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One of the widely used cyber deception techniques is decoying, wheredefenders create fictitious machines (i.e., honeypots) to lure attackers.Honeypots are deployed to entice attackers, but their effectiveness depends ontheir configuration as that would influence whether attackers will judge themas "real" machines or not. In this work, we study two-sided deception, where wemanipulate the observed configuration of both honeypots and real machines. Theidea is to improve cyberdefense by either making honeypots ``look like'' realmachines or by making real machines ``look like honeypots.'"We identify themodifiable features of both real machines and honeypots and conceal thesefeatures 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 40machines where 20 of them are honeypots. We manipulate the features of themachines, and using an experimental testbed (HackIT), we test the effectivenessof the decoying strategies against humans attackers. Results indicate that: Anyof the two forms of deception (conceal honeypots and conceal both honeypots andreal machines) is better than no deception at all. We observe that attackersattempted more exploits on honeypots and exfiltrated more data from honeypotsin the two forms of deception conditions. However, the attacks on honeypots anddata exfiltration were not different within the deception conditions. Resultsinform cybersecurity defenders on how to manipulate the observable features ofhoneypots and real machines to create uncertainty for attackers and improvecyberdefense.

Decoys in Cybersecurity: An Exploratory Study to Test the Effectiveness of 2-sided Deception