Machine Learning In Cybersecurity: Opportunities and Challenges

Machine learning (ML) has emerged as an essential tool for increasing cybersecurity defenses, with the ability to automate threat detection, identify weaknesses, and respond to attacks in real-time. Its ability to evaluate large volumes of data and adapt to changing threats makes it an essential tool for combatting modern cyberattacks. However, the integration of ML in cybersecurity introduces several challenges, including data privacy concerns, model bias, explainability, adversarial attacks, and the need for ethical governance. This paper explores the opportunities provided by ML in cybersecurity, such as improved threat intelligence, anomaly detection, and automated incident response, while critically examining the associated challenges. It proposes a framework for addressing ethical issues and securing ML models from adversarial manipulation, ensuring that ML's role in cybersecurity is both effective and responsible.