Adversarial Machine Learning for Cybersecurity Defense

EasyChair Preprint 13996

Abstract

Machine learning (ML) has emerged as a powerful tool in the field of cybersecurity defense, aiding in the detection and prevention of various cyber threats. However, adversaries have also recognized the potential of ML and are now employing sophisticated techniques to evade detection and exploit vulnerabilities.

This paper presents an in-depth analysis of adversarial machine learning (AML) in the context of cybersecurity defense. AML involves the study and development of techniques that enable ML models to withstand attacks from adversaries seeking to manipulate or deceive the system. The objective is to enhance the robustness and resilience of ML-based cybersecurity systems, ensuring their effectiveness against evolving threats.

The paper examines the different types of attacks that ML models are susceptible to, including evasion attacks, poisoning attacks, and data integrity attacks. It explores the motivations behind these attacks and the potential consequences for cybersecurity systems. Additionally, the paper presents a comprehensive review of existing defense mechanisms and countermeasures that have been proposed to mitigate the impact of adversarial attacks.

Furthermore, the paper discusses the challenges and limitations associated with AML, highlighting the need for ongoing research and development in this area. It emphasizes the importance of a proactive approach to cybersecurity defense, where ML models are continuously trained and adapted to anticipate and counter adversarial attacks.

Keyphrases: Cybersecurity, learning, machine

@booklet{EasyChair:13996,
  author    = {Favour Olaoye and Lucas Doris and Selorm Adablanu},
  title     = {Adversarial Machine Learning for Cybersecurity Defense},
  howpublished = {EasyChair Preprint 13996},
  year      = {EasyChair, 2024}}