Tackling Cyberattacks through AI-based Reactive Systems: A Holistic Review and Future Vision

TL;DR

This paper provides a comprehensive review of recent AI-driven cyberattack response systems, analyzing 22 key publications to identify features, gaps, and future research challenges in the field.

Contribution

It offers the first detailed survey since 2017, introducing a systematic comparison framework and highlighting research gaps and future directions in AI-based cybersecurity responses.

Findings

Identified five key features for comparing AI reaction systems

Analyzed 22 relevant publications in detail

Outlined seven research challenges and future directions

Abstract

There is no denying that the use of Information Technology (IT) is undergoing exponential growth in today's world. This digital transformation has also given rise to a multitude of security challenges, notably in the realm of cybercrime. In response to these growing threats, public and private sectors have prioritized the strengthening of IT security measures. In light of the growing security concern, Artificial Intelligence (AI) has gained prominence within the cybersecurity landscape. This paper presents a comprehensive survey of recent advancements in AI-driven threat response systems. To the best of our knowledge, the most recent survey covering the AI reaction domain was conducted in 2017. Since then, considerable literature has been published, and therefore, it is worth reviewing it. In this comprehensive survey of the state of the art reaction systems, five key features with multiple values have been identified, facilitating a homogeneous comparison between the different works. In addition, through a meticulous methodology of article collection, the 22 most relevant publications in the field have been selected. Then each of these publications has been subjected to a detailed analysis using the features identified, which has allowed for the generation of a comprehensive overview revealing significant relationships between the papers. These relationships are further elaborated in the paper, along with the identification of potential gaps in the literature, which may guide future contributions. A total of seven research challenges have been identified, pointing out these potential gaps and suggesting possible areas of development through concrete proposals.