# Internet of Drones Intrusion Detection Using Deep Learning
## 6 Conclusions  
The field of FANET has gained huge attention in recent years. It has been used in many civilian and military applications. However, there is still a gap in their securities. Current cryptography techniques might not be appropriate due to their complexities and power cost. Moreover, intrusion into such flying robots could be more dangerous than jamming or message interception. Therefore, this paper is considered one step towards an intelligent intrusion detection framework for Drones. The framework is based on two separate components: the drone and the Base station. It utilizes a particular type of RNN for both components, which is RNN-LSTM. The results showed superior performance with average performance, almost 15 on average, over some of the recent algorithms. One of the main restrictions of this paper is applying the proposed framework over real FANET. This is one of the future works following this paper. Another future direction is secure communication between the drones and the base station as well as between the drones themselves. In our future work, we intend to extend the proposed framework in this paper by implementing a lightweight algorithm based on a linear hyperbolic chaotic system of partial differential equations. The chaotic system is mainly applied to drones and satellite communication. Moreover, examining the cyberattacks against the application-layer communication of drones using different methods could be another interesting work to be conducted in a separate paper.  
**Author Contributions:** Conceptualization, R.A.R.; A.-H.E. and M.A.-S.; methodology, R.A.R. and M.A.-S.; software, R.A.R.; validation, R.A.R., A.-H.E., M.A.-S. and M.E.; formal analysis, R.A.R. and A.-H.E.; investigation, R.A.R. and A.-H.E.; resources, R.A.R. and A.-H.E.; data curation, R.A.R. and M.A.-S.; writing--original draft preparation, R.A.R.; A.-H.E., M.A.-S. and M.E.; writing--review and editing, R.A.R.; A.-H.E.; M.A.-S. and M.E.; visualization, R.A.R., A.-H.E. and M.E.; supervision, R.A.R. and A.-H.E.; project administration, R.A.R.; funding acquisition, R.A.R.; A.-H.E.; M.A.-S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors would like to acknowledge the support of Prince Sultan University for paying the Article Processing Charges (APC) of this publication.  
**Data Availability Statement:** KDDCup 99 ([[http://kdd.ics.uci.edu/databases/kddcup99/kddcup9](http://kdd.ics.uci.edu/databases/kddcup99/kddcup9)]([http://kdd.ics.uci.edu/databases/kddcup99/kddcup9](http://kdd.ics.uci.edu/databases/kddcup99/kddcup9)) 9.html); NSL-KDD ([[https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)]([https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)), accessed on 11 October 2021); UNSW-NB15 ([[https://research.unsw.edu.au/projects/unsw-nb15-dataset](https://research.unsw.edu.au/projects/unsw-nb15-dataset)]([https://research.unsw.edu.au/projects/unsw-nb15-dataset](https://research.unsw.edu.au/projects/unsw-nb15-dataset)), accessed on 11 October 2021); Kyoto ([[http://www.takakura.com/Kyoto_data/](http://www.takakura.com/Kyoto_data/)]([http://www.takakura.com/Kyoto_data/](http://www.takakura.com/Kyoto_data/)), accessed on 11 October 2021); CICIDS2017 ([[http://www.unb.ca/cic/datasets/ids-2017.html](http://www.unb.ca/cic/datasets/ids-2017.html)]([http://www.unb.ca/cic/datasets/ids-2017.html](http://www.unb.ca/cic/datasets/ids-2017.html)), accessed on 11 October 2021); TON_IoT ([[https://ieee-dataport.org/documents/toniot-datasets](https://ieee-dataport.org/documents/toniot-datasets)]([https://ieee-dataport.org/documents/toniot-datasets](https://ieee-dataport.org/documents/toniot-datasets)), accessed on 11 October 2021).  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 30: CDD graph for average multiclass classification.