Deep Learning Approach for Intrusion Detection System

Abstract

The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is tool that helps to detect intrusions by inspecting the network traffic. A system called an intrusion detection system (IDS) observes network traffic for malicious transactions and sends immediate alerts when it is observed. It is software that checks a network or system for malicious activities or policy violations. Each illegal activity or violation is often recorded and notified to an administrator. IDS monitors a network or system for malicious activity and protects a computer network from unauthorized access from users, including perhaps insiders. The intrusion detector learning task is to build a predictive model capable of distinguishing between ‘malicious connections’ and ‘genuine connections’. Keywords: Cyber security, intrusion detection, malware, machine learning, deep learning, deep neural networks, CNN,