Cover Image

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Elvin, Gungor, Onat, Shang, Zhengli, Rosing, Tajana
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection
by: Li, Elvin, et al.
Published: (2025)

DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-based Intrusion Detection Against Adversarial Attacks
by: Chen, Jing, et al.
Published: (2025)

SAGE: Sample-Aware Guarding Engine for Robust Intrusion Detection Against Adversarial Attacks
by: Chen, Jing, et al.
Published: (2025)

ACORN-IDS: Adaptive Continual Novelty Detection for Intrusion Detection Systems
by: Fuhrman, Sean, et al.
Published: (2026)

CND-IDS: Continual Novelty Detection for Intrusion Detection Systems
by: Fuhrman, Sean, et al.
Published: (2025)

LIGHT-HIDS: A Lightweight and Effective Machine Learning-Based Framework for Robust Host Intrusion Detection
by: Gungor, Onat, et al.
Published: (2025)

ReLATE+: Unified Framework for Adversarial Attack Detection, Classification, and Resilient Model Selection in Time-Series Classification
by: Kocal, Cagla Ipek, et al.
Published: (2025)

EAGER: Edge-Aligned LLM Defense for Robust, Efficient, and Accurate Cybersecurity Question Answering
by: Gungor, Onat, et al.
Published: (2025)

AQUA-LLM: Evaluating Accuracy, Quantization, and Adversarial Robustness Trade-offs in LLMs for Cybersecurity Question Answering
by: Gungor, Onat, et al.
Published: (2025)

CyberMaskQA: A Privacy-Aware Benchmark for Evaluating Large Language Models in Cybersecurity Question Answering
by: Gaddi, Matilda, et al.
Published: (2026)

CAN-QA: A Question-Answering Benchmark for Reasoning over In-Vehicle CAN Traffic
by: Chen, Jing, et al.
Published: (2026)

INTARG: Informed Real-Time Adversarial Attack Generation for Time-Series Regression
by: Tokgoz, Gamze Kirman, et al.
Published: (2026)

ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
by: Kocal, Cagla Ipek, et al.
Published: (2025)

Deep Reinforcement Learning for Intrusion Detection in IoT: A Survey
by: Gueriani, Afrah, et al.
Published: (2024)

Federated Learning-Enhanced Blockchain Framework for Privacy-Preserving Intrusion Detection in Industrial IoT
by: Ali, Anas, et al.
Published: (2025)

Optimized IoT Intrusion Detection using Machine Learning Technique
by: Mahmud, Muhammad Zawad, et al.
Published: (2024)

CITADEL: A Semi-Supervised Active Learning Framework for Malware Detection Under Continuous Distribution Drift
by: Haque, Md Ahsanul, et al.
Published: (2025)

Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems
by: Gueriani, Afrah, et al.
Published: (2024)

Enhancing Cybersecurity in IoT Networks: A Deep Learning Approach to Anomaly Detection
by: Pang, Yining, et al.
Published: (2024)

Empowering IoT Security: On-Device Intrusion Detection in Resource Constrained Devices
by: Ieropoulos, Vasilis, et al.
Published: (2026)

Hybrid Machine Learning Models for Intrusion Detection in IoT: Leveraging a Real-World IoT Dataset
by: Akif, Md Ahnaf, et al.
Published: (2025)

Incremental Federated Learning for Intrusion Detection in IoT Networks under Evolving Threat Landscape
by: Rehman, Muaan Ur, et al.
Published: (2026)

Optimizing IoT Intrusion Detection with Tabular Foundation Models for Smart City Forensics
by: Al-Dahmani, Asma, et al.
Published: (2026)

Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
by: Panopoulos, Ioannis, et al.
Published: (2025)

Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning
by: Bas, Sumeyye, et al.
Published: (2025)

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack
by: Zhan, Zhonghao, et al.
Published: (2025)

Open Set Dandelion Network for IoT Intrusion Detection
by: Wu, Jiashu, et al.
Published: (2023)

A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks
by: Menssouri, Safaa, et al.
Published: (2025)

Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks
by: Amouri, Amar, et al.
Published: (2024)

Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning
by: Atitallah, Safa Ben, et al.
Published: (2024)

Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks
by: Al-Hammouri, Mohammad F., et al.
Published: (2025)

FLARE: Feature-based Lightweight Aggregation for Robust Evaluation of IoT Intrusion Detection
by: Boswell, Bradley, et al.
Published: (2025)

Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
by: Wulnye, Fortunatus Aabangbio, et al.
Published: (2026)

LiteShield: Hybrid Feature Selection-Driven Lightweight Intrusion Detection for Resource-Constrained IoT Networks
by: Mabulage, Dileepa, et al.
Published: (2026)

Lightweight Autoencoder-Isolation Forest Anomaly Detection for Green IoT Edge Gateways
by: Jamshidi, Saeid, et al.
Published: (2025)

Privacy Preserving Anomaly Detection on Homomorphic Encrypted Data from IoT Sensors
by: Hangan, Anca, et al.
Published: (2024)

Self-adaptive Traffic Anomaly Detection System for IoT Smart Home Environments
by: Watanabe, Naoto, et al.
Published: (2024)

OptiFLIDS: Optimized Federated Learning for Energy-Efficient Intrusion Detection in IoT
by: Elouardi, Saida, et al.
Published: (2025)

Enhancing Autonomous Online Intrusion Detection for IoT with Balanced Learning, Reliable Pseudo-Labels, and Lightweight Architectures
by: Afzaal, Hanzala, et al.
Published: (2026)

Carbon-Aware Intrusion Detection: A Comparative Study of Supervised and Unsupervised DRL for Sustainable IoT Edge Gateways
by: Jamshidi, Saeid, et al.
Published: (2025)