OMAMIDS: Ontology Based Multi-Agent Model Intrusion Detection System for Detecting Web Service Attacks
K. Anusha, E. Sathiyamoorthy · Journal of Applied Security Research · 2016
ABATRACTWeb service plays a significant role in the Internet applications. According to the current researchers, the web services are highly prone to the cyber-attacks. The Intrusion Detection System (IDS) is developed to detect these attacks. The main drawback of the traditional IDSs is the usage of some specific language, to define the rules. To overcome the drawback, a self-learning ontology is used to update the rules according to the new types of attacks. This paper presents an Ontology-based Multi-Agent Model Intrusion Detection System (OMAMIDS) for detecting web service attacks. The attacker rules are generated using the Intuitionistic Fuzzy Logic (IFL). The sniffing agent is used to monitor the network behavior and an analysis agent is used to select the set of required information from the sniffed data. It detects the behavior of the request, whether it is normal or abnormal. If the behavior is normal, request processing is enabled. Otherwise, the packet is forwarded to the decision agent. The decision agent includes globally shared rules of the attacker, for taking actions related to the severity level of the detected attack. The proposed system achieves high detection rate and accuracy and lower false positive rate than the existing techniques.