An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques

• URL: http://arxiv.org/abs/2403.07959v2
• Date: Tue, 05 Nov 2024 07:14:25 GMT
• Title: An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques
• Abstract summary: Interpretable Generalization Mechanism (IG) discerns coherent patterns, making it interpretable in distinguishing between normal and anomalous network traffic. By experiments with real-world datasets, IG is accurate even at a low ratio of training-to-test. IG showcases superior generalization by consistently performing well across diverse datasets and training-to-test ratios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advancements in Intrusion Detection Systems (IDS), integrating Explainable AI (XAI) methodologies, have led to notable improvements in system performance via precise feature selection. However, a thorough understanding of cyber-attacks requires inherently explainable decision-making processes within IDS. In this paper, we present the Interpretable Generalization Mechanism (IG), poised to revolutionize IDS capabilities. IG discerns coherent patterns, making it interpretable in distinguishing between normal and anomalous network traffic. Further, the synthesis of coherent patterns sheds light on intricate intrusion pathways, providing essential insights for cybersecurity forensics. By experiments with real-world datasets NSL-KDD, UNSW-NB15, and UKM-IDS20, IG is accurate even at a low ratio of training-to-test. With 10%-to-90%, IG achieves Precision (PRE)=0.93, Recall (REC)=0.94, and Area Under Curve (AUC)=0.94 in NSL-KDD; PRE=0.98, REC=0.99, and AUC=0.99 in UNSW-NB15; and PRE=0.98, REC=0.98, and AUC=0.99 in UKM-IDS20. Notably, in UNSW-NB15, IG achieves REC=1.0 and at least PRE=0.98 since 40%-to-60%; in UKM-IDS20, IG achieves REC=1.0 and at least PRE=0.88 since 20%-to-80%. Importantly, in UKM-IDS20, IG successfully identifies all three anomalous instances without prior exposure, demonstrating its generalization capabilities. These results and inferences are reproducible. In sum, IG showcases superior generalization by consistently performing well across diverse datasets and training-to-test ratios (from 10%-to-90% to 90%-to-10%), and excels in identifying novel anomalies without prior exposure. Its interpretability is enhanced by coherent evidence that accurately distinguishes both normal and anomalous activities, significantly improving detection accuracy and reducing false alarms, thereby strengthening IDS reliability and trustworthiness.

Related papers

• Deep Learning-based Intelligent Diagnosis of Congenital Uterine Anomalies in 3D Ultrasound [10.135095835279495]
  We develop an intelligent framework, termed CUA-Net, for the automated classification of congenital uterine anomalies (CUA)<n> CUA-Net was built on 3D ResNet-18, equipped with a dynamic data resampling strategy to mitigate the data imbalance issue.<n>We compared the CUA-Net with different deep-learning methods and junior/senior sonographers in the testing set.
  arXiv  Detail & Related papers  (2026-09-14T08:44:47Z)
• Are we still able to recognize pearls? Machine-driven peer review and the risk to creativity: An explainable RAG-XAI detection framework with markers extraction [7.723181091241251]
  We introduce an explainable framework (RAG-XAI) for assessing review quality and detecting automated patterns.<n>The framework achieves near-perfect detection performance, with XGBoost, Random Forest and LightGBM reaching 99.61% accuracy, AUC-ROC above 0.999 and F1-scores of 0.9925 on the test set.
  arXiv  Detail & Related papers  (2026-04-09T08:25:49Z)
• L-RAG: Balancing Context and Retrieval with Entropy-Based Lazy Loading [0.0]
  Reval-Augmented Generation (RAG) has emerged as the predominant paradigm for grounding Large Language outputs in factual knowledge.<n>We introduce L-RAG, an adaptive framework that implements hierarchical context management through entropy gating.<n>L-RAG offers a practical, training-free approach toward more efficient RAG deployment.
  arXiv  Detail & Related papers  (2026-01-10T12:25:19Z)
• Differentially Private Linear Regression and Synthetic Data Generation with Statistical Guarantees [41.99844472131922]
  In social sciences, small- to medium-scale datasets are common and linear regression (LR) is canonical.<n>In privacy-aware settings, much work has focused on differentially private (DP) LR, but mostly on point estimation with limited attention to uncertainty quantification.
  arXiv  Detail & Related papers  (2025-10-19T19:30:41Z)
• VISION: Robust and Interpretable Code Vulnerability Detection Leveraging Counterfactual Augmentation [6.576811224645293]
  Graph Neural Networks (GNNs) can learn structural and logical code relationships in a data-driven manner.<n>GNNs often learn'spurious' correlations from superficial code similarities.<n>We propose a unified framework for robust and interpretable vulnerability detection, called VISION.
  arXiv  Detail & Related papers  (2025-08-26T11:20:39Z)
• Multi-Granular Discretization for Interpretable Generalization in Precise Cyberattack Identification [0.0]
  Interpretable Generalization (IG) mechanism is used to learn coherent patterns.<n>IG-MD represents every continuous feature at several Gaussian-based resolutions.<n>On UKM-IDS20, IG-MD lifts precision by greater than or equal to 4 percentage points across all nine train-test splits.
  arXiv  Detail & Related papers  (2025-07-16T12:57:38Z)
• 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)
• Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detection [50.343419243749054]
  Anomaly detection is critical in fields such as medical diagnostics and industrial defect detection.<n> CLIP's coarse-grained image-text alignment limits localization and detection performance for fine-grained anomalies.<n>Crane improves the state-of-the-art ZSAD from 2% to 28%, at both image and pixel levels, while remaining competitive in inference speed.
  arXiv  Detail & Related papers  (2025-04-15T10:42:25Z)
• EVolutionary Independent DEtermiNistiC Explanation [5.127310126394387]
  This paper introduces the Evolutionary Independent Deterministic Explanation (EVIDENCE) theory. EVIDENCE offers a deterministic, model-independent method for extracting significant signals from black-box models. Practical applications of EVIDENCE include improving diagnostic accuracy in healthcare and enhancing audio signal analysis.
  arXiv  Detail & Related papers  (2025-01-20T12:05:14Z)
• Typicalness-Aware Learning for Failure Detection [26.23185979968123]
  Deep neural networks (DNNs) often suffer from the overconfidence issue, where incorrect predictions are made with high confidence scores. We propose a novel approach called Typicalness-Aware Learning (TAL) to address this issue and improve failure detection performance.
  arXiv  Detail & Related papers  (2024-11-04T11:09:47Z)
• SGUQ: Staged Graph Convolution Neural Network for Alzheimer's Disease Diagnosis using Multi-Omics Data [7.090283934070421]
  Alzheimer's disease (AD) is a chronic neurodegenerative disorder and the leading cause of dementia. Conventional approaches typically require the completion of all omics data at the outset to achieve optimal AD diagnosis. We propose a novel staged graph convolutional network with uncertainty quantification (SGUQ)
  arXiv  Detail & Related papers  (2024-10-14T19:51:32Z)
• Enhanced Anomaly Detection in Automotive Systems Using SAAD: Statistical Aggregated Anomaly Detection [0.0]
  This paper presents a novel anomaly detection methodology termed Statistical Aggregated Anomaly Detection (SAAD) The SAAD approach integrates advanced statistical techniques with machine learning, and its efficacy is demonstrated through validation on real sensor data from a Hardware-in-the-Loop (HIL) environment within the automotive domain.
  arXiv  Detail & Related papers  (2024-06-11T12:41:24Z)
• Accurate and Reliable Predictions with Mutual-Transport Ensemble [46.368395985214875]
  We propose a co-trained auxiliary model and adaptively regularizes the cross-entropy loss using Kullback-Leibler (KL) We show that MTE can simultaneously enhance both accuracy and uncertainty calibration. For example, on the CIFAR-100 dataset, our MTE method on ResNet34/50 achieved significant improvements compared to previous state-of-the-art method.
  arXiv  Detail & Related papers  (2024-05-30T03:15:59Z)
• Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL [86.0987896274354]
  We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL. We then propose a novel Self-Excite Eigenvalue Measure (SEEM) metric to measure the evolving property of Q-network at training. For the first time, our theory can reliably decide whether the training will diverge at an early stage.
  arXiv  Detail & Related papers  (2023-10-06T17:57:44Z)
• Deep Neural Networks Tend To Extrapolate Predictably [51.303814412294514]
  neural network predictions tend to be unpredictable and overconfident when faced with out-of-distribution (OOD) inputs. We observe that neural network predictions often tend towards a constant value as input data becomes increasingly OOD. We show how one can leverage our insights in practice to enable risk-sensitive decision-making in the presence of OOD inputs.
  arXiv  Detail & Related papers  (2023-10-02T03:25:32Z)
• Cascading Neural Network Methodology for Artificial Intelligence-Assisted Radiographic Detection and Classification of Lead-Less Implanted Electronic Devices within the Chest [0.7874708385247353]
  This work focused on developing CXR interpretation-assisting Artificial Intelligence (AI) methodology with: 1. 100% detection for LLIED presence/location; and 2. High classification in LLIED typing. For developing the cascading neural network (detection via Faster R-CNN and classification via Inception V3), "ground-truth" CXR annotation (ROI labeling per LLIED), as well as inference display (as Generated Bounding Boxes (GBBs))
  arXiv  Detail & Related papers  (2021-08-25T19:29:48Z)
• Semantic Perturbations with Normalizing Flows for Improved Generalization [62.998818375912506]
  We show that perturbations in the latent space can be used to define fully unsupervised data augmentations. We find that our latent adversarial perturbations adaptive to the classifier throughout its training are most effective.
  arXiv  Detail & Related papers  (2021-08-18T03:20:00Z)
• Scalable End-to-End RF Classification: A Case Study on Undersized Dataset Regularization by Convolutional-MST [0.0]
  We present a new deep learning approach based on multistage training and demonstrate it on RF sensing signal classification. We consistently achieve over 99% accuracy for up to 17 diverse classes using only 11 samples per class for training, yielding up to 35% improvement in accuracy over standard DL approaches.
  arXiv  Detail & Related papers  (2021-04-25T08:41:52Z)
• TELESTO: A Graph Neural Network Model for Anomaly Classification in Cloud Services [77.454688257702]
  Machine learning (ML) and artificial intelligence (AI) are applied on IT system operation and maintenance. One direction aims at the recognition of re-occurring anomaly types to enable remediation automation. We propose a method that is invariant to dimensionality changes of given data.
  arXiv  Detail & Related papers  (2021-02-25T14:24:49Z)
• Improving Uncertainty Calibration via Prior Augmented Data [56.88185136509654]
  Neural networks have proven successful at learning from complex data distributions by acting as universal function approximators. They are often overconfident in their predictions, which leads to inaccurate and miscalibrated probabilistic predictions. We propose a solution by seeking out regions of feature space where the model is unjustifiably overconfident, and conditionally raising the entropy of those predictions towards that of the prior distribution of the labels.
  arXiv  Detail & Related papers  (2021-02-22T07:02:37Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

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.