Impact of Machine Learning in Cybersecurity Augmentation

Abstract

The increasing number of cyber threats and attacks has led to the development and implementation of various cybersecurity measures to protect organizations and individuals from potential breaches. The field of machine learning (ML) has become promising technology in the field of cybersecurity, as it can help detect and prevent malicious activities in real time. This report presents a review of the current research on the impact of machine learning in cybersecurity. The research paper begins with an overview of the key concepts of machine learning and cybersecurity. It then provides an overview of the various ML techniques used in cybersecurity, including supervised, unsupervised, and reinforcement learning. The report then explores the various applications of ML in cybersecurity, such as intrusion detection, malware analysis, and anomaly detection. The report also discusses the benefits and limitations of ML in cybersecurity. Benefits include improved accuracy and speed of threat detection. In contrast, limitations include the potential for ML models to be tricked by sophisticated attacks and the requirement for vast volumes of data for the efficient training of ML models. Finally, the report provides a discussion on the future of ML in cybersecurity, including potential research directions and challenges to be addressed. These include the need for better explainability and interpretability of ML models, the development of adversarial ML techniques, and the integration of human expertise with ML-based cybersecurity solutions. Overall, this report highlights the significant impact of machine learning on cybersecurity and the potential for continued advancements in this field. With ongoing research and development, ML has the potential to greatly enhance the security of individuals and organizations against cyber threats.