mlsanityData to 14 Oct 2026

Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017

arXiv 2506.19877 ↗First posted 23 Jun 2025cs.CR cs.AI cs.LGcited by 4

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Abstract (arXiv)

Identifying suitable machine learning paradigms for intrusion detection remains critical for building effective and generalizable security solutions. In this study, we present a controlled comparison of four representative models - Multi-Layer Perceptron (MLP), 1D Convolutional Neural Network (CNN), One-Class Support Vector Machine (OCSVM) and Local Outlier Factor (LOF) - on the CICIDS2017 dataset under two scenarios: detecting known attack types and generalizing to previously unseen threats. Our results show that supervised MLP and CNN achieve near-perfect accuracy on familiar attacks but suffer drastic recall drops on novel attacks. Unsupervised LOF attains moderate overall accuracy and high recall on unknown threats at the cost of elevated false alarms, while boundary-based OCSVM balances precision and recall best, demonstrating robust detection across both scenarios. These findings offer practical guidance for selecting IDS models in dynamic network environments.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 23 Jun 2025.

Builds on

11 of 26 references.

References · 11

  1. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  2. 2024Post-Training Attribute Unlearning in Recommender Systemscited by 24
  3. 2025Evaluating Supervised Learning Models for Fraud Detection: A Comparative Study of Classical and Deep Architectures on Imbalanced Transaction Datacited by 20
  4. 2025SETransformer: A Hybrid Attention-Based Architecture for Robust Human Activity Recognitioncited by 19
  5. 2023Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systemscited by 15
  6. 2024DDPM-MoCo: Advancing Industrial Surface Defect Generation and Detection with Generative and Contrastive Learningcited by 10
  7. 2022A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detectioncited by 5
  8. 2024DRIVE: Dependable Robust Interpretable Visionary Ensemble Framework in Autonomous Drivingcited by 5
  9. 2025Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Reviewcited by 4
  10. 2025Research on Enhancing Cloud Computing Network Security using Artificial Intelligence Algorithmscited by 4
  11. 2024Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Modelscited by 3

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

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  2. 2025Modality-Aware Infrared and Visible Image Fusion with Target-Aware Supervisioncited by 1
  3. 2025Enhanced Pre-training of Graph Neural Networks for Million-Scale Heterogeneous Graphscited by 0
  4. 2025Latent Sculpting for Zero-Shot Generalization: A Manifold Learning Approach to Out-of-Distribution Anomaly Detectioncited by 0