Exploring Machine Learning Algorithms for Detecting Cyber Attacks and Threats
Boya Leela Mahesh, Sandeep Kaur · 2024
Securing systems, networks, and information amid cyber threats involves a blend of machine learning and cybersecurity, which uses machine learning to identify abnormal behaviors, classify malicious software, authenticate user identities, and predict possible security breaches. This paper presents a detailed review and analysis of applying machine learning in beefing up cybersecurity defenses and it outlines various machine learning methods such as decision trees, support vector machines, neural networks, and Long Short-Term Memory Models illustrating their efficiency in recognizing diverse cyber threats in several datasets and attack scenarios. In this Analysis, a real-time phishing dataset was used, the Support Vector Machine (SVM) model demonstrated exceptional accuracy of 94%, surpassing other tested algorithms. This study addresses important challenges of cybersecurity which include: the complex nature of cyber-attacks, high rates of false positives and difficulties experienced when analyzing encrypted data. The role of advanced solutions in automated threat detection and response processes is underscored by the work. It also incorporates global threat intelligence feeds that would improve adaptive defense strategies using these advanced machine learning approaches. Finally, upcoming trends are discussed like quantum computing integration into cryptographic protocols and insider threats identification through behavioral analysis as well as user profiling. This paper synthesizes existing research while proposing future directions, intending to guide the development of resilient cybersecurity frameworks that can protect digital ecosystems from emerging threats.