Intrusion detection in user behavior can be divided into two appr

Intrusion detection in user behavior can be divided into two approaches: misuse detection and anomaly detection. Misuse detection systems use sensors to monitor and scan for known misuses, while anomaly detection systems have sensors that monitor and detect deviations from normal behavior. The misuse detection system sensor can detect misuse by a malicious user. However, the sensor would have difficulty in detecting unknown types of misuse because of a lack of information regarding the specific misuse. In an anomaly detection system, the sensor has the potential to detect misuse by evaluating deviations from normal behavior, even if the specific details of the type of misuse are unknown.

However, anomaly detection systems sometimes gives rise to false alarms, which can result in ��the boy who cried wolf�� syndrome.

The objective of anomaly detection is to reduce missed alarms without giving rise to false alarms. This paper focuses on anomaly detection of a masquerader using someone else’s account on a multiuser system, such as a UNIX-like system.Studies on masquerader detection have employed various approaches, including incremental probabilistic action modeling (IPAM) [1], hidden Markov models (HMM) [2,3], the uniqueness approach [4], etc. The uniqueness method outperforms the hybrid multistep Markov method [5], Bayes 1-step Markov method [6], compression method [7], sequence-match method [8], and IPAM method [1] with a false alarm rate between 1% and 5% [7].

These methods have been restricted to systems using a single sensor.

One drawback of the single sensor is that many false alarms arise when a valid user carries out new operations they have never performed Entinostat previously.In this paper, we propose an immunity-based anomaly detection system with multiple sensor agents based on the specificity and diversity of the biological immune system, in which each immune cell has a unique Dacomitinib receptor that has a high affinity for only specific antigens. Similarly, each of our agents has a unique sensor, which reacts strongly to the behavior of a specific user. When a user types a command sequence, all the agents check their own score of the command sequence by their sensor.

On the basis of all the scores, one of the agents determines whether the user is a masquerader or not. That is, our approach makes use of multiple sensors rather than a single sensor, which leads to an improvement in masquerader detection accuracy.In performance evaluation, detection accuracy has been evaluated with no distinction between users internal and external to a LAN. In general, anomaly detection methods use the information of only internal users.

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