Autonomous malicious activity inspector - AMAI

  • Authors:

  • Umar Manzoor;Samia Nefti;Yacine Rezgui

  • Affiliations:

  • Department of Computer Science, School of Computing, Science and Engineering, The University of Salford, Salford, Greater Manchester, United Kingdom;Department of Computer Science, School of Computing, Science and Engineering, The University of Salford, Salford, Greater Manchester, United Kingdom;Department of Computer Science, School of Engineering, Cardiff University, Cardiff, United Kingdom

  • Venue:
  • NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
  • Year:
  • 2010

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Abstract

Computer networks today are far more complex and managing such networks is not more then a job of an expert. Monitoring systems helps network administrator in monitoring and protecting the network by not allowing the users to run illegal application or changing the configuration of the network node. In this paper, we have proposed Autonomous Malicious Activity Inspector - AMAI which uses ontology based knowledge base to predict unknown illegal applications based on known illegal application behaviors. AMAI is an Intelligent Multi Agent System used to detect known and unknown malicious activities carried out by the users over the network. We have compared ABSAMN and AMAI concurrently at the university campus having seven labs equipped with 20 to 300 number of PCs in various labs; results shows AMAI outperform ABSAMN in every aspect.