Artificial Neural Network for Anomaly Intrusion Detection
نویسنده
چکیده
Since the advent of intrusion detection system (IDS) in the early 1980s, IDS has been suffering many problems until now. The detection of novel attacks and lower rate of false alarms must be realized in successful IDS. Misuse detection compares data against predefined patterns usually collected by an IDS signature database. It is hard for misuse detection to detect even slightly variation of known attacks. Anomaly detection finds attacks by using deviations from the normal behavior. Although anomaly detection can detect novel attacks, it cannot identify specific type of attack and entail the high rates of false alarms. In this paper, we point out the weakness of the previous methods such as statistical analysis and rulebased system in intrusion detection. In order to detect unknown attacks and avoid malicious hiding intrusion, artificial neural network for anomaly detection was introduced.
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