Cover Image

Binary Anomaly Detection in Streaming IoT Traffic under Concept Drift

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Carnier, Rodrigo Matos, Lahesoo, Laura, Fukuda, Kensuke
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GothX: a generator of customizable, legitimate and malicious IoT network traffic
by: Poisson, Manuel, et al.
Published: (2024)

Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway
by: Palmese, Fabio, et al.
Published: (2025)

A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data
by: Rezakhani, Mahshid, et al.
Published: (2025)

Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD
by: Ghajari, Ghazal, et al.
Published: (2025)

Unsupervised Anomaly Detection for Smart IoT Devices: Performance and Resource Comparison
by: Sami, Md. Sad Abdullah, et al.
Published: (2025)

Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams
by: Gower-Winter, Brandon, et al.
Published: (2024)

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

SD-CGAN: Conditional Sinkhorn Divergence GAN for DDoS Anomaly Detection in IoT Networks
by: Onyeka, Henry, et al.
Published: (2025)

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)

Efficient Network Traffic Feature Sets for IoT Intrusion Detection
by: Silva, Miguel, et al.
Published: (2024)

Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)

Smart Surveillance: Identifying IoT Device Behaviours using ML-Powered Traffic Analysis
by: Ryan, Reza, et al.
Published: (2025)

IoT Network Traffic Analysis with Deep Learning
by: Liu, Mei, et al.
Published: (2024)

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

Optimizing IoT Threat Detection with Kolmogorov-Arnold Networks (KANs)
by: Emelianova, Natalia, et al.
Published: (2025)

Evasion of IoT Malware Detection via Dummy Code Injection
by: Zargarzadeh, Sahar, et al.
Published: (2026)

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

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
by: Sabbah, Ahmed, et al.
Published: (2025)

Concept Drift Detection using Ensemble of Integrally Private Models
by: Varshney, Ayush K., et al.
Published: (2024)

Optimized Deep Learning Models for Malware Detection under Concept Drift
by: Maillet, William, et al.
Published: (2023)

Towards Efficient Machine Learning Method for IoT DDoS Attack Detection
by: Modi, P
Published: (2024)

Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing
by: Mainuddin, Md, et al.
Published: (2024)

FARM: Few-shot Adaptive Malware Family Classification under Concept Drift
by: Guldemir, Numan Halit, et al.
Published: (2026)

Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT
by: Khatun, Mirza Akhi, et al.
Published: (2024)

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

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

A Comparative Analysis of Machine Learning Models for DDoS Detection in IoT Networks
by: Shakya, Sushil, et al.
Published: (2024)

MADCAT: Combating Malware Detection Under Concept Drift with Test-Time Adaptation
by: Roh, Eunjin, et al.
Published: (2025)

ADAPT: A Pseudo-labeling Approach to Combat Concept Drift in Malware Detection
by: Alam, Md Tanvirul, et al.
Published: (2025)

Federated PCA on Grassmann Manifold for IoT Anomaly Detection
by: Nguyen, Tung-Anh, et al.
Published: (2024)

In-network Attack Detection with Federated Deep Learning in IoT Networks: Real Implementation and Analysis
by: Chaudhary, Devashish, et al.
Published: (2026)

SunBlock: Cloudless Protection for IoT Systems
by: Safronov, Vadim, et al.
Published: (2024)

Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
by: Kalný, Tomáš, et al.
Published: (2026)

Thwarting Cybersecurity Attacks with Explainable Concept Drift
by: Shaer, Ibrahim, et al.
Published: (2024)

Analyzing Consumer IoT Traffic from Security and Privacy Perspectives: a Comprehensive Survey
by: Jia, Yan, et al.
Published: (2024)

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

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

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

PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments
by: Chathoth, Ajesh Koyatan, et al.
Published: (2025)

Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Models
by: Mithsara, W. K. M, et al.
Published: (2025)