Enhancing Cybersecurity with AI-Based Threat Classification

In the contemporary era, cyber security has been on the rise as cyber attacks increase and become more sophisticated. This research aims at improving methods of automated threat detection using machine learning techniques to explore and categorize cyber attacks. Data sets are utilized in the research and pre-processed to transform them into feature sets to feed into machine learning models. Data sets utilized are different types of network traffic and patterns of attacks. Performance of a few of the classification methods, random forests, decision trees, and SVM, for accurate detection and classification of cyberattacks is discussed. Performance of the method is tested on a Linux platform to estimate scalability by its support for Windows and Linux OS. To enhance machine-based cybersecurity, this research attempts to investigate and implement a machine learning approach to analyse and categorize different cyberattacks. The aim is to enhance cybersecurity by offering an efficient, precise, and automated cyber threat detection process in real-time, which will prevent possible security attacks. The performance of each model is evaluated based on accuracy, precision, recall, and F1-score. Random Forests are trained to perform well on any kind of attack, while SVM may be effective on certain kinds of attacks. Confusion matrices and performance plots show the classification output for miscellaneous attack categories, i.e., DoS, DDoS, malware attacks, indicating the entire analysis of how each model performs in detecting cyber threats.