Transfer Learning for Internet of Things Malware Analysis

Karanja, Evanson Mwangi; Masupe, Shedden; Jeffrey, Mandu

doi:10.1007/978-3-030-38501-9_20

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…The main objective of TL is to utilize the knowledge from the source, i.e., D S and T S , to improve the learning process of the target task, i.e., learn f T (.). Based on the availability of labeled data and the difference among D S , T S , D T , and T T , TL can be classified into inductive (D S = D T , T S = T T , labeled target data), transductive (D S = D T , T S = T T , labeled source data), and unsupervised TL (unlabeled target and source data) [6], [36], [37]. With the precious properties in inheriting knowledge from bigger source, TL has been widely applied in various sections especially in cybersecurity to detect cyberattack in IoT network [6], [23], [38].…”

Section: Transfer Learningmentioning

confidence: 99%

Collaborative Learning Model for Cyberattack Detection Systems in IoT Industry 4.0

Khoa

Saputra

Hoang

et al. 2020

2020 IEEE Wireless Communications and Networking Conference (WCNC)

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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. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn "knowledge" from a source network that possesses abundant labeled data. It is important that the stateof-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. However, our 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% as compared to the state-of-the-art deep learning based approaches.

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