2021/08/25 by Palvi Aggarwal, Aggarwal, Palvi, Yinuo Du +6
Computer Science · #Advanced Malware Detection Techniques #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information and Cyber Security #Network Security and Intrusion Detection #cs.CR
paper · pdf · doi:10.48550/arxiv.2108.11037
Presented at 1st International Workshop on Adaptive Cyber Defense, 2021 (arXiv:2108.08476)
arxiv created 2021/08/25 · openalex publication_date 2021/08/25 · arxiv updated 2021/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the widely used cyber deception techniques is decoying, where defenders create fictitious machines (i.e., honeypots) to lure attackers. Honeypots are deployed to entice attackers, but their effectiveness depends on their configuration as that would influence whether attackers will judge them as "real" machines or not. In this work, we study two-sided deception, where we manipulate the observed configuration of both honeypots and real machines. The idea is to improve cyberdefense by either making honeypots ``look like'' real machines or by making real machines ``look like honeypots.'"We identify the modifiable features of both real machines and honeypots and conceal these features to different degrees. In an experiment, we study three conditions: default features on both honeypot and real machines, concealed honeypots only, and concealed both honeypots and real machines. We use a network with 40 machines where 20 of them are honeypots. We manipulate the features of the machines, and using an experimental testbed (HackIT), we test the effectiveness of the decoying strategies against humans attackers. Results indicate that: Any of the two forms of deception (conceal honeypots and conceal both honeypots and real machines) is better than no deception at all. We observe that attackers attempted more exploits on honeypots and exfiltrated more data from honeypots in the two forms of deception conditions. However, the attacks on honeypots and data exfiltration were not different within the deception conditions. Results inform cybersecurity defenders on how to manipulate the observable features of honeypots and real machines to create uncertainty for attackers and improve cyberdefense.