preprints.org > computer science and mathematics > security systems > doi: 10.20944/preprints202312.0205.v1

Preprint Review Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 4 December 2023 / Approved: 4 December 2023 / Online: 5 December 2023 (04:25:34 CET)
Version 2 : Received: 30 December 2023 / Approved: 2 January 2024 / Online: 3 January 2024 (02:05:06 CET)

In today's digital landscape, cybersecurity has become a priority, with attacks becoming increasingly sophisticated. Traditional security approaches are no longer enough, requiring a more dynamic and advanced response. In this context, integrating Artificial Intelligence (AI) and Machine Learning (ML) appears key to addressing this growing threat. However, despite their high effectiveness, there is a developed problem: the integration of various data sources and technologies for comprehensive protection. This article presents an in-depth review of integrating Artificial Intelligence and Ma-chine Learning in cybersecurity, focusing particularly on Open Extended Detection and Response (Open XDR) technology. The methodology used is a detailed literature review, examining how various cybersecurity components interact and function. These components include Intrusion Detection Systems (IDS), which monitor networks for malicious activities; Endpoint Detection and Response (EDR), which focuses on detecting and investigating security incidents on endpoints; and Security Information and Event Management (SIEM), systems that provide real-time analysis of security alerts. The review also considers the role of Active Directory, a directory service for Windows domain networks, and the process of log forwarding, where log files are transmitted to a central server for analysis, in the context of AI and ML. The paper delves into the development of AI and ML, underscoring their roles in cybersecurity for advanced data processing, pattern recognition, and predicting threats. It explores both supervised (where the model is trained on labeled data) and unsupervised learning (where the model learns from unlabeled data) in ML, and how these techniques bolster cybersecurity measures. The article highlights the significance of Open XDR as a critical innovation that integrates data from multiple sources for comprehensive security analysis. Further, the review discusses how the integration of AI and ML into various cybersecurity tools, such as IDS, EDR, and SIEM, augments capabilities in threat detection and response. It addresses the challenges and opportunities that AI and ML present in the cybersecurity domain, focusing on ethical issues, data privacy concerns, and the necessity for ongoing professional development in this rapidly advancing field. The paper concludes by affirming the effectiveness of merging AI and ML with these cybersecurity tools within the Open XDR framework. This integration significantly enhances threat detection, response efficiency, and overall cybersecurity resilience. The aim is to provide a comprehensive understanding of the current state of cybersecurity technologies, their interaction with AI and ML, and insights into the field's future developments.

Artificial Intelligence; Machine Learning; Open Extended Detection and Response; Intrusion Detection Systems; Endpoint Detection and Response; Security Information and Event Management; Threat Detection and Response

Computer Science and Mathematics, Security Systems

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