{"total":67,"rows":[{"id":"2211.08413","title":"Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges","created":"2022-11-15","cited_by_count":138,"area":null},{"id":"2104.09994","title":"Federated Learning for Malware Detection in IoT Devices","created":"2021-04-15","cited_by_count":36,"area":null},{"id":"2106.07976","title":"Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data Detection","created":"2021-06-15","cited_by_count":20,"area":null},{"id":"2108.00974","title":"Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challenges","created":"2021-08-02","cited_by_count":16,"area":null},{"id":"2101.10181","title":"Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey","created":"2021-01-25","cited_by_count":9,"area":null},{"id":"2110.04488","title":"Demystifying the Transferability of Adversarial Attacks in Computer Networks","created":"2021-10-09","cited_by_count":9,"area":null},{"id":"2501.15563","title":"PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments","created":"2025-01-26","cited_by_count":9,"area":"safety"},{"id":"2204.03433","title":"Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats","created":"2022-04-07","cited_by_count":7,"area":null},{"id":"2203.09332","title":"Machine Learning for Encrypted Malicious Traffic Detection: Approaches, Datasets and Comparative Study","created":"2022-03-17","cited_by_count":6,"area":null},{"id":"2207.09088","title":"XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics","created":"2022-07-19","cited_by_count":6,"area":null},{"id":"2302.09844","title":"FederatedTrust: A Solution for Trustworthy Federated Learning","created":"2023-02-20","cited_by_count":6,"area":null},{"id":"2406.06207","title":"Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning","created":"2024-06-10","cited_by_count":6,"area":null},{"id":"2504.14879","title":"Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder","created":"2025-04-21","cited_by_count":6,"area":null},{"id":"2212.12121","title":"Federated PCA on Grassmann Manifold for Anomaly Detection in IoT Networks","created":"2022-12-23","cited_by_count":5,"area":null},{"id":"2312.02490","title":"Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems","created":"2023-12-05","cited_by_count":5,"area":null},{"id":"2505.01437","title":"Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets","created":"2025-04-26","cited_by_count":5,"area":null},{"id":"2101.02780","title":"SHARKS: Smart Hacking Approaches for RisK Scanning in Internet-of-Things and Cyber-Physical Systems based on Machine Learning","created":"2021-01-07","cited_by_count":4,"area":null},{"id":"2210.15911","title":"Joint Semantic Transfer Network for IoT Intrusion Detection","created":"2022-10-28","cited_by_count":4,"area":null},{"id":"2301.09801","title":"Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach","created":"2023-01-24","cited_by_count":3,"area":null},{"id":"2306.01377","title":"A systematic literature review on the code smells datasets and validation mechanisms","created":"2023-06-02","cited_by_count":3,"area":null},{"id":"2407.07421","title":"Federated PCA on Grassmann Manifold for IoT Anomaly Detection","created":"2024-07-10","cited_by_count":3,"area":null},{"id":"2505.17357","title":"Graph Attention Neural Network for Botnet Detection: Evaluating Autoencoder, VAE and PCA-Based Dimension Reduction","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2505.17363","title":"Are GNNs Worth the Effort for IoT Botnet Detection? A Comparative Study of VAE-GNN vs. ViT-MLP and VAE-MLP Approaches","created":"2025-05-23","cited_by_count":3,"area":null},{"id":"2103.10824","title":"Enhancing Robustness of On-line Learning Models on Highly Noisy Data","created":"2021-03-19","cited_by_count":2,"area":null},{"id":"2112.00988","title":"Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks","created":"2021-12-02","cited_by_count":2,"area":null},{"id":"2209.12195","title":"Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks","created":"2022-09-25","cited_by_count":2,"area":null},{"id":"2210.13547","title":"Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions","created":"2022-10-24","cited_by_count":2,"area":null},{"id":"2310.10661","title":"TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection","created":"2023-09-14","cited_by_count":2,"area":null},{"id":"2401.05896","title":"The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey","created":"2024-01-11","cited_by_count":2,"area":null},{"id":"2406.03680","title":"Meta-learning for Positive-unlabeled Classification","created":"2024-06-06","cited_by_count":2,"area":null},{"id":"2410.14121","title":"FedMSE: Semi-supervised federated learning approach for IoT network intrusion detection","created":"2024-10-18","cited_by_count":2,"area":null},{"id":"2510.19462","title":"AegisMCP: Online Graph Intrusion Detection for Tool-Augmented LLMs on Edge Devices","created":"2025-10-22","cited_by_count":2,"area":"agents"},{"id":"2102.01515","title":"Robust Attack Detection Approach for IIoT Using Ensemble Classifier","created":"2021-01-30","cited_by_count":1,"area":null},{"id":"2109.03989","title":"Detecting Attacks on IoT Devices using Featureless 1D-CNN","created":"2021-09-09","cited_by_count":1,"area":null},{"id":"2207.00041","title":"DP$^2$-NILM: A Distributed and Privacy-preserving Framework for Non-intrusive Load Monitoring","created":"2022-06-30","cited_by_count":1,"area":null},{"id":"2207.07903","title":"Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network","created":"2022-07-16","cited_by_count":1,"area":null},{"id":"2208.08524","title":"DF-Captcha: A Deepfake Captcha for Preventing Fake Calls","created":"2022-08-17","cited_by_count":1,"area":null},{"id":"2306.13030","title":"Online Self-Supervised Deep Learning for Intrusion Detection Systems","created":"2023-06-22","cited_by_count":1,"area":null},{"id":"2507.07258","title":"FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning","created":"2025-07-09","cited_by_count":1,"area":null},{"id":"2601.00389","title":"NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasion","created":"2026-01-01","cited_by_count":1,"area":null},{"id":"2604.11324","title":"BRIDGE and TCH-Net: Heterogeneous Benchmark and Multi-Branch Baseline for Cross-Domain IoT Botnet Detection","created":"2026-04-13","cited_by_count":1,"area":null},{"id":"2103.00684","title":"Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection","created":"2021-03-01","cited_by_count":0,"area":null},{"id":"2106.07074","title":"RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks","created":"2021-06-13","cited_by_count":0,"area":null},{"id":"2110.01842","title":"Dataset: Large-scale Urban IoT Activity Data for DDoS Attack Emulation","created":"2021-10-05","cited_by_count":0,"area":null},{"id":"2110.03127","title":"Reliable Probability Intervals For Classification Using Inductive Venn Predictors Based on Distance Learning","created":"2021-10-07","cited_by_count":0,"area":null},{"id":"2201.07383","title":"Online Deep Learning based on Auto-Encoder","created":"2022-01-19","cited_by_count":0,"area":null},{"id":"2204.07772","title":"SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment","created":"2022-04-16","cited_by_count":0,"area":null},{"id":"2301.03532","title":"Efficient Attack Detection in IoT Devices using Feature Engineering-Less Machine Learning","created":"2023-01-09","cited_by_count":0,"area":null},{"id":"2302.12205","title":"Harris Hawks Feature Selection in Distributed Machine Learning for Secure IoT Environments","created":"2023-02-20","cited_by_count":0,"area":null},{"id":"2303.01021","title":"CADeSH: Collaborative Anomaly Detection for Smart Homes","created":"2023-03-02","cited_by_count":0,"area":null},{"id":"2402.08063","title":"Locality Sensitive Hashing for Network Traffic Fingerprinting","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2403.10968","title":"Enhancing IoT Security Against DDoS Attacks through Federated Learning","created":"2024-03-16","cited_by_count":0,"area":null},{"id":"2403.15509","title":"Teacher-free Latent Self-distillation and Class-separable Representations for Lightweight IoT Attack Detection","created":"2024-03-22","cited_by_count":0,"area":"distill"},{"id":"2403.16149","title":"Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey","created":"2024-03-24","cited_by_count":0,"area":null},{"id":"2404.13690","title":"Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing","created":"2024-04-21","cited_by_count":0,"area":null},{"id":"2408.15886","title":"Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks","created":"2024-08-28","cited_by_count":0,"area":null},{"id":"2505.24269","title":"INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2510.03513","title":"A Lightweight Federated Learning Approach for Privacy-Preserving Botnet Detection in IoT","created":"2025-10-03","cited_by_count":0,"area":null},{"id":"2510.08084","title":"A Novel Ensemble Learning Approach for Enhanced IoT Attack Detection: Redefining Security Paradigms in Connected Systems","created":"2025-10-09","cited_by_count":0,"area":null},{"id":"2510.23019","title":"Sentinel: Dynamic Knowledge Distillation for Personalized Federated Intrusion Detection in Heterogeneous IoT Networks","created":"2025-10-27","cited_by_count":0,"area":null},{"id":"2511.01583","title":"Federated Cyber Defense: Privacy-Preserving Ransomware Detection Across Distributed Systems","created":"2025-11-03","cited_by_count":0,"area":null},{"id":"2511.03201","title":"A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies","created":"2025-11-05","cited_by_count":0,"area":null},{"id":"2512.22488","title":"Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection via Latent Space Representation Learning and Alignment","created":"2025-12-27","cited_by_count":0,"area":null},{"id":"2601.14505","title":"Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2603.06654","title":"How the Graph Construction Technique Shapes Performance in IoT Botnet Detection","created":"2026-02-28","cited_by_count":0,"area":null},{"id":"2605.05896","title":"VARS-FL: Validation-Aligned Client Selection for Non-IID Federated Learning in IoT Systems","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2607.04698","title":"F-ACVAE: A Federated Adaptive Conditional Variational Auto-Encoder for Privacy-Preserving Intrusion Detection in IoT Networks","created":"2026-07-06","cited_by_count":0,"area":null}],"next_cursor":null}