Machine Learning Techniques Used in Detection of DOS Attacks: A Literature Review

Due to increasing complexity of systems, it is easier to compromise computer systems. Thus in order to detect attacks, Intrusion Detection Systems / Intrusion Prevention Systems is employed. Intrusion Detection Systems / Intrusion Prevention Systems is the most basic way to protect the network. IDS/IPS systems follow two different approaches on how to detect intruders: signature-based or anomaly-based detection. A signature-based IDS monitors packets on a specified network and then compares these packets against a set of signatures from known malicious threats. The anomaly-based detection technique centres on the concept of a baseline for network behaviour. Baseline can be considered as description of the type of network behaviour that can be accepted or is normal, any deviation from this baseline is considered as an anomaly. Therefore Anomaly based Intrusion detection uses machine learning techniques to detect whether a packet is intrusive or non-intrusive. The focal point of our study is to provide a systematic review of machine learning techniques used in dos attack detection.

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