Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions

Version 1 : Received: 21 January 2021 / Approved: 25 January 2021 / Online: 25 January 2021 (09:19:10 CET)

How to cite: Sarker, I.H. AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions. Preprints 2021, 2021010457 (doi: 10.20944/preprints202101.0457.v1). Sarker, I.H. AI-Driven Cybersecurity: An Overview, Security Intelligence Modeling and Research Directions. Preprints 2021, 2021010457 (doi: 10.20944/preprints202101.0457.v1).

Abstract

Artificial Intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (Industry 4.0), which can be used for the protection of Internet-connected systems from cyber-threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving Machine Learning (ML) and Deep Learning (DL) methods, the concept of Natural Language Processing (NLP), Knowledge Representation and Reasoning (KRR), as well as the concept of knowledge or rule-based Expert Systems (ES) modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an AI-based technical point of view.

Subject Areas

Cybersecurity; artificial intelligence; machine learning; cyber data analytics; cyber-attacks; anomaly; intrusion detection; security intelligence

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