Intelligent Incident Response Systems Using Machine Learning

Neibo Augustine Olobo, Waliu Adebayo Ayuba, Abiamamela Obi-Obuoha, Izevbigie Hope Iyobosa, Aderemi Ibraheem Adebayo, Ishiwu Ifeanyichukwu Jude, Chioma Jessica Ifechukwu

Abstract

Machine learning (ML) is revolutionising cybersecurity by enhancing the ability to predict, detect, and respond to cyber threats. By leveraging advanced algorithms, ML systems can analyse vast datasets in real-time, identify patterns, and automate responses, addressing the challenges of increasingly sophisticated cyberattacks. This paper explores the transformative impact of machine learning in cybersecurity, highlighting key tasks such as classification, anomaly detection, and natural language processing. It also discusses future research directions, including explainable AI, adversarial machine learning, federated learning, and privacy-preserving techniques. The cybersecurity community can develop more robust and adaptive defences by focusing on these innovative areas, ensuring a safer digital environment. Integrating machine learning into cybersecurity practices is crucial for navigating the evolving threat landscape and maintaining trust in digital systems.

10.22178/pos.112-13


Keywords


Intelligent Incident Response; Machine Learning; Threat Detection; Automated Response; Predictive Analytics

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References


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Copyright (c) 2024 Neibo Augustine Olobo, Waliu Adebayo Ayuba, Abiamamela Obi-Obuoha, Izevbigie Hope Iyobosa, Aderemi Ibraheem Adebayo, Ishiwu Ifeanyichukwu Jude, Chioma Jessica Ifechukwu

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