Snap7 Honeypot Honeypot Detection Scanner

Snap7 Honeypot is utilized primarily for research and security testing purposes rather than in production environments. It simulates the Siemens S7 PLC communication protocol to identify unauthorized access attempts and gather intelligence on potential threats. Security researchers use it to study threats in Industrial Control Systems (ICS) by running honeypots in controlled environments. The purpose is to detect anomalies and unauthorized interactions with the ICS systems, allowing for improved security measures. Snap7 Honeypot is valuable for understanding the tactics used by attackers and enhancing defensive strategies. The software is an integral tool for cybersecurity experts focusing on network environments where S7 communications are used.

The detection focuses on identifying Snap7 Honeypot set up with default configurations, which are commonly used in research or security testing scenarios. Honeypot detection is crucial as it differentiates between genuine industrial systems and intentional decoys. The honeypot's default settings make it recognizable through specific response patterns when S7comm requests are analyzed. This detection helps in understanding the presence of honeypots within a network, facilitating insights into network security postures. By identifying these setups, the scanner helps in discerning efforts to lure attackers for security research or to bait them in misleading attempts.

The technical detection involves analyzing response patterns specific to the Snap7 Honeypot when S7comm protocol requests are made. It includes sending specially crafted data packets to the suspected honeypot on designated ports, usually port 102. The detection engine looks for characteristic responses from the honeypot which are not typical of genuine industrial control systems. The binary matchers in the template verify the presence of known honeypot signatures. Response matches confirm the operation of a honeypot rather than a genuine S7 server. This technique leverages unique response codes or sequences that are consistently used by Snap7 honeypots in default configurations.

When a honeypot like Snap7 is detected, the effects include potential misinterpretations of network security assessments. Attackers might avoid the network, believing it to be a trap, thereby leaving actual vulnerabilities unchecked. Conversely, successfully identifying honeypots can lead attackers to adjust their strategies, aiming to bypass these decoys. Organizations deploying honeypots may gather valuable interaction data, but those detected as such could lose effectiveness in misleading adversaries. Dismissed threats due to this detection could shift attackers to other operational targets that may not have such traps in place. Overall, well-configured honeypots assist in proactive defense but necessitate constant updates to maintain their disguise.

REFERENCES

• https://github.com/sefcom/honeyplc
• https://medium.com/@biero-llagas/simple-use-of-the-python-snap7-lib-now-with-real-honeypot-11a2979baaf0