Improving Intrusion Detection Systems with Artificial Intelligence: A Review of Techniques and Applications
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Abstract
In order to protect computer networks, intrusion detection systems are required. However, due to the increasing sophistication and complexity of cyber threats, they are no longer able to provide effective protection against attacks. This is why the development of AI techniques has been identified as a promising way to improve the efficiency and accuracy of IDS. The paper presents an overview of the various techniques that are used in IDS with the help of AI, such as fuzzy logic, machine learning, and evolutionary algorithms, and discusses their limitations and strengths. It also covers the recent advancements in this field. The abstract summarizes the main objective of the study, which is to analyze the current state of IDS and how AI can be utilized to improve its efficiency and accuracy. It explores and evaluates the effectiveness of various techniques, such as deep learning, evolutionary algorithms, and machine learning, in detecting and preventing intrusions. The concluding section of the study provides an overview of the current status of the field and future directions in this area.
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