Machine learning in cybersecurity: Innovations in threat prevention and mitigation

Cyberattacks are becoming more frequent and complex, posing serious risks to organizations and governments. Traditional methods of cybersecurity are often not enough to handle these evolving threats. Machine Learning (ML) is emerging as a powerful tool to improve cybersecurity by helping to detect and prevent cyberattacks more efficiently.
Problem: Many current cybersecurity methods struggle with the increasing complexity of cyber threats, and face challenges in handling large volumes of data and adapting to new types of attacks, making it important to explore better solutions.
Objectives: This study aimed to investigate how ML can improve threat detection, prevention, and mitigation. It focused on how ML algorithms can analyze large amounts of data in real-time and identify unusual behavior that could signal a security breach.
Results and Findings: The study found that ML can greatly improve the accuracy of threat detection, reduce false alarms, and speed up responses to attacks. Well-trained ML models can also predict and adapt to new threats, improving cybersecurity over time. However, the research highlighted challenges such as protecting ML models from attacks and ensuring they are regularly updated.
Recommendations: To make ML even more effective in cybersecurity, the study recommends strengthening ML models against adversarial attacks, continuously updating models with fresh data, and combining ML with other technologies like blockchain to create stronger security systems.