The paper introduces a framework for detecting performance drift in Machine Learning as a Service (MLaaS) tailored to Internet of Things (IoT) settings. It first builds an extraction model that learns the service’s behavior from input‑output pairs, then uses this to jointly monitor changes in data and service behavior. An adaptive temporal mechanism adjusts monitoring frequency, and experiments on real datasets show significant accuracy gains and reduced miss‑detection rates compared to baseline methods.
By Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Erik Elmroth, Aneesh Krishna, Monowar Bhuyan
arXiv:2608. 15465v1 Announce Type: cross Abstract: Identification of IoT device types from passive traffic is increasingly used for security management in enterprise and ISP networks.
By Shayan Azizi, Norihiro Okui, Masataka Nakahara, Ayumu Kubota, Gustavo Batista, Hassan Habibi Gharakaheili
arXiv:2606. 07685v1 Announce Type: cross Abstract: The dynamic nature of Internet of Things (IoT) environments affects the long-term effectiveness of Machine Learning as a Service (MLaaS) compositions.
By Deepak Kanneganti, Sajib Mistry, Sheik Mohammad Mostakim Fattah, Aneesh Krishna
arXiv:2606. 14710v1 Announce Type: cross Abstract: Edge-resident AI agents increasingly span home servers, IoT hubs, laptops, and phones, yet their coordination stacks still assume cloud-style transports or a central relay.
By Zhonghao Zhan, Yefan Zhang, Hamed Haddadi
arXiv:2607. 09653v1 Announce Type: cross Abstract: Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches.
By Katherine Swinea, Kshitiz Aryal, Lopamudra Praharaj, Maanak Gupta
arXiv:2606. 30701v1 Announce Type: cross Abstract: As the Internet of Things (IoT) continues its rapid expansion, the attack surface grows accordingly, with emerging threats targeting smart objects and their interactions.
By Marco Arazzi, Mert Cihangiroglu, Serena Nicolazzo, Antonino Nocera, Vinod P
arXiv:2304. 13905v2 Announce Type: replace-cross Abstract: While the use of the Internet of Things is becoming more and more popular, many security vulnerabilities are emerging with the large number of devices being introduced to the market.
By Kahraman Kostas
Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and adaptive security testing approaches. While recent adoptions of Large Language Model (LLM) agents have demonstrated promise in penetration testing and Capture-the-Flag (CTF) environments, their application to IoT specific vulnerabilities remains unexplored.
arXiv:2412.06210v3 Announce Type: replace
Abstract: With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use...
By Jiechao Gao, Yuangang Li, Jie Wang, Yue Zhao, Michael Lepech, Brad Campbell
arXiv:2510. 13817v2 Announce Type: replace Abstract: The growth of IoT devices in shared environments has outpaced our ability to identify them, posing urgent risks to privacy, safety, and accountability.
By Rameen Mahmood, Tousif Ahmed, Sai Teja Peddinti, Danny Yuxing Huang
arXiv:2608. 00118v1 Announce Type: cross Abstract: Cybersecurity is a fundamental requirement for protecting wearable devices used in healthcare Internet of Things (H-IoT) systems.
By Mirza Akhi
arXiv:2603.25507v2 Announce Type: replace-cross
Abstract: Network Traffic Classification (NTC) increasingly relies on data-driven models, yet its practical deployment is often constrained by limited...
By Giampaolo Bovenzi, Domenico Ciuonzo, Jonatan Krolikowski, Antonio Montieri, Alfredo Nascita, Antonio Pescap\`e, Dario Rossi