ARTIFICIAL INTELLIGENCE APPROACHES FOR INTRUSION DETECTION

Recent research indicates a lot of attempts to create an intrusion detection system that is capable of learning and recognizing attacks it faces for the first time. Benchmark datasets were created by the MIT Lincoln Lab and by the International Knowledge Discovery and Data Mining group (KDD). A number of competitions were held and many systems developed as a result. The overall preference was given to expert systems that were based on decision making tree algorithms. This paper explores neural networks as means of intrusion detection. After multiple techniques and methodologies are investigated, we show that properly trained neural networks are capable of fast recognition and classification of different attacks at the level superior to previous approaches.