine learning algorithm for early detection of heart diseases , 2017, Comput. Electr. Eng..

[17]  Mahesh Motwani,et al.  Survey of clustering algorithms for MANET , 2009, ArXiv.

[18]  Suhaidi Hassan,et al.  MOBILE AD HOC NETWORKS UNDER WORMHOLE ATTACK: A SIMULATION STUDY , 2011 .

[19]  Gunasekaran Manogaran,et al.  RETRACTED ARTICLE: Hybrid Recommendation System for Heart Disease Diagnosis based on Multiple Kernel Learning with Adaptive Neuro-Fuzzy Inference System , 2017, Multimedia Tools and Applications.

[20]  Gunasekaran Manogaran,et al.  RETRACTED ARTICLE: A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing , 2017, Multimedia Tools and Applications.

[21]  Shyamala Ramachandran Performance Comparison of Routing Attacks in Manet and WSN , 2012 .

[22]  F. Cherblanc,et al.  Soil-Water Characteristic Curve Modeling at Low Water Content: Empirical and Semi-Empirical Approaches , 2013 .