Network Intrusion Detection

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

14 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 67 papers with code (261 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Deep Anomaly Detection with Deviation Networks
  19 Nov 2019 6 repositories listed
  Instead of representation learning, our method fulfills an end-to-end learning of anomaly scores by a neural deviation learning, in which we leverage a few (e.
• Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security
  8 Oct 2018 5 repositories listed
  In this paper, DNNs have been utilized to predict the attacks on Network Intrusion Detection System (N-IDS).
• E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
  30 Mar 2021 3 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
• AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
  10 Feb 2020 3 repositories listed Syntology ran 5 of 14 samples · 9 unverified
  In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node…
• Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
  13 Jun 2018 3 repositories listed Syntology ran 5 of 5 samples · 0 unverified · 5 pointer-only (licence)
  However, existing unsupervised representation learning methods mainly focus on preserving the data regularity information and learning the representations independently of subsequent outlier detection methods, which can…
• Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
  25 Feb 2018 3 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  In this paper, we present Kitsune: a plug and play NIDS which can learn to detect attacks on the local network, without supervision, and in an efficient online manner.
• IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
  17 Oct 2023 2 repositories listed
  Previous research on behavior-based attack detection for networks of IoT devices has resulted in machine learning models whose ability to adapt to unseen data is limited and often not demonstrated.
• Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 Dataset
  1 Jan 2020 2 repositories listed
  The conclusion was made that it is possible to use machine learning methods to detect computer attacks taking into account these limitations.
• A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
  9 Jun 2018 2 repositories listed
  This manuscript aims to pinpoint research gaps and shortcomings of current datasets, their impact on building Network Intrusion Detection Systems (NIDS) and the growing number of sophisticated threats.
• A Robust PPO-optimized Tabular Transformer Framework for Intrusion Detection in Industrial IoT Systems
  23 May 2025 1 repository listed
  In this paper, we propose a robust and reinforcement-learning-enhanced network intrusion detection system (NIDS) designed for class-imbalanced and few-shot attack scenarios in Industrial Internet of Things (IIoT)…
• CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection
  2 Mar 2025 1 repository listed
  Cybersecurity threats are growing, making network intrusion detection essential.
• Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems
  16 Feb 2025 1 repository listed
  In this work, we investigate the potential impact of QML on cybersecurity applications of traditional ML.
• A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security
  14 Jan 2025 1 repository listed
  New research focuses on creating artificial intelligence (AI) solutions for network intrusion detection systems (NIDS), drawing its inspiration from the ever-growing number of intrusions on networked systems, increasing…
• Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection
  20 Dec 2024 1 repository listed
  Our approach features a strategic sample selection algorithm to select representative new samples and a strategic forgetting mechanism to drop outdated samples.
• PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
  11 Dec 2024 1 repository listed Syntology ran 1 of 1 samples · 0 unverified
  Outlier detection (OD), also known as anomaly detection, is a critical machine learning (ML) task with applications in fraud detection, network intrusion detection, clickstream analysis, recommendation systems, and…
• SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type Classification
  29 Oct 2024 1 repository listed
  Intrusion detection system (IDS) is a piece of hardware or software that looks for malicious activity or policy violations in a network.
• Implementing Lightweight Intrusion Detection System on Resource Constrained Devices
  28 Oct 2024 1 repository listed
  The rapid growth of Internet of Things (IoT) devices has increased the risk of network intrusions, which need effective security solutions for devices with low computational capability.
• A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection Systems
  21 Oct 2024 1 repository listed
  Our framework encompasses the loading of input datasets, training of individual models and ensemble methods, and the generation of evaluation metrics.
• FedMSE: Federated learning for IoT network intrusion detection
  18 Oct 2024 1 repository listed
  This approach enhances the performance of intrusion detection by effectively representing normal network data and accurately identifying anomalies in the decentralized strategy.
• XAI-based Feature Selection for Improved Network Intrusion Detection Systems
  14 Oct 2024 1 repository listed
  Explainability and evaluation of AI models are crucial parts of the security of modern intrusion detection systems (IDS) in the network security field, yet they are lacking.
• Data Distribution Valuation
  6 Oct 2024 1 repository listed Syntology ran 12 of 13 samples · 1 unverified
  Data valuation is a class of techniques for quantitatively assessing the value of data for applications like pricing in data marketplaces.
• Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection
  27 Sep 2024 1 repository listed
  This demonstrates the effectiveness of EnCNN in real-time network intrusion detection, offering a robust solution for identifying and mitigating security threats, and enhancing overall network resilience.
• XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model
  27 Aug 2024 1 repository listed
  This paper introduces "XG-NID," a novel framework that, to the best of our knowledge, is the first to fuse flow-level and packet-level data within a heterogeneous graph structure, offering a comprehensive analysis of…
• Diffusion-based Adversarial Purification for Intrusion Detection
  25 Jun 2024 1 repository listed
  The escalating sophistication of cyberattacks has encouraged the integration of machine learning techniques in intrusion detection systems, but the rise of adversarial examples presents a significant challenge.
• PolyLUT-Add: FPGA-based LUT Inference with Wide Inputs
  7 Jun 2024 1 repository listed
  FPGAs have distinct advantages as a technology for deploying deep neural networks (DNNs) at the edge.
• Large Language Models for Cyber Security: A Systematic Literature Review
  8 May 2024 1 repository listed Syntology ran 3 of 3 samples · 0 unverified
  Overall, our survey provides a comprehensive overview of the current state-of-the-art in LLM4Security and identifies several promising directions for future research.
• Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural Networks
  18 Mar 2024 1 repository listed
  Machine Learning (ML) algorithms have become increasingly popular for supporting Network Intrusion Detection Systems (NIDS).
• Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
  3 Mar 2024 1 repository listed
  To the best of our knowledge, it is the first GNN-based self-supervised method for the multiclass classification of network flows in NIDS.
• On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection
  15 Feb 2024 1 repository listed
  The results show nearly perfect classification performance when the models are trained and tested on the same dataset.
• Deep Learning Applications for Intrusion Detection in Network Traffic
  13 Jan 2024 1 repository listed
  The CNN-BiLSTM neural network is synthesized to assess the applicability of deep learning methods for intrusion detection.

Syntology lines on 7 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.