Cybersecurity Threat Detection using Machine Learning and Network Analysis

Amaresh Kumar ()

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023, 2024, vol. 1, issue 1, 124-131

Abstract: Cybercriminals continually develop innovative strategies to confound and frustrate their victims, necessitating constant vigilance to protect the availability, confidentiality, and integrity of digital systems. Machine learning (ML) has emerged as a powerful technique for intelligent cyber analysis, enabling proactive defenses by studying recurring patterns of successful attacks. However, two significant drawbacks hinder the widespread adoption of ML in security analysis: high computing overheads and the need for specialized frameworks. This study aims to quantify the extent to which a hub can enhance ecosystem safety. Typical cyberattacks were executed on an Internet of Things (IoT) network within a smart house to validate the hub's efficacy. Furthermore, the resistance of the intrusion detection system (IDS) to adversarial machine learning (AML) attacks was investigated, where models are targeted with adversarial samples exploiting weaknesses in the pre-trained detector.

Keywords: Intrusion Detection Systems; Adversarial Machine Learning; Internet of Things; Cyber-Physical Systems (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://newjaigs.com/index.php/JAIGS/article/view/88 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:das:njaigs:v:1:y:2024:i:1:p:124-131:id:88

Access Statistics for this article

Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 is currently edited by Justyna Żywiołek

More articles in Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 from Open Knowledge
Bibliographic data for series maintained by Open Knowledge ().