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A Survey On Intrusion Detection System IDS Using Machine Learning Algorithms

. Abdulla Alali and Maria Yousef


Nowadays, the usage of the internet is growing rapidly which leads to different security problems in the network. Security threat indicates violating the integrity and confidentiality of the systems thereby the organizations may suffer a financial loss. Hackers get access to the system and bypass the authentication procedure to extract financial and personal data from their victims' databases. As a result, detection of security threats, also known as intrusion detection, has become an essential issue in network, data, and information security. An intrusion detection system (IDS) is designed to detect and classify various threats. It divides attacks into two categories: normal and abnormal. Generally, IDS are based on either a host or a network. A variety of data mining approaches and machine learning techniques are widely employed by IDS. in this study, a survey on intrusion detection systems is offered. The survey focused on the methodologies employed in IDS, in addition to a thorough understanding of the strengths and limits of detection methods, which serves as a basis for the development of effective IDS.

Keywords: IDS, Data Mining, Network-Based, Host-Based, Machine Learning.

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