mlsanityData to 7 Oct 2026

A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI

arXiv 2406.00938 ↗First posted 3 Jun 2024cs.CR cs.AI cs.SCcited by 2

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Abstract (arXiv)

The prevailing approaches in Network Intrusion Detection Systems (NIDS) are often hampered by issues such as high resource consumption, significant computational demands, and poor interpretability. Furthermore, these systems generally struggle to identify novel, rapidly changing cyber threats. This paper delves into the potential of incorporating Neurosymbolic Artificial Intelligence (NSAI) into NIDS, combining deep learning's data-driven strengths with symbolic AI's logical reasoning to tackle the dynamic challenges in cybersecurity, which also includes detailed NSAI techniques introduction for cyber professionals to explore the potential strengths of NSAI in NIDS. The inclusion of NSAI in NIDS marks potential advancements in both the detection and interpretation of intricate network threats, benefiting from the robust pattern recognition of neural networks and the interpretive prowess of symbolic reasoning. By analyzing network traffic data types and machine learning architectures, we illustrate NSAI's distinctive capability to offer more profound insights into network behavior, thereby improving both detection performance and the adaptability of the system. This merging of technologies not only enhances the functionality of traditional NIDS but also sets the stage for future developments in building more resilient, interpretable, and dynamic defense mechanisms against advanced cyber threats. The continued progress in this area is poised to transform NIDS into a system that is both responsive to known threats and anticipatory of emerging, unseen ones.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 74 references that are in the map. First posted on arXiv 3 Jun 2024.

Builds on

32 of 74 references.

References · 32

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017Mask R-CNNcited by 7,259
  3. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  4. 2016CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoningcited by 1,445
  5. 2017Explanation in Artificial Intelligence: Insights from the Social Sciencescited by 1,201
  6. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  7. 2019CLEVRER: CoLlision Events for Video REpresentation and Reasoningcited by 475
  8. 2019The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervisioncited by 369
  9. 2017A simple neural network module for relational reasoningcited by 345
  10. 2017A Semantic Loss Function for Deep Learning with Symbolic Knowledgecited by 267
  11. 2020Logic Tensor Networkscited by 197
  12. 2019Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoningcited by 149
20 more
  1. 2017RobustFill: Neural Program Learning under Noisy I/Ocited by 124
  2. 2023NeurASP: Embracing Neural Networks into Answer Set Programmingcited by 92
  3. 2018Propagation Networks for Model-Based Control Under Partial Observationcited by 64
  4. 2021DeepStochLog: Neural Stochastic Logic Programmingcited by 58
  5. 2020Neurosymbolic Reinforcement Learning with Formally Verified Explorationcited by 49
  6. 2016Towards Deep Symbolic Reinforcement Learningcited by 42
  7. 2021A Survey on Neural-symbolic Learning Systemscited by 39
  8. 2018HOUDINI: Lifelong Learning as Program Synthesiscited by 38
  9. 2021Neuro-Symbolic Reinforcement Learning with First-Order Logiccited by 30
  10. 2021Neuro-Symbolic AI: An Emerging Class of AI Workloads and their Characterizationcited by 22
  11. 2018Estimate and Replace: A Novel Approach to Integrating Deep Neural Networks with Existing Applicationscited by 21
  12. 2018Towards Symbolic Reinforcement Learning with Common Sensecited by 12
  13. 2023Real-time Network Intrusion Detection via Decision Transformerscited by 12
  14. 2022Safe Neurosymbolic Learning with Differentiable Symbolic Executioncited by 9
  15. 2023RIDE: Real-time Intrusion Detection via Explainable Machine Learning Implemented in a Memristor Hardware Architecturecited by 7
  16. 2020I-SiamIDS: an improved Siam-IDS for handling class imbalance in network-based intrusion detection systemscited by 5
  17. 2023Deep PackGen: A Deep Reinforcement Learning Framework for Adversarial Network Packet Generationcited by 5
  18. 2021Cybersecurity Anomaly Detection in Adversarial Environmentscited by 4
  19. 2022Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methodscited by 3
  20. 2023Novelty Detection in Network Traffic: Using Survival Analysis for Feature Identificationcited by 3

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Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Neuro-Symbolic AI for Cybersecurity: State of the Art, Challenges, and Opportunitiescited by 2
  2. 2025Neurosymbolic Artificial Intelligence for Robust Network Intrusion Detection: From Scratch to Transfer Learningcited by 1

Similar, not cited

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  1. 2024Towards Explainable Network Intrusion Detection using Large Language Models
  2. 2026Generative AI and Federated Learning for Intrusion Detection Systems: A Survey
  3. 2026Does Reasoning Mitigate Backdoor Attacks? A Neuro-Symbolic Perspective
  4. 2026Safety, Security, and Cognitive Risks in Neuro-Symbolic AI
  5. 2026From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation
  6. 2025Reasoning Under Threat: Symbolic and Neural Techniques for Cybersecurity Verification
  7. 2025NetMoniAI: An Agentic AI Framework for Network Security & Monitoring
  8. 2025Unlocking the Potential of Generative AI through Neuro-Symbolic Architectures: Benefits and Limitations