Machine learning and deep learning in cyber security
B. Ragavi, S. Divya, M. R. Mano Jemila, Ahmed A. Elngar · 2024
Machine learning and deep learning have gained crucial roles in recent cybersecurity efforts, especially in improving malware detection accuracy. Automation has replaced human involvement in various applications, which has increased the risk of security threats like phishing, network intrusions, spam, and malware. To address these challenges, cybersecurity has evolved, integrating machine learning and deep learning to detect and mitigate security threats and analyze complex data. This chapter focuses on practical applications in daily life, showcasing real-time solutions for public security threats. Through insightful discussions, we highlight how machine learning and deep learning are shaping cybersecurity, emphasizing their potential for future innovations. This chapter introduces a recent cybersecurity application and explores the challenges of integrating these technologies with complementary techniques. Our goal is to promote the widespread adoption of comprehensive cyber systems to meet the growing demand for robust cybersecurity. The data in cybersecurity systems analyze the data from security incidents for learning and applying for preventing the new attack in it. Promoting flexibility in analyzing data breaches related to malicious network activities, the system helps to proactively prevent intruders from accessing the systems and promptly notifies the user of any potential threats.