LnaCBR:Case Based Reasoning Architecture for Intrusion Detection to Learning New Attacks

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The agents used in the intrusion detection architectures have multiple characteristics namely delegation, cooperation and communication. However, an important property of agents: learning is not used. The concept of learning in existing IDSs used in general to learn the normal behavior of the system to secure. For this,normal profiles are built in a dedicated training phase, these profiles are then compared with the current activity. Thus, the IDS does not have the ability to detect new attacks. We propose in this paper, a new architecture based intrusion MAS adding a learning feature abnormal behaviors that correspond to new attack patterns detection. Thanks to this feature to update the knowledge base of attacks take place when a new plan of attack is discovered. To learn a new attack, the architecture must detect at first and then update the basic attack patterns. For the detection step, the detection approach adopted is based on the technique of Case-Based Reasoning (CBR). Thus, the proposed architecture is based on a hierarchical and distributed strategy where features are structured and separated into layers.

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Security , Intrusion Detection , Learning , Plan of Attack , Case , Based Reasoning , Agent , Network , Multi , Agent System

Mediterranean Telecommunications Journal

59

Mediterranean Telecommunications Journal