DECANTeR: DEteCtion of Anomalous outbouNd HTTP Traffic by Passive Application Fingerprinting

Riccardo Bortolameotti, Thijs van Ede, Marco Caselli, Maarten Everts, Pieter Hartel, Rick Hofstede, Willem Jonker, Andreas Peter

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

13 Citations (Scopus)
129 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 33rd Annual Computer Security Applications Conference, ACSAC 2017
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery (ACM)
Pages373-386
Number of pages14
VolumePart F132521
ISBN (Electronic)978-1-4503-5345-8
DOIs
Publication statusPublished - 2017
EventACSAC 2017: 33th Annual Computer Security Applications Conference - Orlando, FL, United States
Duration: 4 Dec 20178 Dec 2017

Conference

ConferenceACSAC 2017
CountryUnited States
CityOrlando, FL
Period4/12/178/12/17

Keywords

  • Anomaly Detection
  • Application Fingerprinting
  • Data Exfiltration
  • Data Leakage
  • Network Security

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