An intelligent clustering scheme for distributed intrusion detection in vehicular cloud computing

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• 作者：Neeraj Kumar ; Jaskaran Preet Singh ; Rasmeet S. Bali ; Sudip Misra…
• 关键词：Vehicular cloud ; Clustering ; Multimedia healthcare applications ; Intrusion detection ; Learning
• 刊名：Cluster Computing
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：18
• 期：3
• 页码：1263-1283
• 全文大小：1,888 KB
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• 作者单位：Neeraj Kumar (1)
  Jaskaran Preet Singh (2)
  Rasmeet S. Bali (2)
  Sudip Misra (3)
  Sana Ullah (4)
  
  1. Department of Computer Science and Engineering, Thapar University, Patiala, Punjab, India
  2. Department of Computer Science and Engineering, Chandigarh University, Chandigarh, Punjab, India
  3. School of Information Technology, IIT Kharagpur, Kharagpur, W.B., India
  4. Polytechnic Institute of Porto, Porto, Portugal
  
• 刊物类别：Computer Science
• 刊物主题：Processor Architectures
  Operating Systems
  Computer Communication Networks
  
• 出版者：Springer Netherlands
• ISSN：1573-7543

文摘

In recent years, vehicular cloud computing (VCC) has emerged as a new technology which is being used in wide range of applications in the area of multimedia-based healthcare applications. In VCC, vehicles act as the intelligent machines which can be used to collect and transfer the healthcare data to the local, or global sites for storage, and computation purposes, as vehicles are having comparatively limited storage and computation power for handling the multimedia files. However, due to the dynamic changes in topology, and lack of centralized monitoring points, this information can be altered, or misused. These security breaches can result in disastrous consequences such as-loss of life or financial frauds. Therefore, to address these issues, a learning automata-assisted distributive intrusion detection system is designed based on clustering. Although there exist a number of applications where the proposed scheme can be applied but, we have taken multimedia-based healthcare application for illustration of the proposed scheme. In the proposed scheme, learning automata (LA) are assumed to be stationed on the vehicles which take clustering decisions intelligently and select one of the members of the group as a cluster-head. The cluster-heads then assist in efficient storage and dissemination of information through a cloud-based infrastructure. To secure the proposed scheme from malicious activities, standard cryptographic technique is used in which the auotmaton learns from the environment and takes adaptive decisions for identification of any malicious activity in the network. A reward and penalty is given by the stochastic environment where an automaton performs its actions so that it updates its action probability vector after getting the reinforcement signal from the environment. The proposed scheme was evaluated using extensive simulations on ns-2 with SUMO. The results obtained indicate that the proposed scheme yields an improvement of 10 % in detection rate of malicious nodes when compared with the existing schemes. Keywords Vehicular cloud Clustering Multimedia healthcare applications Intrusion detection Learning