Comparative Analysis of Machine Learning Methods for Classification of IoT Malware Attacks-Reference-Cited by-同舟云学术

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Comparative Analysis of Machine Learning Methods for Classification of IoT Malware Attacks

Published:2022 Issue: Volume: Page:157-167
ISSN:2367-3370
Container-title:Soft Computing: Theories and Applications
language:
Short-container-title:

Author:

Chauhan Kshamta,Gandotra Ekta

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-0707-4_16

Reference25 articles.

1. Ngo QD, Nguyen HT, Le VH, Nguyen DH (2020) A survey of IoT malware and detection methods based on static features. ICT Express 6:280–286

2. Abusnaina A, Khormali A, Alasmary H, Park J, Anwar A, Mohaisen A (2019) Adversarial learning attacks on graph-based IoT malware detection systems. In: Proceedings—international conference on distributed computing systems 2019-July, pp 1296–1305

3. Bendiab G, Shiaeles S, Alruban A, Kolokotronis N (2020) IoT malware network traffic classification using visual representation and deep learning

4. Kolias C, Kambourakis G, Stavrou A, Voas J (2017) DDoS in the IoT: Mirai and other botnets. Computer (Long Beach Calif) 50:80–84

5. Sinanovic HS, Mrdovic S (2017) Analysis of Mirai malicious software

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1. Risk Analysis of Android Applications using Static Permissions and Convolutional Neural Network;Proceedings of the 2023 Fifteenth International Conference on Contemporary Computing;2023-08-03

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