MACHINE LEARNING TECHNIQUES FOR CYBER SECURITY

Abstract

Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on developing computer programs that can recognize patterns in historical data, learn from it, and make logical judgements with little to no human input. Protecting digital systems, such as computers, servers, mobile devices, networks, and related data against hostile assaults is known as cyber security. Two key components of combining cyber security with ML are accounting for cyber security where machine learning is used and using machine learning to enable cyber security. This coming together may benefit us in a number of ways, including by enhancing the security of machine learning models, enhancing the effectiveness of cyber security techniques, and supporting the efficient detection of zero day attacks with minimal human interaction. The cyber security landscape has grown more complicated due to the quick development of technology, creating a number of difficulties for protecting sensitive data and important infrastructures. This project's objective is to implement three different systems using machine learning in cyber security. The first system investigates how reinforcement learning may be used to improve cyber security measures. Reinforcement learning algorithms are taught to make the best choices based on their interactions with the environment through trial and error, which can be useful in adjusting to changing cyberthreats. The second approach focuses on malware identification since evasive and polymorphic malware have proven difficult to identify using standard signature-based methods. Several machine learning and deep learning approaches are used in this effort to accurately identify and categorize dangerous software. The third solution uses machine learning and deep learning techniques to address the crucial problem of network intrusion detection. The performance of each system's machine learning models will be evaluated throughout the project using a variety of datasets alongside evaluation measures.