DECANTeR: DEteCtion of Anomalous outbouNd HTTP Traffic by Passive Application Fingerprinting
conference paper
We present DECANTeR, a system to detect anomalous outbound HTTP communication, which passively extracts fingerprints for each application running on a monitored host. The goal of our system is to detect unknown malware and backdoor communication indicated by unknown fingerprints extracted from a host's network traffic. We evaluate a prototype with realistic data from an international organization and datasets composed of malicious traffic. We show that our system achieves a false positive rate of 0.9% for 441 monitored host machines, an average detection rate of 97.7%, and that it cannot be evaded by malware using simple evasion techniques such as using known browser user agent values. We compare our solution with DUMONT [24], the current state-of-The-Art IDS which detects HTTP covert communication channels by focusing on benign HTTP traffic. The results show that DECANTeR outperforms DUMONT in terms of detection rate, false positive rate, and even evasion-resistance. Finally, DECANTeR detects 96.8% of information stealers in our dataset, which shows its potential to detect data exfiltration. © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. Applied Computer Security Associates (ACSA)
Topics
TNO Identifier
782916
ISBN
9781450353458
Publisher
Association for Computing Machinery
Source title
33rd Annual Computer Security Applications Conference, ACSAC 2017. 4 December 2017 through 8 December 2017
Pages
373-386
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