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Design of Cognitive Fog Computing for Intrusion Detection in Internet of Things
S.Prabavathy,K.Sundarakantham,S.Mercy Shalinie 한국통신학회 2018 Journal of communications and networks Vol.20 No.3
Internet of things (IoT) is penetrating into every aspectof our lives including our body, our home and our living environmentalong with numerous security challenges.With rapidly growingnumber of connected devices in IoT, the scope for cyber-attackalso increases exponentially. Therefore an effective intrusion detectionsystem (IDS) is needed to efficiently detect the attack at fasterrate in highly scalable and dynamic IoT environment. In this paper,a novel intrusion detection technique is proposed based on fog computingusing Online Sequential Extreme Learning Machine (OSELM)which can intelligently interpret the attacks from the IoTtraffic. In the proposed system, the existing centralized cloud intelligencein detecting the attack is distributed to local fog nodesto detect the attack at faster rate for IoT application. The distributedarchitecture of fog computing enables distributed intrusiondetection mechanism with scalability, flexibility and interoperability. The analysis of the proposed system proves to be efficientin terms of response time and detection accuracy.
Design of Cognitive Fog Computing for Intrusion Detection in Internet of Things
Prabavathy, S.,Sundarakantham, K.,Shalinie, S.Mercy The Korean Institute of Communications and Informa 2018 Journal of communications and networks Vol.20 No.3
Internet of things (IoT) is penetrating into every aspect of our lives including our body, our home and our living environment along with numerous security challenges. With rapidly growing number of connected devices in IoT, the scope for cyber-attack also increases exponentially. Therefore an effective intrusion detection system (IDS) is needed to efficiently detect the attack at faster rate in highly scalable and dynamic IoT environment. In this paper, a novel intrusion detection technique is proposed based on fog computing using Online Sequential Extreme Learning Machine (OS-ELM) which can intelligently interpret the attacks from the IoT traffic. In the proposed system, the existing centralized cloud intelligence in detecting the attack is distributed to local fog nodes to detect the attack at faster rate for IoT application. The distributed architecture of fog computing enables distributed intrusion detection mechanism with scalability, flexibility and interoperability. The analysis of the proposed system proves to be efficient in terms of response time and detection accuracy.
Trust Based Authentication and Key Establishment for Secure Routing in WMN
( G. Akilarasu ),( S. Mercy Shalinie ) 한국인터넷정보학회 2014 KSII Transactions on Internet and Information Syst Vol.8 No.12
In Wireless Mesh Networks (WMN), an authentication technique can be compromised due to the distributed network architecture, the broadcast nature of the wireless medium and dynamic network topology. Several vulnerabilities exist in different protocols for WMNs. Hence, in this paper, we propose trust based authentication and key establishment for secure routing in WMN. Initially, a trust model is designed based on Ant Colony Optimization (ACO) to exchange the trust information among the nodes. The routing table is utilized to select the destination nodes, for which the link information is updated and the route verification is performed. Based on the trust model, mutual authentication is applied. When a node moves from one operator to another for accessing the router, inter-authentication will be performed. When a node moves within the operator for accessing the router, then intra-authentication will be performed. During authentication, keys are established using identity based cryptography technique. By simulation results, we show that the proposed technique enhances the packet delivery ratio and resilience with reduced drop and overhead.