Cyber Attack Detection Dataset: A Review

Abstract

Abstract As cyber attack become more complicated, it becomes more difficult to identify breaches successfully. The inability to identify intrusions might jeopardize security services’ confidence, compromising data confidentiality, integrity, and availability. Cyber attacks like, Ping of Death, Botnets, also IP spoofing, as well as Social Engineering attacks, are becoming more common. A number of Intrusion Detection System (IDS) approaches developed to encounter cyber security intrusion. In order to discover attack patterns, the IDS performance was evaluated by employing dataset of IDS made up of network traffic properties. Intrusion detection is a classification problem in which different Artificial Intelligence techniques have been utilized to classify between legitimate also malicious network traffic. The multiple IDS datasets used to evaluate the IDS model are listed in this publication. These are new attack categories and recent datasets containing network attack features. This paper presents several IDS dataset with many existing evaluation techniques in model of IDS. Hopefully the outcome can be used in designing efficient and effective systems employing the benchmark and new IDS datasets.