Specific attack adjusted Bayesian network for intrusion detection system
作者： Dana Simian , Olga Miljkovic , Dusan Bulatovic , Milan Tuba
DOI:
关键词: Anomaly-based intrusion detection system 、 Engineering 、 Continuation 、 Bayesian network 、 Computation 、 Quality (business) 、 Data Protection Act 1998 、 Sensitivity (control systems) 、 Machine learning 、 Data mining 、 Artificial intelligence 、 Intrusion detection system
摘要: This paper further examines suitability of Bayesian networks for intrusion detection in computer networks. It is a continuation [1]. Medical records should be readily available but also well protected, which are contrdictory goals. Automatic system (IDS) required, all used approaches have certain shortcomings. known to good features, except that general case computationally too expensive. Here we show limiting any specific attack makes manageable and suitable the IDS. Example network constructed examined. Results promising since with very limited computation low sensitivity quality prior knowledge, potentially dangerous situations successfully detected classified. Such can represent an independent agent distributed system.
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