GMA-SAWGAN-GP: A Novel Data Generative Framework to Enhance IDS Detection Performance

• URL: http://arxiv.org/abs/2603.28838v1
• Date: Mon, 30 Mar 2026 14:35:23 GMT
• Title: GMA-SAWGAN-GP: A Novel Data Generative Framework to Enhance IDS Detection Performance
• Abstract summary: Intrusion Detection System (IDS) is often calibrated to known attacks and generalizes poorly to unknown threats.<n>This paper proposes GMA-SAWGAN-GP, a novel generative augmentation framework built on a Self-Attention-enhanced Wasserstein GAN with Gradient Penalty.
• Score: 0.2530367162206854
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intrusion Detection System (IDS) is often calibrated to known attacks and generalizes poorly to unknown threats. This paper proposes GMA-SAWGAN-GP, a novel generative augmentation framework built on a Self-Attention-enhanced Wasserstein GAN with Gradient Penalty (WGAN-GP). The generator employs Gumbel-Softmax regularization to model discrete fields, while a Multilayer Perceptron (MLP)-based AutoEncoder acts as a manifold regularizer. A lightweight gating network adaptively balances adversarial and reconstruction losses via entropy regularization, improving stability and mitigating mode collapse. The self-attention mechanism enables the generator to capture both short- and long-range dependencies among features within each record while preserving categorical semantics through Gumbel-Softmax heads. Extensive experiments on NSL-KDD, UNSW-NB15, and CICIDS2017 using five representative IDS models demonstrate that GMA-SAWGAN-GP significantly improves detection performance on known attacks and enhances generalization to unknown attacks. Leave-One-Attack-type-Out (LOAO) evaluations using Area Under the Receiver Operating Characteristic (AUROC) and True Positive Rate at a 5 percent False Positive Rate confirm that IDS models trained on augmented datasets achieve higher robustness under unseen attack scenarios. Ablation studies validate the contribution of each component to performance gains. Compared with baseline models, the proposed framework improves binary classification accuracy by an average of 5.3 percent and multi-classification accuracy by 2.2 percent, while AUROC and True Positive Rate at a 5 percent False Positive Rate for unknown attacks increase by 3.9 percent and 4.8 percent, respectively, across the three datasets. Overall, GMA-SAWGAN-GP provides an effective approach to generative augmentation for mixed-type network traffic, improving IDS accuracy and resilience.

Related papers

• Towards Understanding the Robustness of Sparse Autoencoders [13.16745936025085]
  We present a study of integrating pretrained SAEs into transformer residual streams at inference time.<n>SAE-augmented models achieve up to a 5x reduction in jailbreak success rate relative to the undefended baseline.
  arXiv  Detail & Related papers  (2026-04-20T19:00:09Z)
• Unifying Group-Relative and Self-Distillation Policy Optimization via Sample Routing [79.88256756334327]
  Self-Distillation Policy Optimization (SDPO) addresses this by providing denser, more targeted logit-level supervision.<n>We propose Sample-Routed Policy Optimization (SRPO)<n>SRPO routes correct samples to GRPO's reward-aligned reinforcement and failed samples to SDPO's targeted logit-level correction.
  arXiv  Detail & Related papers  (2026-04-02T17:29:18Z)
• A Novel Solution for Zero-Day Attack Detection in IDS using Self-Attention and Jensen-Shannon Divergence in WGAN-GP [0.2530367162206854]
  Cyber threats, especially zero-day attacks, pose a significant challenge to cybersecurity.<n>Existing approaches patch flaws and deploy an Intrusion Detection System (IDS)<n>This paper makes a novel proposition to synthesize network traffic that mimics zero-day patterns.
  arXiv  Detail & Related papers  (2026-03-19T17:51:08Z)
• FGGM: Fisher-Guided Gradient Masking for Continual Learning [57.56585138260662]
  Catastrophic forgetting impairs the continuous learning of large language models.<n>We propose Fisher-Guided Gradient Masking (FGGM), a framework that mitigates this by strategically selecting parameters for updates using diagonal Fisher Information.
  arXiv  Detail & Related papers  (2026-01-26T08:35:34Z)
• Smooth Operator: Smooth Verifiable Reward Activates Spatial Reasoning Ability of Vision-Language Model [18.526821056010384]
  Vision-Language Models (VLMs) face a critical bottleneck in achieving precise numerical prediction for 3D scene understanding.<n>Traditional reinforcement learning approaches, primarily based on relative ranking, often suffer from severe reward sparsity and gradient instability.<n>We introduce the Smooth Numerical Reward Activation (SNRA) operator and the Absolute-Preserving GRPO framework.
  arXiv  Detail & Related papers  (2026-01-12T16:26:42Z)
• Hybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments [0.0]
  Federated Learning (FL) in 5G and edge network environments face severe security threats from adversarial clients.<n>This paper introduces Hybrid Reputation Aggregation (HRA), a novel robust aggregation mechanism designed to defend against adversarial behaviors in FL without prior knowledge of the attack type.<n>HRA combines geometric anomaly detection with momentum-based reputation tracking of clients.
  arXiv  Detail & Related papers  (2025-09-22T17:18:59Z)
• ARGOS: Anomaly Recognition and Guarding through O-RAN Sensing [3.018691733760647]
  Rogue Base Station (RBS) attacks, particularly those exploiting downgrade vulnerabilities, remain a persistent threat.<n>This work introduces ARGOS, a comprehensive O-RAN compliant Intrusion Detection System (IDS) deployed within the Near Real-Time RIC.<n>ARGOS detects RBS downgrade attacks in real time, an area previously unexplored within the O-RAN context.
  arXiv  Detail & Related papers  (2025-06-07T20:32:23Z)
• Normalized Attention Guidance: Universal Negative Guidance for Diffusion Models [57.20761595019967]
  We present Normalized Attention Guidance (NAG), an efficient, training-free mechanism that applies extrapolation in attention space with L1-based normalization and refinement.<n>NAG restores effective negative guidance where CFG collapses while maintaining fidelity.<n>NAG generalizes across architectures (UNet, DiT), sampling regimes (few-step, multi-step), and modalities (image, video)
  arXiv  Detail & Related papers  (2025-05-27T13:30:46Z)
• UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation [93.38604803625294]
  We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) We use Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. UncertaintyRAG outperforms baselines by 2.03% on LLaMA-2-7B, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-03T17:39:38Z)
• HGAttack: Transferable Heterogeneous Graph Adversarial Attack [63.35560741500611]
  Heterogeneous Graph Neural Networks (HGNNs) are increasingly recognized for their performance in areas like the web and e-commerce. This paper introduces HGAttack, the first dedicated gray box evasion attack method for heterogeneous graphs.
  arXiv  Detail & Related papers  (2024-01-18T12:47:13Z)
• GReAT: A Graph Regularized Adversarial Training Method [0.0]
  GReAT (Graph Regularized Adversarial Training) is a novel regularization method designed to enhance the robust classification performance of deep learning models. GReAT integrates graph based regularization into the adversarial training process, leveraging the data's inherent structure to enhance model robustness.
  arXiv  Detail & Related papers  (2023-10-09T01:44:06Z)
• GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models [60.48306899271866]
  We present a new framework, called GREAT Score, for global robustness evaluation of adversarial perturbation using generative models. We show high correlation and significantly reduced cost of GREAT Score when compared to the attack-based model ranking on RobustBench. GREAT Score can be used for remote auditing of privacy-sensitive black-box models.
  arXiv  Detail & Related papers  (2023-04-19T14:58:27Z)

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.