Empowering Security with Machine Learning for Ransomware and Malware Detection

Abstract

Due to the increase and frequency of network attacks, network security protection needs to be established[1]. The project focuses on using machine learning technology to detect ransomware and malware and aims to improve computer security. This work involves using advanced algorithms and models to analyze different types of data to identify patterns associated with crime. The proposed system extracts relevant features from various data points, including archive behavior, network traffic, and physical interactions. These features are used to train machine learning models to distinguish between good behavior and bad behavior. By constantly learning and updating, the model improves accuracy over time and stays ahead of evolving ransomware and malware threats. This project aims to contribute to the field of cybersecurity by providing efficient and effective methods to detect and mitigate threats. The use of machine learning-based search engines should improve defense against cyber attacks, ultimately protecting sensitive data and ensuring the integrity of computer systems[2]. This research aligns with the growing need for cybersecurity in response to new solutions to combat the evolution of malware. Key Words: Cybersecurity, Ransomware detection, Malware detection, Machine learning techniques, Advanced algorithms, Data analysis, Feature extraction, Model training, Continuous learning, Threat detection, Defence mechanisms