The Cyber Security Incident Response and Reverse Engineering

Suleman, Taseer; Liaquat, Nadia

doi:10.54692/ijeci.2022.0604121

Cited by 1 publication

(1 citation statement)

References 10 publications

(12 reference statements)

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“…The future of network security practices holds immense potential for leveraging ML techniques to enhance threat detection, incident response, and risk mitigation strategies (Hassan, and Ibrahim, 2023). By embracing emerging trends in ML, exploring new research avenues, and implementing proactive strategies to address current challenges and limitations, organizations can strengthen their defense mechanisms, adapt to evolving cyber threats, and safeguard network infrastructure against sophisticated attack s (Safitra ,2023 ).…”

Section: Future Directions and Research Opportunitiesmentioning

confidence: 99%

A comprehensive review of machine learning's role in enhancing network security and threat detection

Akoh Atadoga,

Enoch Oluwademilade Sodiya,

Uchenna Joseph Umoga

et al. 2024

World J. Adv. Res. Rev.

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As network security threats continue to evolve in complexity and sophistication, there is a growing need for advanced solutions to enhance network security and threat detection capabilities. Machine learning (ML) has emerged as a powerful tool in this context, offering the potential to detect and mitigate threats in real-time by analyzing vast amounts of network data. This comprehensive review explores the role of machine learning in enhancing network security and threat detection. The review begins by providing an overview of the current landscape of network security threats and the challenges faced by traditional security approaches. It then delves into the fundamental principles of machine learning and its application to network security. Various machine learning techniques, including supervised learning, unsupervised learning, and deep learning, are discussed in detail, highlighting their strengths and limitations in the context of threat detection. Next, the review examines the application of machine learning in different aspects of network security, including intrusion detection, malware detection, anomaly detection, and behavioral analysis. Case studies and real-world examples are presented to illustrate the effectiveness of machine learning-based approaches in identifying and mitigating security threats. Furthermore, the review discusses the challenges and considerations associated with deploying machine learning in network security environments, such as data privacy, model interpretability, and adversarial attacks. Strategies for addressing these challenges and improving the robustness of machine learning models are explored. Finally, the review outlines future research directions and opportunities for leveraging machine learning to enhance network security. Areas such as federated learning, adversarial machine learning, and explainable AI are identified as promising avenues for further investigation. In summary, this comprehensive review provides insights into the potential of machine learning in enhancing network security and threat detection. By leveraging the capabilities of machine learning algorithms and techniques, organizations can strengthen their defenses against cyber threats and better protect their networks and sensitive data.

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Section: Future Directions and Research Opportunitiesmentioning

confidence: 99%