Anteater: Advanced Persistent Threat Detection With Program Network Traffic Behavior

Yangzong Zhang, Wenjian Liu, Kaiian Kuok, Ngai Cheong

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

Recent stealth attacks cleverly disguise malicious activities, masquerading as ordinary connections to popular online services through seemingly innocuous applications. These methods often evade detection by traditional network monitoring or signature-based techniques, as attackers frequently hide Command and Control (C&C) servers within well-known cloud service providers, making the traffic anomalies appear normal. In this paper, we introduce an application-level monitoring system, Anteater. Anteater constructs a detailed profile for each legitimate software's network traffic behavior, outlining the expected traffic patterns. By scrutinizing a program's network traffic configuration, Anteater efficiently pinpoints and intercepts the IP addresses associated with abnormal program access. Implemented in a real-world enterprise environment, Anteater was tested on a dataset containing over 400 million real-world network traffic sessions. The evaluation results demonstrate that Anteater achieves a high detection rate for malware injections, boasting a true positive rate of 94.5% and a false positive rate of less than 0.1%.

原文English
頁(從 - 到)8536-8551
頁數16
期刊IEEE Access
12
DOIs
出版狀態Published - 2024

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