Abstract
Cyber security is becoming more sophisticated, and as a result, there is an increasing challenge to accurately detect intrusions. Lack of intrusion prevention can degrade the credibility of security services, namely data confidentiality, integrity and availability. Many intrusion detection methods have been suggested in the literature to address threats to computer security, which can be broadly classified into signature-based intrusion detection (SIDS) and anomaly-based intrusion detection systems. (AIDS). This research presents the contemporary taxonomy of IDS, a comprehensive review of important recent work, and an overview of commonly used datasets for assessment purposes. It also presents detail analysis of different machine learning approach for intrusion detection.
Highlights
Outlier detection refers to the problem of finding patterns in the data that do not meet the expected normal behaviour [1]
Many intrusion detection methods have been suggested in the literature to address threats to computer security, which can be broadly classified into signaturebased intrusion detection (SIDS) and anomaly-based intrusion detection systems. (AIDS)
The importance of Outlier detection system from the fact that anomalies in the data translate into meaningful information across a wide range of application domains
Summary
Outlier detection refers to the problem of finding patterns in the data that do not meet the expected normal behaviour [1]. These anomalous patterns are often referred to as anomalies, inconsistent observations, exceptions, glitches, defects, noise, errors, or contaminants in various application domains. . The importance of Outlier detection system from the fact that anomalies in the data translate into meaningful information across a wide range of application domains. Outlier detection techniques are widely used to detect abnormal patterns in patients' medical records that may be symptoms of a new disease. We evaluate different machine learning approach for network anomaly detection and their results based on standard network dataset with machine learning tools
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