Home // International Journal On Advances in Security, volume 17, numbers 3 and 4, 2024 // View article

AI-Driven Analysis for Network Attacks: Enhancing IDS Alerts and ACL Integration

Authors:
Nader Shahata
Hirokazu Hasegawa
Masahiko Kato
Hiroki Takakura

Keywords: Access Control List; Cyber Security; Network; Intrusion Detection; Artificial Intelligence.

Abstract:
Due to the widespread deployment of digital systems, and the increasing complexity of cyber threats, it has become crucial to us to secure our resources in computer connected systems. Access Control Lists (ACLs) are fundamental frameworks that govern the authorization and authentication processes that occur in our network. Essentially, ACLs are a set of rules that define users who have permissions to access particular resources. Furthermore, ACLs indicate whether a user's access will be permitted and what specific actions they will be able to perform. Access control lists play a vital role in the security and confidentiality of sensitive information and resources. However, the emergence of artificial intelligence has the ability to transform the process of access control lists which may result in securing our network. When the system manages the network traffic with the generated ACL, it will enable the network analysts to track certain threats first without having to monitor all network traffic. This method will allow for more efficient threat detection and analysis ending up with saving time and resources. In this paper, we will discuss the usefulness of artificial intelligence and its role in generating access control lists and the consequences of using such technology in securing our network.

Pages: 115 to 124

Copyright: Copyright (c) to authors, 2024. Used with permission.

Publication date: December 30, 2024

Published in: journal

ISSN: 1942-2636