Network Intrusion Detection using Machine Learning, Deep Learning - A Review
Venkata Ramani Varanasi, Shaik Razia · 2022 4th International Conference on Smart Systems and Inventive Technology (ICSSIT) · 2022
An Intrusion Detection System (IDS) is an essential feature, aims to defend the integrity, availability, the confidentiality of the data utilized in the networks against attacks. An IDS observes the network activities to examine the invasive patterns. In the case of an attack, the system should have a proper response. Different Machine Learning (ML) techniques are being used over the past several years. Since an algorithm can be evaluated on various parameters, no single algorithm is said to be more accurate. Ensemble learning combines several weak learners and are useful to improve the performance of intrusion detection. Deep learning methods are gaining traction as useful techniques, if the input data is huge and it suits real-time applications. Transfer Learning (TL) methods suits the applications that have limited data. These methods are useful in detecting zero day attacks. This paper presents the recent works carried out on intrusion detection. The study enables the readers to understand research foundation, research status, challenges, and the future scope of research.