mlsanityData to 14 Oct 2026

Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model

arXiv 2308.00074 ↗First posted 31 Jul 2023cs.LG cs.CRcited by 10

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Abstract (arXiv)

Anomaly detection and its explanation is important in many research areas such as intrusion detection, fraud detection, unknown attack detection in network traffic and logs. It is challenging to identify the cause or explanation of why one instance is an anomaly? and the other is not due to its unbounded and lack of supervisory nature. The answer to this question is possible with the emerging technique of explainable artificial intelligence (XAI). XAI provides tools and techniques to interpret and explain the output and working of complex models such as Deep Learning (DL). This paper aims to detect and explain network anomalies with XAI, kernelSHAP method. The same approach is used to improve the network anomaly detection model in terms of accuracy, recall, precision and f score. The experiment is conduced with the latest CICIDS2017 dataset. Two models are created (Model_1 and OPT_Model) and compared. The overall accuracy and F score of OPT_Model (when trained in unsupervised way) are 0.90 and 0.76, respectively.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 8 of its 39 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

8 of 39 references.

References · 8

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2019Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AIcited by 1,552
  3. 2018A Survey Of Methods For Explaining Black Box Modelscited by 973
  4. 2019Explaining Anomalies Detected by Autoencoders Using SHAPcited by 27
  5. 2017Contextual Outlier Interpretationcited by 19
  6. 2019Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detectioncited by 19
  7. 2021Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)cited by 15
  8. 2018Instance-Level Explanations for Fraud Detection: A Case Studycited by 10

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 3; 2026: 3.

10 papers.

  1. 2023Untargeted White-box Adversarial Attack with Heuristic Defence Methods in Real-time Deep Learning based Network Intrusion Detection Systemcited by 8
  2. 2024Black-box Adversarial Transferability: An Empirical Study in Cybersecurity Perspectivecited by 3
  3. 2024Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainabilitycited by 2
  4. 2024An Approach To Enhance IoT Security In 6G Networks Through Explainable AIcited by 1
  5. 2025On Using the Shapley Value for Anomaly Localization: A Statistical Investigationcited by 1
  6. 2025FairSHAP: Preprocessing for Fairness Through Attribution-Based Data Augmentationcited by 0
  7. 2025Beyond Technocratic XAI: The Who, What & How in Explanation Designcited by 0
  8. 2026An Explainable Failure Prediction Framework for Neural Networks in Radio Access Networkscited by 0
  9. 2026Statistical Analysis of using the Shapley Value for Sensor Anomaly Localization with Accurate Classifierscited by 0
  10. 2026A Multi-Model Hybrid Defense Approach Against White-box Adversarial Attacks in Computer Network Trafficcited by 0