{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/intrusion-detection/papers/3","list_of":"/task/intrusion-detection","task":"Intrusion Detection","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":3,"pages_in_order":8,"rows_per_page":100,"rows":[201,300],"of":800,"counts":{"archive_papers_tagged":800,"with_a_code_link":151,"where_syntology_ran_a_sample":14,"not_listed_spam_title":0,"listed":800,"listed_where_code_ran":14,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":11,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":11,"listed_every_run_a_failure_of_syntologys_instrument":3,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/intrusion-detection","prev":"/task/intrusion-detection/papers/2","next":"/task/intrusion-detection/papers/4","papers":[{"url":null,"slug":"2503-00358","title":"CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid","date":"2025-03-01","arxiv_id":"2503.00358","repositories_listed":0,"syntology":null},{"url":null,"slug":"unmasking-stealthy-attacks-on-nonlinear-dae","title":"Unmasking Stealthy Attacks on Nonlinear DAE Models of Power Grids","date":"2025-02-28","arxiv_id":"2502.21146","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-sensor-attack-detection-in","title":"Enhancing sensor attack detection in supervisory control systems modeled by probabilistic automata","date":"2025-02-23","arxiv_id":"2502.16753","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-defensive-framework-against-adversarial","title":"A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems","date":"2025-02-21","arxiv_id":"2502.15561","repositories_listed":0,"syntology":null},{"url":null,"slug":"binary-and-multi-class-intrusion-detection-in","title":"Binary and Multi-Class Intrusion Detection in IoT Using Standalone and Hybrid Machine and Deep Learning Models","date":"2025-02-20","arxiv_id":"2503.22684","repositories_listed":0,"syntology":null},{"url":null,"slug":"cnd-ids-continual-novelty-detection-for","title":"CND-IDS: Continual Novelty Detection for Intrusion Detection Systems","date":"2025-02-19","arxiv_id":"2502.14094","repositories_listed":0,"syntology":null},{"url":null,"slug":"hybrid-machine-learning-models-for-intrusion","title":"Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset","date":"2025-02-17","arxiv_id":"2502.12382","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-intrusion-detection-2","title":"Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions","date":"2025-02-16","arxiv_id":"2502.11138","repositories_listed":0,"syntology":null},{"url":null,"slug":"mapping-the-landscape-of-generative-ai-in","title":"Mapping the Landscape of Generative AI in Network Monitoring and Management","date":"2025-02-12","arxiv_id":"2502.08576","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-tuning-federated-learning-based","title":"Fine-Tuning Federated Learning-Based Intrusion Detection Systems for Transportation IoT","date":"2025-02-10","arxiv_id":"2502.06099","repositories_listed":0,"syntology":null},{"url":null,"slug":"safe-self-supervised-anomaly-detection","title":"SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection","date":"2025-02-10","arxiv_id":"2502.07119","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-conditional-tabular-gan-enhanced-intrusion","title":"A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks","date":"2025-02-09","arxiv_id":"2502.06031","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-apt-malware-command-and-control","title":"Detecting APT Malware Command and Control over HTTP(S) Using Contextual Summaries","date":"2025-02-07","arxiv_id":"2502.05367","repositories_listed":0,"syntology":null},{"url":null,"slug":"technical-report-generating-the-web-ids23-1","title":"Technical Report: Generating the WEB-IDS23 Dataset","date":"2025-02-06","arxiv_id":"2502.03909","repositories_listed":0,"syntology":null},{"url":null,"slug":"implementing-large-quantum-boltzmann-machines","title":"Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing","date":"2025-02-05","arxiv_id":"2502.03086","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-of-zero-day-attack-detection-using","title":"Analysis of Zero Day Attack Detection Using MLP and XAI","date":"2025-01-28","arxiv_id":"2501.16638","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-application-of-deep-neural","title":"Investigating Application of Deep Neural Networks in Intrusion Detection System Design","date":"2025-01-27","arxiv_id":"2501.15760","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-transfer-learning-framework-for-anomaly-1","title":"A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data","date":"2025-01-26","arxiv_id":"2501.15365","repositories_listed":0,"syntology":null},{"url":null,"slug":"pcap-backdoor-backdoor-poisoning-generator","title":"PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments","date":"2025-01-26","arxiv_id":"2501.15563","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-intrusion-detection-in-iiot-networks","title":"Enhanced Intrusion Detection in IIoT Networks: A Lightweight Approach with Autoencoder-Based Feature Learning","date":"2025-01-25","arxiv_id":"2501.15266","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-cyber-attack-detection-in-iiot-using","title":"Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models","date":"2025-01-21","arxiv_id":"2501.13962","repositories_listed":0,"syntology":null},{"url":null,"slug":"polylut-ultra-low-latency-polynomial","title":"PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning","date":"2025-01-14","arxiv_id":"2501.08043","repositories_listed":0,"syntology":null},{"url":null,"slug":"continuum-detecting-apt-attacks-through","title":"CONTINUUM: Detecting APT Attacks through Spatial-Temporal Graph Neural Networks","date":"2025-01-06","arxiv_id":"2501.02981","repositories_listed":0,"syntology":null},{"url":null,"slug":"bartpredict-empowering-iot-security-with-llm","title":"BARTPredict: Empowering IoT Security with LLM-Driven Cyber Threat Prediction","date":"2025-01-03","arxiv_id":"2501.01664","repositories_listed":0,"syntology":null},{"url":null,"slug":"cyber-shadows-neutralizing-security-threats","title":"Cyber Shadows: Neutralizing Security Threats with AI and Targeted Policy Measures","date":"2025-01-03","arxiv_id":"2501.09025","repositories_listed":0,"syntology":null},{"url":null,"slug":"lens-xai-redefining-lightweight-and","title":"LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity","date":"2025-01-01","arxiv_id":"2501.00790","repositories_listed":0,"syntology":null},{"url":null,"slug":"collaborative-approaches-to-enhancing-smart","title":"Collaborative Approaches to Enhancing Smart Vehicle Cybersecurity by AI-Driven Threat Detection","date":"2024-12-31","arxiv_id":"2501.00261","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-anomaly-detection-system-based-on","title":"An Anomaly Detection System Based on Generative Classifiers for Controller Area Network","date":"2024-12-28","arxiv_id":"2412.20255","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-in-multiple-spaces-few-shot-network","title":"Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks","date":"2024-12-28","arxiv_id":"2501.00050","repositories_listed":0,"syntology":null},{"url":null,"slug":"powerradio-manipulate-sensor-measurementvia","title":"PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation","date":"2024-12-24","arxiv_id":"2412.18103","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-temporal-convolutional-network-based","title":"A Temporal Convolutional Network-based Approach for Network Intrusion Detection","date":"2024-12-23","arxiv_id":"2412.17452","repositories_listed":0,"syntology":null},{"url":null,"slug":"flow-exporter-impact-on-intelligent-intrusion","title":"Flow Exporter Impact on Intelligent Intrusion Detection Systems","date":"2024-12-18","arxiv_id":"2412.14021","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-internet-of-things-security","title":"Enhancing Internet of Things Security throughSelf-Supervised Graph Neural Networks","date":"2024-12-17","arxiv_id":"2412.13240","repositories_listed":0,"syntology":null},{"url":null,"slug":"comprehensive-survey-on-adversarial-examples","title":"Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies","date":"2024-12-16","arxiv_id":"2412.12217","repositories_listed":0,"syntology":null},{"url":null,"slug":"distributed-intrusion-detection-system-using","title":"Distributed Intrusion Detection System using Semantic-based Rules for SCADA in Smart Grid","date":"2024-12-10","arxiv_id":"2412.07917","repositories_listed":0,"syntology":null},{"url":null,"slug":"applications-of-positive-unlabeled-pu-and","title":"Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity","date":"2024-12-09","arxiv_id":"2412.06203","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-based-android-intrusion","title":"Machine Learning-based Android Intrusion Detection System","date":"2024-12-05","arxiv_id":"2412.03894","repositories_listed":0,"syntology":null},{"url":null,"slug":"convolutional-neural-networks-and-mixture-of","title":"Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond","date":"2024-12-04","arxiv_id":"2412.03483","repositories_listed":0,"syntology":null},{"url":null,"slug":"graph-powered-defense-controller-area-network","title":"Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles","date":"2024-12-03","arxiv_id":"2412.02539","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimized-iot-intrusion-detection-using","title":"Optimized IoT Intrusion Detection using Machine Learning Technique","date":"2024-12-03","arxiv_id":"2412.02845","repositories_listed":0,"syntology":null},{"url":null,"slug":"swarm-intelligence-driven-client-selection","title":"Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications","date":"2024-11-28","arxiv_id":"2411.18877","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimal-in-network-distribution-of-learning","title":"Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks","date":"2024-11-27","arxiv_id":"2411.18384","repositories_listed":0,"syntology":null},{"url":null,"slug":"an-automl-based-approach-for-network","title":"An AutoML-based approach for Network Intrusion Detection","date":"2024-11-24","arxiv_id":"2411.15920","repositories_listed":0,"syntology":null},{"url":null,"slug":"the-importance-of-the-clustering-model-to","title":"The importance of the clustering model to detect new types of intrusion in data traffic","date":"2024-11-21","arxiv_id":"2411.14550","repositories_listed":0,"syntology":null},{"url":null,"slug":"feature-selection-for-network-intrusion","title":"Feature Selection for Network Intrusion Detection","date":"2024-11-18","arxiv_id":"2411.11603","repositories_listed":0,"syntology":null},{"url":null,"slug":"take-package-as-language-anomaly-detection","title":"Take Package as Language: Anomaly Detection Using Transformer","date":"2024-11-15","arxiv_id":"2412.04473","repositories_listed":0,"syntology":null},{"url":null,"slug":"intelligent-green-efficiency-for-intrusion","title":"Intelligent Green Efficiency for Intrusion Detection","date":"2024-11-11","arxiv_id":"2411.08069","repositories_listed":0,"syntology":null},{"url":null,"slug":"sdn-intrusion-detection-using-machine","title":"Sdn Intrusion Detection Using Machine Learning Method","date":"2024-11-08","arxiv_id":"2411.05888","repositories_listed":0,"syntology":null},{"url":null,"slug":"securing-from-unseen-connected-pattern","title":"Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection","date":"2024-11-07","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-real-time-threat-detection-in-5g","title":"Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis","date":"2024-11-05","arxiv_id":"2411.03365","repositories_listed":0,"syntology":null},{"url":null,"slug":"visually-analyze-shap-plots-to-diagnose","title":"Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection","date":"2024-11-04","arxiv_id":"2411.02670","repositories_listed":0,"syntology":null},{"url":null,"slug":"nids-neural-networks-using-sliding-time","title":"NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability","date":"2024-10-24","arxiv_id":"2410.18658","repositories_listed":0,"syntology":null},{"url":null,"slug":"transforming-in-vehicle-network-intrusion","title":"Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI","date":"2024-10-11","arxiv_id":"2410.09043","repositories_listed":0,"syntology":null},{"url":null,"slug":"knowgraph-knowledge-enabled-anomaly-detection","title":"KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data","date":"2024-10-10","arxiv_id":"2410.08390","repositories_listed":0,"syntology":null},{"url":null,"slug":"effective-intrusion-detection-for-uav","title":"Effective Intrusion Detection for UAV Communications using Autoencoder-based Feature Extraction and Machine Learning Approach","date":"2024-10-01","arxiv_id":"2410.02827","repositories_listed":0,"syntology":null},{"url":null,"slug":"machine-learning-assisted-intrusion-detection","title":"Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security","date":"2024-10-01","arxiv_id":"2410.01016","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-for-deep-reinforcement-learning","title":"A Survey for Deep Reinforcement Learning Based Network Intrusion Detection","date":"2024-09-25","arxiv_id":"2410.07612","repositories_listed":0,"syntology":null},{"url":null,"slug":"team-temporal-adversarial-examples-attack","title":"TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN","date":"2024-09-19","arxiv_id":"2409.12472","repositories_listed":0,"syntology":null},{"url":null,"slug":"trustworthy-intrusion-detection-confidence","title":"Trustworthy Intrusion Detection: Confidence Estimation Using Latent Space","date":"2024-09-19","arxiv_id":"2409.13774","repositories_listed":0,"syntology":null},{"url":null,"slug":"fair-anomaly-detection-for-imbalanced-groups","title":"Fair Anomaly Detection For Imbalanced Groups","date":"2024-09-17","arxiv_id":"2409.10951","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-in-adversarial","title":"Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity","date":"2024-09-15","arxiv_id":"2409.09794","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-a-graph-based-foundation-model-for","title":"Towards a graph-based foundation model for network traffic analysis","date":"2024-09-12","arxiv_id":"2409.08111","repositories_listed":0,"syntology":null},{"url":null,"slug":"introducing-perturb-ability-score-ps-to","title":"A Novel Perturb-ability Score to Mitigate Evasion Adversarial Attacks on Flow-Based ML-NIDS","date":"2024-09-11","arxiv_id":"2409.07448","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-driven-intrusion-detection-systems-ids-on","title":"AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)","date":"2024-08-30","arxiv_id":"2408.17235","repositories_listed":0,"syntology":null},{"url":null,"slug":"c-radar-a-centralized-deep-learning-system","title":"C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks","date":"2024-08-30","arxiv_id":"2408.17356","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-intrusion-detection-in-iot","title":"Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks","date":"2024-08-28","arxiv_id":"2408.15886","repositories_listed":0,"syntology":null},{"url":null,"slug":"systematic-evaluation-of-synthetic-data","title":"Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic","date":"2024-08-28","arxiv_id":"2408.16034","repositories_listed":0,"syntology":null},{"url":null,"slug":"beyond-detection-leveraging-large-language","title":"Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks","date":"2024-08-26","arxiv_id":"2408.14045","repositories_listed":0,"syntology":null},{"url":null,"slug":"transformers-and-large-language-models-for-1","title":"Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey","date":"2024-08-14","arxiv_id":"2408.07583","repositories_listed":0,"syntology":null},{"url":null,"slug":"ai-driven-chatbot-for-intrusion-detection-in","title":"AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent","date":"2024-08-08","arxiv_id":"2408.04281","repositories_listed":0,"syntology":null},{"url":null,"slug":"towards-explainable-network-intrusion","title":"Towards Explainable Network Intrusion Detection using Large Language Models","date":"2024-08-08","arxiv_id":"2408.04342","repositories_listed":0,"syntology":null},{"url":null,"slug":"preliminary-study-on-artificial-intelligence","title":"Preliminary study on artificial intelligence methods for cybersecurity threat detection in computer networks based on raw data packets","date":"2024-07-24","arxiv_id":"2407.17339","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-life-long-learning-intrusion-detection","title":"A Life-long Learning Intrusion Detection System for 6G-Enabled IoV","date":"2024-07-22","arxiv_id":"2407.15700","repositories_listed":0,"syntology":null},{"url":null,"slug":"explainable-ai-based-intrusion-detection","title":"Explainable AI-based Intrusion Detection System for Industry 5.0: An Overview of the Literature, associated Challenges, the existing Solutions, and Potential Research Directions","date":"2024-07-21","arxiv_id":"2408.03335","repositories_listed":0,"syntology":null},{"url":null,"slug":"decentralized-federated-anomaly-detection-in","title":"Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach","date":"2024-07-20","arxiv_id":"2407.15879","repositories_listed":0,"syntology":null},{"url":null,"slug":"operating-system-and-artificial-intelligence","title":"Integrating Artificial Intelligence into Operating Systems: A Comprehensive Survey on Techniques, Applications, and Future Directions","date":"2024-07-19","arxiv_id":"2407.14567","repositories_listed":0,"syntology":null},{"url":null,"slug":"impacts-of-data-preprocessing-and","title":"Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems","date":"2024-07-15","arxiv_id":"2407.11105","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-survey-on-the-application-of-generative","title":"A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes","date":"2024-07-11","arxiv_id":"2407.08839","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-agent-reinforcement-learning-based-5","title":"Multi-agent Reinforcement Learning-based Network Intrusion Detection System","date":"2024-07-08","arxiv_id":"2407.05766","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-critical-assessment-of-interpretable-and","title":"A Critical Assessment of Interpretable and Explainable Machine Learning for Intrusion Detection","date":"2024-07-04","arxiv_id":"2407.04009","repositories_listed":0,"syntology":null},{"url":null,"slug":"antibotv-a-multilevel-behaviour-based","title":"AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks","date":"2024-07-03","arxiv_id":"2407.03506","repositories_listed":0,"syntology":null},{"url":null,"slug":"federated-learning-for-zero-day-attack","title":"Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks","date":"2024-07-03","arxiv_id":"2407.03070","repositories_listed":0,"syntology":null},{"url":null,"slug":"zero-x-a-blockchain-enabled-open-set","title":"Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV","date":"2024-07-03","arxiv_id":"2407.02969","repositories_listed":0,"syntology":null},{"url":null,"slug":"ppt-gnn-a-practical-pre-trained-spatio","title":"PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security","date":"2024-06-19","arxiv_id":"2406.13365","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-cutting-edge-deep-learning-method-for","title":"A Cutting-Edge Deep Learning Method For Enhancing IoT Security","date":"2024-06-18","arxiv_id":"2406.12400","repositories_listed":0,"syntology":null},{"url":null,"slug":"feasibility-of-non-line-of-sight-integrated","title":"Feasibility of Non-Line-of-Sight Integrated Sensing and Communication at mmWave","date":"2024-06-18","arxiv_id":"2406.12828","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhanced-intrusion-detection-system-for","title":"Enhanced Intrusion Detection System for Multiclass Classification in UAV Networks","date":"2024-06-14","arxiv_id":"2406.10417","repositories_listed":0,"syntology":null},{"url":null,"slug":"detection-rate-emphasized-multi-objective","title":"Detection-Rate-Emphasized Multi-objective Evolutionary Feature Selection for Network Intrusion Detection","date":"2024-06-13","arxiv_id":"2406.09180","repositories_listed":0,"syntology":null},{"url":null,"slug":"efficient-network-traffic-feature-sets-for","title":"Efficient Network Traffic Feature Sets for IoT Intrusion Detection","date":"2024-06-12","arxiv_id":"2406.08042","repositories_listed":0,"syntology":null},{"url":null,"slug":"sequential-binary-classification-for","title":"Sequential Binary Classification for Intrusion Detection","date":"2024-06-10","arxiv_id":"2406.06099","repositories_listed":0,"syntology":null},{"url":null,"slug":"novel-approach-to-intrusion-detection","title":"Novel Approach to Intrusion Detection: Introducing GAN-MSCNN-BILSTM with LIME Predictions","date":"2024-06-08","arxiv_id":"2406.05443","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-in-the-loop-integrating-llms","title":"Generative AI-in-the-loop: Integrating LLMs and GPTs into the Next Generation Networks","date":"2024-06-06","arxiv_id":"2406.04276","repositories_listed":0,"syntology":null},{"url":null,"slug":"strengthening-network-intrusion-detection-in","title":"Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning","date":"2024-06-04","arxiv_id":"2406.02636","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-synergistic-approach-in-network-intrusion","title":"A Synergistic Approach In Network Intrusion Detection By Neurosymbolic AI","date":"2024-06-03","arxiv_id":"2406.00938","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimizing-cnn-bigru-performance-mish","title":"Optimizing cnn-Bigru performance: Mish activation and comparative analysis with Relu","date":"2024-05-30","arxiv_id":"2405.20503","repositories_listed":0,"syntology":null},{"url":null,"slug":"enhancing-iot-security-with-cnn-and-lstm","title":"Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems","date":"2024-05-28","arxiv_id":"2405.18624","repositories_listed":0,"syntology":null},{"url":null,"slug":"survey-of-graph-neural-network-for-internet","title":"Survey of Graph Neural Network for Internet of Things and NextG Networks","date":"2024-05-27","arxiv_id":"2405.17309","repositories_listed":0,"syntology":null},{"url":"/paper/kinetgan-enabling-distributed-network","slug":"kinetgan-enabling-distributed-network","title":"KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation","date":"2024-05-26","arxiv_id":"2405.16476","repositories_listed":0,"syntology":null},{"url":null,"slug":"generative-ai-and-large-language-models-for","title":"Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities","date":"2024-05-21","arxiv_id":"2405.12750","repositories_listed":0,"syntology":null},{"url":null,"slug":"strategic-deployment-of-honeypots-in","title":"Strategic Deployment of Honeypots in Blockchain-based IoT Systems","date":"2024-05-21","arxiv_id":"2405.12951","repositories_listed":0,"syntology":null}],"record_sha256":"6a9191f2962a8a0baffba3eec093daa1067f94db6392d2753cebca6ad4b50854","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}