Modeling Malware-driven Honeypots

The current cybersecurity ecosystem is proving insufficient in today’s increasingly sophisticated cyber attacks. Malware authors and intruders have pursued innovative avenues to circumvent emulated monitoring systems (EMSs) such as honeypots, virtual machines, sandboxes and debuggers to continue with their malicious activities while remaining inconspicuous. Cybercriminals are improving their ability to detect EMS, by finding indicators of deception (IoDs) to expose their presence and avoid detection. It is proving a challenge for security analysts to deploy and manage EMS to evaluate their deceptive capability. In this paper, we introduce the Hydrakon framework, which is composed of an EMS controller and several Linux and Windows 10 clients. The EMS controller automates the deployment and management of the clients and EMS for the purpose of measuring EMS deceptive capabilities. Experiments were conducted by applying custom detection vectors to client real machines, virtual machines and sandboxes, where various artifacts were extracted and stored as csv files on the EMS controller. The experiment leverages the cosine similarity metric to compare and identify similar artifacts between a real system and a virtual machine or sandbox. Our results show that Hydrakon offers a valid approach to assess the deceptive capabilities of EMS without the need to target specific IoD within the target system, thereby fostering more robust and effective emulated monitoring systems.

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Modeling Malware-driven Honeypots

Cited by 2 publications

References 5 publications

Cyberattack Graph Modeling for Visual Analytics

Cyberattack Graph Modeling for Visual Analytics

Hydrakon, a Framework for Measuring Indicators of Deception in Emulated Monitoring Systems

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