AI AND MACHINE LEARNING IN CYBER THREAT DETECTION

Abstract

Cybersecurity has gained a lot of attention in today's security issues due to the increased popularity of the Internet of Things, the quick growth of computer networks, and the multitude of important applications. Because of this, identifying different types of cyberattacks or network anomalies and designing an effective intrusion detection system is becoming more and more crucial for modern security. Artificial intelligence, specifically machine learning techniques, can be used to build an intelligent intrusion detection system based on data. To achieve this, we provide in this work an artificial intelligence, Tree machine-learning-based security model, which, after first determining the importance, builds a tree-based generalized artificial intelligence model based on the chosen essential features. Nowadays, the most important problem is cyber threats in machine learning and artificial intelligence. If cyber threats are not detected in time it can cause severe damage to the organization's reputation and revenue and whole networking system in machine learning and artificial intelligence. The demand and need for Cyber security and protection is increasing to deal with the different types of cyber-attacks.  To deal with and solve the problems associated with cyber-threats, different organizations, and institutes have implemented several cyber-threat detections.