no code implementations • 11 Apr 2023 • Ayyoob Hamza, Hassan Habibi Gharakheili, Theophilus A. Benson, Gustavo Batista, Vijay Sivaraman
(4) We demonstrate how our models scale in environments with a large number of connected IoTs (with datasets collected from a network of IP cameras in our university campus) by considering various training strategies (per device unit versus per device type), and balancing the accuracy of prediction against the cost of models in terms of size and training time.
no code implementations • 17 Jan 2023 • Arman Pashamokhtari, Norihiro Okui, Masataka Nakahara, Ayumu Kubota, Gustavo Batista, Hassan Habibi Gharakheili
Our contributions are three-fold: (1) We collect and analyze network traffic of 24 types of consumer IoT devices from 12 real homes over six weeks to highlight the challenge of temporal and spatial concept drifts in network behavior of IoT devices; (2) We analyze the performance of two inference strategies, namely "global inference" (a model trained on a combined set of all labeled data from training homes) and "contextualized inference" (several models each trained on the labeled data from a training home) in the presence of concept drifts; and (3) To manage concept drifts, we develop a method that dynamically applies the ``closest'' model (from a set) to network traffic of unseen homes during the testing phase, yielding better performance in 20% of scenarios.
no code implementations • 18 Mar 2022 • Arman Pashamokhtari, Gustavo Batista, Hassan Habibi Gharakheili
In this paper, we present AdIoTack, a system that highlights vulnerabilities of decision trees against adversarial attacks, helping cybersecurity teams quantify and refine the resilience of their trained models for monitoring IoT networks.
no code implementations • 5 Dec 2021 • Sharat Chandra Madanapalli, Alex Mathai, Hassan Habibi Gharakheili, Vijay Sivaraman
Our contributions are four-fold: (1) We analyze about 23, 000 video streams from Twitch and YouTube, and identify key features in their traffic profile that differentiate live and on-demand streaming.
no code implementations • 19 Apr 2021 • Iresha Pasquel Mohottige, Hassan Habibi Gharakheili, Vijay Sivaraman, Tim Moors
Universities worldwide are experiencing a surge in enrollments, therefore campus estate managers are seeking continuous data on attendance patterns to optimize the usage of classroom space.
1 code implementation • 7 Feb 2019 • Ayyoob Hamza, Dinesha Ranathunga, Hassan Habibi Gharakheili, Theophilus A. Benson, Matthew Roughan, Vijay Sivaraman
Our first contribution is to develop a tool that takes the traffic trace of an arbitrary IoT device as input and automatically generates the MUD profile for it.
Networking and Internet Architecture