Encrypted Malware Traffic Detection via Graph-based Network Analysis-Reference-Cited by-同舟云学术

Encrypted Malware Traffic Detection via Graph-based Network Analysis

Published:2022-10-26 Issue: Volume: Page:
ISSN:
Container-title:25th International Symposium on Research in Attacks, Intrusions and Defenses
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Author:

Fu Zhuoqun¹,Liu Mingxuan¹,Qin Yue²,Zhang Jia¹,Zou Yuan³,Yin Qilei¹,Li Qi¹,Duan Haixin⁴

Affiliation:

1. Tsinghua University, China

2. Indiana University Bloomington, United States of America

3. Tsinghua University; GeekSec Security Group, China

4. Tsinghua University; Qi An Xin Group Corp., China

Funder

Huawei Technologies Co., Ltd

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3545948.3545983

Reference74 articles.

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2. Blake Anderson and David McGrew. 2016. Identifying Encrypted Malware Traffic with Contextual Flow Data. In AISec@CCS. 35–46. Blake Anderson and David McGrew. 2016. Identifying Encrypted Malware Traffic with Contextual Flow Data. In AISec@CCS. 35–46.

3. Blake Anderson and David McGrew. 2017. Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity. In SIGKDD. 1723–1732. Blake Anderson and David McGrew. 2017. Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity. In SIGKDD. 1723–1732.

4. AndroidDev. 2021. Developer Guides-SSL Socket . https://developer.android.com/reference/javax/net/ssl/SSLSocketAccessed January 22, 2022 . AndroidDev. 2021. Developer Guides-SSLSocket. https://developer.android.com/reference/javax/net/ssl/SSLSocketAccessed January 22, 2022.

5. Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML Vol. 97. PMLR 695–704. Aleksandar Bojchevski and Stephan Günnemann. 2019. Adversarial Attacks on Node Embeddings via Graph Poisoning. In ICML Vol. 97. PMLR 695–704.

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