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

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

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 151 papers with code (800 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.

• 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).
• A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
  10 Sep 2017 5 repositories listed
  Conventionally, like most neural networks, both of the aforementioned RNN variants employ the Softmax function as its final output layer for its prediction, and the cross-entropy function for computing its loss.
• 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…
• 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.
• TOD: GPU-accelerated Outlier Detection via Tensor Operations
  26 Oct 2021 2 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection.
• Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks
  6 Feb 2021 2 repositories listed
  In this paper, we present an intrusion detection method for detecting audio-video transport protocol (AVTP) stream injection attacks in automotive Ethernet-based networks.
• SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure
  27 May 2020 2 repositories listed
  Ensuring safety and explainability of machine learning (ML) is a topic of increasing relevance as data-driven applications venture into safety-critical application domains, traditionally committed to high safety…
• Cyber Attack Detection thanks to Machine Learning Algorithms
  17 Jan 2020 2 repositories listed
  The Random Forest Classifier succeeds in detecting more than 95% of the botnets in 8 out of 13 scenarios and more than 55% in the most difficult datasets.
• Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems
  4 Aug 2018 2 repositories listed
  Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields.
• 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.
• eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys
  27 Feb 2017 2 repositories listed
  For years security machine learning research has promised to obviate the need for signature based detection by automatically learning to detect indicators of attack.
• 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)…
• Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge
  20 May 2025 1 repository listed
  Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network.
• SecCAN: An Extended CAN Controller with Embedded Intrusion Detection
  20 May 2025 1 repository listed
  Here, initiating IDS requires complete reception of a CAN message from the controller, incurring data movement and software overheads.
• Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems
  12 May 2025 1 repository listed
  Unlike traditional methods reliant on handcrafted statistical features (NetFlow), our approach automatically learns comprehensive packet sequence representations, significantly enhancing performance in anomaly…
• Cyber Security Data Science: Machine Learning Methods and their Performance on Imbalanced Datasets
  7 May 2025 1 repository listed
  Therefore, we recommend testing single classifiers and imbalance learning techniques for each new dataset and application involving imbalanced datasets as is the case in several cyber security applications.
• Simplified and Secure MCP Gateways for Enterprise AI Integration
  28 Apr 2025 1 repository listed
  The increased adoption of the Model Context Protocol (MCP) for AI Agents necessitates robust security for Enterprise integrations.
• CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection
  2 Apr 2025 1 repository listed
  The proposed DFL framework, which is scalable and privacy-preserving, is based on a federation process that allows multiple entities to train online their local models using incoming DoH flows in real time as they are…
• 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.
• Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis
  28 Feb 2025 1 repository listed
  In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models.
• Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks
  28 Feb 2025 1 repository listed
  The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology.
• 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.
• Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security Research
  5 Feb 2025 1 repository listed
  In this paper, a dataset of IoT network traffic is presented.
• Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS
  31 Jan 2025 1 repository listed
  By incorporating CRYSTALS-Kyber, the framework mitigates vulnerabilities in ECC against quantum attacks, positioning it as a quantum-resistant alternative.
• 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.

Syntology lines on 5 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.