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Related Experiment Video

Updated: Mar 20, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Epistemic uncertain computing for intrusion detection with explainability & multi-criteria optimization using AA-NLS

K Kiruthika1, M Karpagam2, Tanvir H Sardar3

  • 1Department of Mathematics, K.S.Rangasamy College of Technology, Tiruchengode, Namakkal, Tamil Nadu, 637 215, India.

Scientific Reports
|March 19, 2026
PubMed
Summary

This study introduces a new method for network intrusion detection, addressing uncertainties from out-of-order packets. The proposed system achieves 99.299% accuracy, outperforming existing deep learning approaches.

Keywords:
Behaviour analysisIntelligent decision supportMulti-criteria optimizationNeutrosophic algebraUncertain computingUncertainty in intrusion detectioneXplainable artificial intelligence (XAI)

Related Experiment Videos

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Network intrusion detection systems (NIDS) face uncertainty due to incomplete data and unaddressed out-of-order packet arrivals, leading to false positives.
  • Existing NIDS models often overlook the epistemic uncertainty introduced by out-of-order packet sequences, limiting detection accuracy.
  • Accurate network security relies on robust intrusion detection that can effectively handle data uncertainties.

Purpose of the Study:

  • To propose a novel framework for network intrusion detection that specifically analyzes and mitigates uncertainty from out-of-order packet arrivals.
  • To enhance the accuracy and reliability of NIDS by incorporating advanced uncertainty quantification techniques.
  • To develop a comprehensive system for detecting, explaining, and prioritizing network intrusions.

Main Methods:

  • Implemented an Algebraic Average-based Neutrosophic Logic System (AA-NLS) for out-of-order packet uncertainty analysis.
  • Utilized Weighted Distance Error Function-based K-Nearest Neighbour (WDEFKNN) for missing data imputation and Density-Based Beta Distribution Soergel Spatial Clustering of Applications with Noise (DBBDSSCAN) for pattern grouping.
  • Employed a Generalized Riccati Uniform Scaling Orthogonal-based Gated Recurrent Unit (GRUSO-GRU) for intrusion detection and a Pareto Entropy-based SHapley Additive exPlanation (PESHAP) model for explainability.

Main Results:

  • The proposed AA-NLS based framework achieved a high intrusion detection accuracy of 99.299% in a full multi-class setting.
  • Demonstrated superior performance compared to prevailing Deep Learning (DL)-based NIDS approaches.
  • Successfully analyzed epistemic uncertainty from out-of-order packets and temporal patterns using AA-NLS and Hidden Laplace Witten Bell Markov Model (HLWBMM).

Conclusions:

  • The novel AA-NLS framework effectively addresses out-of-order packet uncertainty, significantly improving network intrusion detection accuracy.
  • The integrated approach provides explainable AI for intrusion detection and prioritizes alerts using multi-criteria optimization.
  • This research offers a robust and accurate solution for modern network security challenges.