The Role of Machine Learning Algorithms in Enhancing Cybersecurity Threat Detection

Abstract

Traditional cybersecurity measures are becoming more ineffective in dealing with the ever-changing nature of cyber threats, which are both more complex and larger in scale. how crucial ML algorithms are for bettering cybersecurity threat identification and response. Machine learning allows for the rapid and precise identification of malicious activity by harnessing the power of data-driven models. It can recognise intricate patterns in network traffic, user behaviour, and attack fingerprints. several machine learning (ML) methods, including supervised, unsupervised, and reinforcement learning, and evaluate their use in IDS, anomaly detection, malware analysis, and phishing prevention. The benefits of ML in automating threat detection procedures, better response times, and less false positives. Data quality, algorithmic transparency, and adversarial assaults are some of the issues it covers as they pertain to ML algorithm integration into cybersecurity frameworks. the ability of machine learning to revolutionise cybersecurity, while simultaneously tackling the necessity of ongoing innovation to evade new dangers.