Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection

• URL: http://arxiv.org/abs/2507.17161v1
• Date: Wed, 23 Jul 2025 02:53:58 GMT
• Title: Tabular Diffusion based Actionable Counterfactual Explanations for Network Intrusion Detection
• Authors: Vinura Galwaduge, Jagath Samarabandu,
• Abstract summary: We propose a novel diffusion-based counterfactual explanation framework that can provide actionable explanations for network intrusion attacks.<n>Our proposed method provide minimal, diverse counterfactual explanations out of the tested counterfactual explanation algorithms.<n>We also demonstrate how counterfactual explanations can provide actionable explanations by summarizing them to create a set of global rules.
• Score: 0.45060992929802207
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern network intrusion detection systems (NIDS) frequently utilize the predictive power of complex deep learning models. However, the "black-box" nature of such deep learning methods adds a layer of opaqueness that hinders the proper understanding of detection decisions, trust in the decisions and prevent timely countermeasures against such attacks. Explainable AI (XAI) methods provide a solution to this problem by providing insights into the causes of the predictions. The majority of the existing XAI methods provide explanations which are not convenient to convert into actionable countermeasures. In this work, we propose a novel diffusion-based counterfactual explanation framework that can provide actionable explanations for network intrusion attacks. We evaluated our proposed algorithm against several other publicly available counterfactual explanation algorithms on 3 modern network intrusion datasets. To the best of our knowledge, this work also presents the first comparative analysis of existing counterfactual explanation algorithms within the context of network intrusion detection systems. Our proposed method provide minimal, diverse counterfactual explanations out of the tested counterfactual explanation algorithms in a more efficient manner by reducing the time to generate explanations. We also demonstrate how counterfactual explanations can provide actionable explanations by summarizing them to create a set of global rules. These rules are actionable not only at instance level but also at the global level for intrusion attacks. These global counterfactual rules show the ability to effectively filter out incoming attack queries which is crucial for efficient intrusion detection and defense mechanisms.

Related papers

• How Explanations Leak the Decision Logic: Stealing Graph Neural Networks via Explanation Alignment [9.329315232799814]
  Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial analysis.<n>Recent advances in explainable GNNs have addressed this need by revealing important subgraphs that influence predictions.<n>This paper investigates how such explanations potentially leak critical decision logic that can be exploited for model stealing.
  arXiv  Detail & Related papers  (2025-06-03T17:11:05Z)
• Detecting, Explaining, and Mitigating Memorization in Diffusion Models [49.438362005962375]
  We introduce a straightforward yet effective method for detecting memorized prompts by inspecting the magnitude of text-conditional predictions. Our proposed method seamlessly integrates without disrupting sampling algorithms, and delivers high accuracy even at the first generation step. Building on our detection strategy, we unveil an explainable approach that shows the contribution of individual words or tokens to memorization.
  arXiv  Detail & Related papers  (2024-07-31T16:13:29Z)
• Revealing Vulnerabilities of Neural Networks in Parameter Learning and Defense Against Explanation-Aware Backdoors [2.1165011830664673]
  Blinding attacks can drastically alter a machine learning algorithm's prediction and explanation. We leverage statistical analysis to highlight the changes in CNN weights within a CNN following blinding attacks. We introduce a method specifically designed to limit the effectiveness of such attacks during the evaluation phase.
  arXiv  Detail & Related papers  (2024-03-25T09:36:10Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)
• Adv-Bot: Realistic Adversarial Botnet Attacks against Network Intrusion Detection Systems [0.7829352305480285]
  A growing number of researchers are recently investigating the feasibility of such attacks against machine learning-based security systems. This study was to investigate the actual feasibility of adversarial attacks, specifically evasion attacks, against network-based intrusion detection systems. Our goal is to create adversarial botnet traffic that can avoid detection while still performing all of its intended malicious functionality.
  arXiv  Detail & Related papers  (2023-03-12T14:01:00Z)
• ExAD: An Ensemble Approach for Explanation-based Adversarial Detection [17.455233006559734]
  We propose ExAD, a framework to detect adversarial examples using an ensemble of explanation techniques. We evaluate our approach using six state-of-the-art adversarial attacks on three image datasets.
  arXiv  Detail & Related papers  (2021-03-22T00:53:07Z)
• Explaining Network Intrusion Detection System Using Explainable AI Framework [0.5076419064097734]
  Intrusion detection system is one of the important layers in cyber safety in today's world. In this paper, we have used deep neural network for network intrusion detection. We also proposed explainable AI framework to add transparency at every stage of machine learning pipeline.
  arXiv  Detail & Related papers  (2021-03-12T07:15:09Z)
• Increasing the Confidence of Deep Neural Networks by Coverage Analysis [71.57324258813674]
  This paper presents a lightweight monitoring architecture based on coverage paradigms to enhance the model against different unsafe inputs. Experimental results show that the proposed approach is effective in detecting both powerful adversarial examples and out-of-distribution inputs.
  arXiv  Detail & Related papers  (2021-01-28T16:38:26Z)
• Attack Agnostic Adversarial Defense via Visual Imperceptible Bound [70.72413095698961]
  This research aims to design a defense model that is robust within a certain bound against both seen and unseen adversarial attacks. The proposed defense model is evaluated on the MNIST, CIFAR-10, and Tiny ImageNet databases. The proposed algorithm is attack agnostic, i.e. it does not require any knowledge of the attack algorithm.
  arXiv  Detail & Related papers  (2020-10-25T23:14:26Z)
• A black-box adversarial attack for poisoning clustering [78.19784577498031]
  We propose a black-box adversarial attack for crafting adversarial samples to test the robustness of clustering algorithms. We show that our attacks are transferable even against supervised algorithms such as SVMs, random forests, and neural networks.
  arXiv  Detail & Related papers  (2020-09-09T18:19:31Z)
• Open-set Adversarial Defense [93.25058425356694]
  We show that open-set recognition systems are vulnerable to adversarial attacks. Motivated by this observation, we emphasize the need of an Open-Set Adrial Defense (OSAD) mechanism. This paper proposes an Open-Set Defense Network (OSDN) as a solution to the OSAD problem.
  arXiv  Detail & Related papers  (2020-09-02T04:35:33Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.