# Attack 3 sudo hive-passwd j65 kj675; sudo pkill Xorg; sudo pkill x11 vnc; uname -a
## 7 Conclusions and Future Works  
In this paper, we presented our design and implementation of a self-guarded adaptive honeypot for the SSH protocol and compared two variants of it with Cowrie, a widely used medium-interaction honeypot and a high-interaction honeypot, Aster, that we also set up for this comparative study.  
The experimental results show that the adaptive self-guarded honeypot Asgard performs best by leveraging Reinforcement Learning to learn to compromise between collecting attack data and keeping the system safe. This shows that Asgard can be deployed with a lesser risk of security than the high-interaction honeypot. Another benefit that is worth repeating is the usage of the real system as a honeypot, thanks to the proxy-based architecture and implementation. This can avoid the problem of the medium-interaction honeypot being fingerprinted by the adversaries because of their limited and simulated functions.  
The resulting \(q\)-values clearly show that the coupling of the environment state with the action in the reward function helps Asgard effectively learn its two objectives, as opposed to Midgard, which only uses the environment state to define its own reward function.  
Nevertheless, there are still some limitations to our Asgard system:  
* Its reward function encourages the agent to block or substitute the execution of malicious programs; this can prevent us from observing more complex attacks that could happen after the execution of these programs. The attacker can also use this feature to fingerprint our system by executing a test program.
* The implementation of our proxy system is still limited, as during the attacks, we were not able to deal with all of the command types, especially some complex commands such as pipeline commands and compound commands.
* The experiments were conducted only one time, and on the same network; as a result, we could not draw a definitive answer to all of our claims. It could have been tested in more different locations.
* Adaptive honeypots can also be subject to being fingerprinted by the adversaries by assuming all the honeypots are adaptive, in this case, they can launch 'blind attacks' by creating fake attacks to fingerprint these systems [35].  
As for our future works, we intend to extend our adaptive self-guarded honeypot to enrich its description of the environment state by including the commands and their arguments, and also information about the honeypot system itself, such as CPU, memory and network flow. Indices of compromise coming from external IDS could also be used to create a richer state. As mentioned in the limitations, we see the potential by changing the static reward function to a more dynamic reward function to allow the execution of malicious programs for a certain degree of risk, which will bridge the gap between Aster and Asgard, and this, in turn, could achieve a better result. Another direction that we want to work on is to deploy several adaptive self-guarded honeypots in different locations and allow them to exchange and exploit their learned models. We believe that this will speed up the learning process.  
Conceptualisation, S.T. and J.-N.C.; methodology, S.T.; software, S.T.; validation, S.T. and J.-N.C.; investigation, S.T.; data curation, S.T.; writing--original draft preparation, S.T.; writing--review and editing, J.-N.C.; visualisation, S.T.; supervision, J.-N.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy concerns.  
The authors declare no conflict of interest.