arXiv:2609.36039v1 Announce Type: cross
Abstract: Machine learning (ML) and deep learning (DL) have dominated Intrusion Detection System (IDS) research in recent years. Unfortunately, many existing s...
By Yufeng Xin, Bryant Goseland, Mohamed Rahouti
arXiv:2606. 05776v1 Announce Type: cross Abstract: With the rapid proliferation of IoT devices, security concerns have dramatically escalated and intrusion detection systems have become critical for protecting networked environments.
By Mohammad Tariq Ikhlas, Pohanyar Khowaja Khil, Malik Muhammad Mueed Aslam, Muhammad Khuram Shahzad
The paper introduces Unsupervised Graph Collective Anomaly Detection (UGCAD), a framework that uses a variational graph autoencoder to learn graph representations of IoT network traffic and then enhances clustering to group nodes. UGCAD identifies collective anomalies by aggregating normal clusters and applying anomaly scores to the refined groups. Experiments on CICIoT2023 and ToN-IoT datasets show that UGCAD outperforms traditional and state‑of‑the‑art clustering‑based CAD methods in both clustering quality and anomaly detection accuracy.
By Dalila Khettaf, Djamel Djenouri, Zeinab Rezaeifar, Youcef Djenouri
arXiv:2511. 22078v2 Announce Type: replace Abstract: Many real-world scenarios involving streaming information can be represented as temporal graphs, where data flows through dynamic changes in edges over time.
By Simone Mungari, Albert Bifet, Giuseppe Manco, Bernhard Pfahringer
arXiv:2606. 31594v1 Announce Type: cross Abstract: The Internet of Things (IoT) is rapidly growing and expanding into various sectors, such as healthcare, transportation, smart homes, and more.
By Rana Alharbi, Chuadhry Mujeeb Ahmed
arXiv:2609.39584v1 Announce Type: cross
Abstract: Edge computing has emerged as a critical computing paradigm in modern distributed systems by migrating data processing closer to end users and Intern...
By Zawad Yalmie Sazid, Robert Abbas