Classification of Malware in HTTPs Traffic Using Machine Learning Approach-Reference-Cited by-同舟云学术

Classification of Malware in HTTPs Traffic Using Machine Learning Approach

Published:2022-01-13 Issue: Volume: Page:
ISSN:2148-3736
Container-title:El-Cezeri Fen ve Mühendislik Dergisi
language:
Short-container-title:

Author:

SİNGH Abhay

Publisher

El-Cezeri: Journal of Science and Engineering

Subject

General Physics and Astronomy,General Engineering,General Chemical Engineering,General Chemistry,General Computer Science

Reference29 articles.

1. [1]. Wang, W., Zhu, M.,Zeng,X., et.al., “Malware traffic classification using convolutional neural network for representation learning” in international conference on information networking (ICOIN), pp 712-717, IEEE, 2017.

2. [2]. C. McCarthy et al., “An investigation on identifying SSL traffic,” in Computational Intelligence for Security and Defense Applications (CISDA), IEEE Symposium on. IEEE, pp. 115–122, 2011.

3. [3]. Husák, M., Čermák, M., Jirsík, T. and Čeleda, P., “HTTPS traffic analysis and client identification using passive SSL/TLS fingerprinting" EURASIP Journal on Information Security, pp.1-14, 2016.

4. [4]. Becker, Jamin. “A Free, Online PCAP Analysis Engine.” Available at: www.packettotal.com/.

5. [5]. “Wireshark.” Wireshark • Go Deep., Available at: www.wireshark.org/.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identification and Classification of Malicious and Benign URL using Machine Learning Classifiers;2023 7th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC);2023-10-11