Machine learning based Cyber Attack detection on Internet Traffic

Abstract

Cyber attacks on the internet have become increasingly sophisticated and frequent, posing significant challenges to cybersecurity. Traditional rule-based methods for detecting these attacks often struggle to keep pace with the evolving tactics of malicious actors. In this context, machine learning (ML) techniques have emerged as a promising approach for cyber attack detection due to their ability to analyze large volumes of data and identify patterns indicative of malicious behavior. The proposed framework for utilizing machine learning in cyber attack detection on the internet. The framework integrates various ML algorithms, including supervised, unsupervised, and reinforcement learning techniques, to enhance the detection capabilities against different types of cyber threats. Moreover, the framework incorporates feature engineering and selection methods to optimize the performance of ML models in identifying malicious activities.