© The Institution of Engineering and Technology
The coordination between aerial and ground nodes has enhanced the versatility and quality of the traditional networks. The application of aerial systems in mission-critical operations, as well as civilian applications, brings in the context of safeguarding unmanned aerial systems (UAS) from malicious attackers. This study discusses the threats and attacks mounted on UAS, alongside the challenges introduced by the unmanned aerial vehicle (UAV) network structure itself. A framework for safeguarding UAS against malicious attackers and recovering the rogue UAVs is proposed in the study. The proposed framework enforces a dynamic conceptual grid-based layout over the actual geographical deployment. The dynamically shuffling grid ascertains the security of transmission channels, as every time the grid is shuffled periodically or based on abnormal behaviour, the safety paradigm is reinitiated. Public key cryptographic algorithms are deployed for securing the communication links. Neural networks-based predictions are used for detecting abnormality in behavioural, statistical, and mobility patterns. Principal component analysis based on multivariate statistical analysis is used for detecting outliers in the aerial network environment. The behaviour prediction and outlier detection algorithms significantly improve the overall performance of the network and provide immunity against the intruders with reduced false positives, high accuracy, and better detection rate.
References
-
-
1)
-
36. Van der Bergh, B., Chiumento, A., Pollin, S.: ‘LTE in the sky: trading off propagation benefits with interference costs for aerial nodes’, IEEE Commun. Mag., 2016, 54, (5), pp. 44–50.
-
2)
-
34. Ye, J., Zhang, C., Lei, H., et al: ‘Secure UAV-to-UAV systems with spatially random uavs’, IEEE Wirel. Commun. Lett., 2018, 8, (2), pp. 564–567.
-
3)
-
40. Cichonski, J., Franklin, J., Bartock, M.: , 2016.
-
4)
-
37. Amorim, R., Nguyen, H., Mogensen, P., et al: ‘Radio channel modeling for UAV communication over cellular networks’, IEEE Wirel. Commun. Lett., 2017, 6, (4), pp. 514–517.
-
5)
-
6)
-
32. Cui, M., Zhang, G., Wu, Q., et al: ‘Robust trajectory and transmit power design for secure uav communications’, IEEE Trans. Veh. Technol., 2018, 67, (9), pp. 9042–9046.
-
7)
-
2. Zeng, Y., Zhang, R., Lim, T.J.: ‘Wireless communications with unmanned aerial vehicles: opportunities and challenges’, IEEE Commun. Mag., 2016, 54, (5), pp. 36–42.
-
8)
-
31. Xiao, L., Xu, Y., Yang, D., et al: ‘Secrecy energy efficiency maximization for UAV-enabled mobile relaying’, IEEE Trans. Green Commun. Network., 2019, 4, (1), pp. 180–193.
-
9)
-
19. Bezemskij, A., Loukas, G., Anthony, R.J., et al: ‘Behaviour-based anomaly detection of cyber-physical attacks on a robotic vehicle’. In: 2016 15th Int. Conf. on Ubiquitous Computing and Communications and 2016 Int. Symp. on Cyberspace and Security (IUCC-CSS), Granada, Spain, 2016, pp. 61–68.
-
10)
-
5. Sharma, V., Kumar, R.: ‘A cooperative network framework for multi-UAV guided ground ad hoc networks’, J. Intell. Robot. Syst., 2015, 77, (3-4), pp. 629–652.
-
11)
-
33. Wang, Q., Chen, Z., Li, H., et al: ‘Joint power and trajectory design for physical-layer secrecy in the uav-aided mobile relaying system’, IEEE Access, 2018, 6, pp. 62849–62855.
-
12)
-
1. Sharma, V., Kumar, R.: ‘Cooperative frameworks and network models for flying ad hoc networks: a survey’, Concurrency Comput. Pract. Exp., 2017, 29, (4), p. e3931.
-
13)
-
35. Kang, H., Joung, J., Ahn, J., et al: ‘Secrecy-aware altitude optimization for quasi-static uav base station without eavesdropper location information’, IEEE Commun. Lett., 2019, 23, (5), pp. 851–854.
-
14)
-
28. Zhou, Y., Yeoh, P.L., Chen, H., et al: ‘Improving physical layer security via a uav friendly jammer for unknown eavesdropper location’, IEEE Trans. Veh. Technol., 2018, 67, (11), pp. 11280–11284.
-
15)
-
11. Moskvitch, K.: ‘Are drones the next target for hackers?’. .
-
16)
-
38. Fotouhi, A., Qiang, H., Ding, M., et al: ‘Survey on UAV cellular communications: practical aspects, standardization advancements, regulation, and security challenges’, IEEE Commun. Surv. Tutor., 2019, 4, pp. 3417–3442.
-
17)
-
15. Mitchell, R., Chen, R.: ‘Adaptive intrusion detection of malicious unmanned air vehicles using behavior rule specifications’, IEEE Trans. Syst. Man Cybern., Syst., 2013, 44, (5), pp. 593–604.
-
18)
-
7. Zhang, H., Song, L., Han, Z., et al: ‘Cooperation techniques for a cellular internet of unmanned aerial vehicles’, IEEE Wirel. Commun., 2019, 26, (5), pp. 167–173.
-
19)
-
20. Sharma, V., Choudhary, G., Ko, Y., et al: ‘Behavior and vulnerability assessment of drones-enabled industrial internet of things (IIOT)’, IEEE Access, 2018, 6, pp. 43368–43383.
-
20)
-
3. Menouar, H., Guvenc, I., Akkaya, K., et al: ‘UAV-enabled intelligent transportation systems for the smart city: applications and challenges’, IEEE Commun. Mag., 2017, 55, (3), pp. 22–28.
-
21)
-
39. Bikos, A.N., Sklavos, N.: ‘LTE/SAE security issues on 4G wireless networks’, IEEE Secur. Priv., 2013, 11, (2), pp. 55–62.
-
22)
-
4. Jian, L., Li, Z., Yang, X., et al: ‘Combining unmanned aerial vehicles with artificial-intelligence technology for traffic-congestion recognition: electronic eyes in the skies to spot clogged roads’, IEEE Consumer Electron. Mag., 2019, 8, (3), pp. 81–86.
-
23)
-
21. Sharma, V., You, I., Chen, R., et al: ‘BRIoT: behavior rule specification-based misbehavior detection for IOT-embedded cyber-physical systems’, IEEE Access, 2019, 7, pp. 118556–118580.
-
24)
-
9. Goddin, D.: ‘There a new way to take down drones and it doesnot involve shotguns’. .
-
25)
-
10. Meunier, P.: ‘Drone flaw known since 1990s was a vulnerability’. .
-
26)
-
30. Wang, Q., Chen, Z., Li, H.: ‘Energy-efficient trajectory planning for UAV-aided secure communication’, China Communications, 2018, 15, (5), pp. 51–60.
-
27)
-
27. Lee, H., Eom, S., Park, J., et al: ‘UAV-aided secure communications with cooperative jamming’, IEEE Trans. Veh. Technol., 2018, 67, (10), pp. 9385–9392.
-
28)
-
42. LeCun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, (11), pp. 2278–2324.
-
29)
-
22. Sun, X., Ng, D.W.K., Ding, Z., et al: ‘Physical layer security in uav systems: challenges and opportunities’, IEEE Wirel. Commun., 2019, 26, (5), pp. 40–47.
-
30)
-
31)
-
23. Wang, Q., Chen, Z., Mei, W., et al: ‘Improving physical layer security using uav-enabled mobile relaying’, IEEE Wirel. Commun. Lett., 2017, 6, (3), pp. 310–313.
-
32)
-
29. Hua, M., Wang, Y., Wu, Q., et al: ‘Energy-efficient cooperative secure transmission in multi-uav-enabled wireless networks’, IEEE Trans. Veh. Technol., 2019, 68, (8), pp. 7761–7775.
-
33)
-
8. Bicchierai, L.F.: ‘Drone hijacking? That's just the start of GPS troubles’. .
-
34)
-
18. Sharma, V., Kumar, R., Srinivasan, K., et al: ‘Coagulation attacks over networked uavs: concept, challenges, and research aspects’, Int. J. Eng. Technol., 2018, 7, pp. 183–187.
-
35)
-
41. Hochreiter, S., Schmidhuber, J.: ‘Long short-term memory’, Neural Comput., 1997, 9, (8), pp. 1735–1780.
-
36)
-
25. Li, A., Wu, Q., Zhang, R.: ‘UAV-enabled cooperative jamming for improving secrecy of ground wiretap channel’, IEEE Wirel. Commun. Lett., 2018, 8, (1), pp. 181–184.
-
37)
-
6. Zhang, S., Zhang, H., He, Q., et al: ‘Joint trajectory and power optimization for UAV relay networks’, IEEE Commun. Lett., 2017, 22, (1), pp. 161–164.
-
38)
-
39)
-
24. Rudinskas, D., Goraj, Z., Stankūnas, J.: ‘Security analysis of UAV radio communication system’, Aviation, 2009, 13, (4), pp. 116–121.
-
40)
-
16. Sedjelmaci, H., Senouci, S.M., Ansari, N.: ‘A hierarchical detection and response system to enhance security against lethal cyber-attacks in uav networks’, IEEE Trans. Syst. Man Cybern., Syst., 2017, 48, (9), pp. 1594–1606.
-
41)
-
42)
-
26. Cai, Y., Cui, F., Shi, Q., et al: ‘Dual-uav-enabled secure communications: joint trajectory design and user scheduling’, IEEE J. Sel. Areas Commun., 2018, 36, (9), pp. 1972–1985.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-com.2020.0073
Related content
content/journals/10.1049/iet-com.2020.0073
pub_keyword,iet_inspecKeyword,pub_concept
6
6