Adversarial Machine Learning for Robust Intrusion Detection Systems - Slideshare 275693314

Aug 3, 2026

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Adversarial machine learning is used to strengthen intrusion detection systems’ ability to counter sophisticated cyberattacks and reduce susceptibility to adversarial evasion. The work outlines practical challenges for deploying adversarial techniques, methods for performance measurement, and ethical considerations. It proposes a multi-level pipeline for detecting artifacts, building complex models, and collecting data. Key findings emphasize the aggressiveness trade-offs of privacy-preserving methods, the need for new performance metrics, and the tension between robustness and detection performance across network contexts.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Literature review

Android malware classification with adversarial machine learning

Image forensics and adversarial vulnerabilities

Methods

Data collection and data processing

Designing machine learning models

| Adversarial Machine Learning for Robust Intrusion
Detection Systems
Akhil Mittal
Independent Researcher,USA
Pandi Kirupa Gopalakrishna Pandian
Independent Researcher,USA. | | --- | | Abstract: In this study, adversarial machine learning to enhance IDS’s capability to counterattack sophisticated cyberattacks employed in the investigation. This paper describes challenges in practice of adversarial techniques, performance measurement and | | ethical issues. In the research proposal, the authors describe the comprehensive and multi-level method of detecting artifacts, building complex models, and gathering data. Researchers stressed important conclusions regarding aggressiveness of privacypreserving methods, the need for developing new performance metrics, and the tension between robust model and detection | |
performance. The research assists in developing IDS that are both efficient and formally correct in various contexts of a network. | | Keywords: Intrusion,detection, systems, adversarial, Machine Learning, develop, robust | Introduction Intrusion detection systems (IDS) ability to stand against other elaborate cyber-attacks is something that has become essential to improve through the use of adversarial machine learning. Better solutions are sorely missing as classical IDS become vulnerable to adversarial instances and forms of evasion. It is the objective of this project to develop a dependable IDS that would be effective at detecting and mitigating strong cyber threats with the use of adversarial machine learning. The vision is to create IDS that are robust against adversarial attacks and maintain high detection rates despite adversarial changes in the environment using adversarial training, model hardening, and adaptive defenses’integration. The work analyzes the challenges brought by malicious machine learning in IDS and provides futuristic solutions to improve the system’s stability. Literature review Android Malware Classification using Adversarial Machine Learning for Hacking According to the author, Chen [et. al](et. al). 2018, This paper explains how adversarial attacks can threaten the effectiveness of the machine learning-based Android malware detection systems. Thus, to counter such rooted-deceived programs, the authors propose KuafuDet, a two-phase iterative adversarial-based detection system with a similarity-based filter. They divide the type of attackers into three classes and prove how effective the toxin attacks are against existing defense mechanisms. Thus, in nonadversarial environments, KuafuDet achieves the result of 96%, while in adversarial environments, the result does not go below 15% . The technology is easily expandable, works, and is even better than the leading antivirus programs. It reaffirms the consideration of hostile incidents within the process of swinging mobile malware detection and introduces an inventive approach to enhance the protection from these attacks. Figure 1: Intrusion Detection network Achievements and Challenges in ML for Image Forensics According to the author, Nowroozi [et. al.](et. al. 2021)[ 2021](et. al. 2021), the paper discusses the rising relevance of picture forensics to prevent the spread of doctored images, which cause harm to criminal and civil jurisdiction. It highlights the fact that different machine learning approaches are progressively applied in picture forensics for classification, identification, and verification of pictures’ origin and integrity. However, the study also revealed that such machine learning-based defenses are very vulnerable to adversarial attacks. These restrictions may lead to rather unfair trials or, in other words, evidence that is inadmissible according to the legal norms. Thus, in image forensics, the authors highlight the need for developing good techniques to protect the learning algorithms, most of all from adversarial examples and counter-forensics tactics. Figure 2 :

FAQs

How does adversarial machine learning improve intrusion detection systems in the proposed work?

It uses adversarial training, model hardening, and adaptive defenses to help IDS remain robust and preserve high detection rates even when attackers change the environment to evade detection.

What dataset and preprocessing steps are used for model training?

The approach forms a diverse network-traffic dataset using real traces, then applies feature extraction, normalization, and cleaning. It also addresses class imbalance via undersampling/oversampling and generates adversarial examples for robustness.

What challenge is highlighted regarding evaluation and robustness?

The research stresses the need for new performance metrics and points out a tension between robust model behavior and detection performance under adversarial conditions.

Aug 3, 2026

Upload by Riley