ExAI5G: A Logic-Based Explainable AI Framework for Intrusion Detection in 5G Networks
organization= Faculty of Information Technology；Electrical Engineering, University of Oulu , addressline= , city= Oulu , postcode= 90570 , state= , country= Finland
Intrusion detection systems (IDSs) for 5G networks must handle complex, high-volume traffic. Although opaque "black-box" models can achieve high accuracy, their lack of transparency hinders trust and effective operational response. We propose ExAI5G, a framework that prioritizes interpretability by integrating a Transformer-based deep learning IDS with logic-based explainable AI (XAI) techniques. The framework uses Integrated Gradients to attribute feature importance and extracts a surrogate decision tree to derive logical rules. We introduce a novel evaluation methodology for LLM-generated explanations, using a powerful evaluator LLM to assess actionability and measuring their semantic similarity and faithfulness. On a 5G IoT intrusion dataset, our system achieves 99.9\% accuracy and a 0.854 macro F1-score, demonstrating strong performance. More importantly, we extract 16 logical rules with 99.7\% fidelity, making the model's reasoning transparent. The evaluation demonstrates that modern LLMs can generate explanations that are both faithful and actionable, indicating that it is possible to build a trustworthy and effective IDS without compromising performance for the sake of marginal gains from an opaque model.
Explainable AI for Comparative Analysis of Intrusion Detection Models
Embry-Riddle Aeronautical University（安柏瑞德航空大学）；University of Tennessee at Martin（田纳西大学马丁分校）；Bowling Green State University（鲍灵格林州立大学）；University of Maryland, Baltimore County（马里兰大学巴尔的摩郡分校）
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School of Electrical Engineering and Computer Science (SEECS), National University of Sciences and Technology (NUST)（电气工程与计算机科学学院（SEECS），国家 sciences and Technology（NUST））
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Hamad bin Khalifa University（哈马德·本·哈利法大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Munster Technological University（芒斯特理工大学）；Toronto Metropolitan University（多伦多都会大学）
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity
Kırıkkale University（克尔克卡拉大学）；University College Dublin（都柏林大学学院）；CeADAR；School of Computer Science（计算机科学学院）
Explainable AI-Driven Cyber Risk Analytics and Model Reliability Assessment for Intelligent Governance of U.S. Critical Infrastructure: An XGBoost and SHAP-Based Intrusion Detection Framework
Department of Business Information Systems, Central Michigan University（中央密歇根大学商业信息系统系）；Department of Information Studies, Trine University（特林大学信息学系）；Department of Computer Science, Maharishi International University（Maharishi国际大学计算机科学系）
Conversational versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance
Phenikaa University（菲卡大学）；Phenikaa School of Computing（菲卡计算机学院）；MobiFone Corporation（MobiFone集团）；MobiFone HighTech Center（MobiFone高科技中心）；National Economics University（国民经济大学）；College of Technology（技术学院）
BACON: A fully explainable AI model with graded logic for decision making problems
Department of Information Systems University of Maryland, Baltimore County (UMBC)（信息系统系大学马里兰大学巴尔的摩县（UMBC））；Department of Computer Science San Francisco State University（计算机科学系旧金山州立大学）；Department of Information Systems UMBC（信息系统系大学马里兰大学巴尔的摩县（UMBC））
Human-Centered Explainable AI for Security Enhancement: A Deep Intrusion Detection Framework
Department of Computer Science and Engineering, United International University (UIU), Dhaka 1212, Bangladesh（计算机科学与工程系，国际联合大学（UIU），达卡1212，孟加拉国）；Department of Electrical and Electronic Engineering, Islamic University of Technology, Gazipur 1704, Bangladesh（电气与电子工程系，伊斯兰科技大学，加兹ipur 1704，孟加拉国）；Department of Computer Science and Engineering (CSE), University of Asia Pacific (UAP), Dhaka 1207, Bangladesh（计算机科学与工程系（CSE），亚洲太平洋大学（UAP），达卡1207，孟加拉国）；Department of Information Systems, University of Maryland, Baltimore, 21250, Maryland, USA（信息系统系，马里兰大学，巴尔的摩，21250，美国）；College Of Information Sciences and Technology, Pennsylvania State University, University Park, PA 16802, USA（信息科学与技术学院，宾夕法尼亚州立大学，大学公园，PA 16802，美国）；Department of Information Technology, Washington University of Science and Technology, Alexandria, VA（信息技术系，科学与技术华盛顿大学，亚历山大，VA）；College of Engineering and Information Technology, University of Maryland, College Park, 20742, Maryland, USA（工程与信息技术学院，马里兰大学，学院公园，20742，美国）
Enhancing Adversarial Robustness of IoT Intrusion Detection via SHAP-Based Attribution Fingerprinting
School of Information Technology, York University（约克大学信息科技学院）；School of Computer Science, University of Guelph（圭尔夫大学计算机科学学院）；National Research Council of Canada（加拿大国家研究委员会）
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System
Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development (GECAD), Porto School of Engineering (ISEP)（波尔图工程学院（ISEP）智能工程与先进创新开发计算研究组（GECAD））
Automated and Explainable Denial of Service Analysis for AI-Driven Intrusion Detection Systems
Computer and Information Science Department, Fordham University（福特汉姆大学计算机与信息科学系）；RENCI, University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校RENCI）；Department of Mathematics and Computer Science, Royal Military College of Canada（加拿大皇家军事学院数学与计算机科学系）；Data Science Department, University of North Texas（德克萨斯大学纳夫卡特数据科学系）
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Centre for Intelligent Cloud Computing, CoE for Advanced Cloud, Faculty of Information Science and Technology, Multimedia University（智能云计算中心、先进云技术中心、信息科学与技术学院、多媒体大学）；Department of Communication Technology and Networking, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia（通信技术与网络系、计算机科学与信息技术学院、马来西亚国立大学）；Laboratory of Computational Science and Mathematical Physics, Institute for Mathematical Research, Universiti Putra Malaysia（计算科学与数学物理实验室、数学研究所、马来西亚国立大学）
Large Language Models for Network Intrusion Detection Systems: Foundations, Implementations, and Future Directions
Department of Electrical and Electronic Engineering, The University of Hong Kong（香港大学电子与电气工程系）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；National Institute of Health Data Science, Peking University（北京大学健康数据科学国家研究院）；Data Science and Analytics Thrust, Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）数据科学与分析方向）
