13/09/2022: ThS. Tran Viet Khoa (AVITECH), Deep Transfer Learning: A Novel CollaborativeLearning Model for Cyberattack DetectionSystems in IoT Networks

Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. One of the major challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. The state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. In this talk, I will present a our proposed framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, it enables a target network with unlabeled data to effectively and quickly learn “knowledge” from a source network that possesses abundant labeled data. The proposed framework can address these problems by exchanging the learning “knowledge” among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% in efficiency as compared to the state-ofthe- art deep learning-based approaches.

Speaker: ThS. Trần Việt Khoa, AVITECH

Time: 15:30, Tuesday, September 13, 2022

Venue: Webinar