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- Title
Learning autoencoder ensembles for detecting malware hidden communications in IoT ecosystems.
- Authors
Cassavia, Nunziato; Caviglione, Luca; Guarascio, Massimo; Liguori, Angelica; Zuppelli, Marco
- Abstract
Modern IoT ecosystems are the preferred target of threat actors wanting to incorporate resource-constrained devices within a botnet or leak sensitive information. A major research effort is then devoted to create countermeasures for mitigating attacks, for instance, hardware-level verification mechanisms or effective network intrusion detection frameworks. Unfortunately, advanced malware is often endowed with the ability of cloaking communications within network traffic, e.g., to orchestrate compromised IoT nodes or exfiltrate data without being noticed. Therefore, this paper showcases how different autoencoder-based architectures can spot the presence of malicious communications hidden in conversations, especially in the TTL of IPv4 traffic. To conduct tests, this work considers IoT traffic traces gathered in a real setting and the presence of an attacker deploying two hiding schemes (i.e., naive and "elusive" approaches). Collected results showcase the effectiveness of our method as well as the feasibility of deploying autoencoders in production-quality IoT settings.
- Subjects
COMPUTER network traffic; CYBERTERRORISM; TELECOMMUNICATION systems; COMMUNICATIVE competence; LEAKS (Disclosure of information); INTRUSION detection systems (Computer security); BOTNETS
- Publication
Journal of Intelligent Information Systems, 2024, Vol 62, Issue 4, p925
- ISSN
0925-9902
- Publication type
Article
- DOI
10.1007/s10844-023-00819-8