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RISS 인기검색어
- 검색키워드
- 저자 : K.Sundarakantham (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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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.
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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.
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