Survey of encrypted malicious traffic detection based on deep learning
| Autor: | ZHAI Mingfang, ZHANG Xingming, ZHAO Bo |
|---|---|
| Jazyk: | English<br />Chinese |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | 网络与信息安全学报, Vol 6, Iss 3, Pp 59-70 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2096-109x 2096-109X |
| DOI: | 10.11959/j.issn.2096-109x.2020034 |
| Popis: | With the increasing awareness of network security, encrypted communication dominates and encrypted traffic grows rapidly. Traffic encryption, while protecting privacy, also masks illegal attempts and changes the form of threats. As one of the most important branch of machine learning, deep learning performs well in traffic classification. For several years, research on deep-learning based intrusion detection has been deepened and achieved good results. The steps of encrypted malicious traffic detection were introduced to be a general detection framework model named “six-step method”. Then, discussion and induction of data processing and detection algorithms were carried out combined with this model. Both advantages and disadvantages of various algorithm models were given as well. Finally, future research directions were pointed out with a view to providing assistance for further research. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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