AI Driven IDS System in Network Security

Abstract: The increasing use of internet technologies, cloud computing, and smart devices has significantly increased cyber threats in modern networks. Traditional Intrusion Detection Systems (IDS) are unable to effectively detect advanced and unknown attacks because they rely mainly on predefined signatures and static security rules. Artificial Intelligence (AI) based IDS provides an intelligent and adaptive approach for improving network security. This paper presents a seminar-based study on AI Driven IDS Systems in Network Security using Machine Learning and Deep Learning techniques. The proposed system analyzes network traffic patterns, identifies abnormal behavior, and detects cyberattacks with improved accuracy and reduced false alarm rates. Various AI algorithms such as Random Forest, Support Vector Machine, Convolutional Neural Network, and Long Short-Term Memory are discussed in this paper. The study highlights the importance of AI-driven security systems in detecting both known and unknown threats efficiently. The proposed approach enhances overall network protection and provides better adaptability against evolving cyberattacks.

Keywords: Artificial Intelligence, Intrusion Detection System, Machine Learning, Deep Learning, Network Security, Cybersecurity