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Konate, Karim
- An Approach to Extracting Distributed Data from the Integrated Environment of Web Technologies based on Set Theory
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PDF Views:100
Authors
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1 Department of Mathematics and Computing, University Cheikh Anta DIOP, Dakar, SN
1 Department of Mathematics and Computing, University Cheikh Anta DIOP, Dakar, SN
Source
AIRCC's International Journal of Computer Science and Information Technology, Vol 11, No 3 (2019), Pagination: 29-44Abstract
The composition and extraction of the information distributed by the different web technologies allow a design of a multifunction imported data environment. However, the problems of integration and communication between the heterogeneous data web always (lodges) on distributed servers.
In fact, Web technologies have been proposed to meet certain needs related to heterogeneous and distributed information systems for communicating and exchanging computerized data on the Web. Further more, web generations go through client-server communication (from a static, dynamic and semantic web human client) to server-server communication.
For this purpose, Web services are technology application integration by excellence across the Internet. They operate independently of the heterogeneities of the system components on which they are based and are weakly coupled software components interacting with each other.
This paper aims to achieve a management approach to extracting and modeling distributed information based on set theory and calculate the execution time of a query to this distributed data.
Keywords
Web Technologies, Data, Distribution, Integration, Modeling, Query, Optimization.Full Text
References
- Nabil Ahmed Sultan, The Evolving Model for Software Delivery: The Case of Web and Semantic Services International Journal of Web Services Practices, Vol. 3, No.1-2 (2008), pp. 57-65
- Anderson, P. (2007). All That Glisters Is Not Gold' -- Web 2.0 And The Librarian. Journal of Librarianship and Information Science, 39 (4), 195–198.
- Isaías, P., Miranda, P., & Pífano, S. (2009). Critical Success Factors for Web 2.0 – A Reference Framework. Heidelberg: Springer-Verlag Berlin.
- Web 1.0 à Web 3.0 les étapes d’évolution, 2008, L'Atelier Informatique – Référencement – Création de sites http://www.atelier-informatique.org/internet/evolution-web-10-web-20-web-30/358/
- Macnamara, Jim (2010). University of Technology Sydney, Public communication practices in the Web 2.0-3.0 mediascape: The case for PRevolution. PRism 7(3): http://www.prismjournal.org
- Le monde du web Gérard Savoie, 2010 http://gerardsavoie.ca/web.html
- Laurence Allard, Revue MediaMorphoses n°21, septembre 2007, Armand Colin/INA Synthèses sur le Web 2.0
- Sankar, K., & Bouchard, S. A. (2009). Enterprise Web 2.0 Fundamentals. Indianapolis: Cisco Systems, Inc,.
- Boulakfouf, S., & Zampunieris, D. (2008). Integrating Web 2.0 Features into a Learning Management System. Paper presented at the 7th European Conference on e-Learning (ECEL'08), (pp. 127-133). Cyprus.
- Myhill, M., Shoebridge, M., & Snook, L. (2009). Virtual research environments – A Web 2.0 cookbook? Library Hi Tech , 27 (2), 228-238.
- Dwivedi, Yogesh; Williams, Michael; Mitra, Amit; Niranjan, Suraj; and Weerakkody, Vishanth, "Understanding advances in web technologies: evolution from web 2.0 to web 3.0" (2011). ECIS 2011 Proceedings. Paper 257.
- Principles of Distributed Database Systems M. T. Ozsu and P. Valduriez.: Second Edition. Prentice Hall, 1999.
- Techniques d'optimisation des requêtes dans les data warehouses Ladjel Bellatreche LISI/ENSMA, 2003
- Optimisation de requêtes OLAP en entrepôts de données Approche basée sur la fragmentation génétique, El houssain ZIYATI, 2010
- Pearson Education France – Optimisation des bases de données – Laurent Navarro, 2010 http://www.pearson.fr/resources/titles/27440100612040/extras/2412_chap06.pdf
- Abdul Mateen, Basit Raza, Mian Muhammad Awais, Muhammad Sher Selection of the Best DBMS: A Fuzzy based Multi-objective Decision Making Approach, Proceedings of the International MultiConference of Engineers and Computer Scientists 2011 vol I IMECS 2011, March 16 – 18, Hong Kong
- D. Litan, M. Velicanu, L. Copcea (Teohari), M. Teohari, A. M. Mocanu (Virgolici), I. Surugiu, and O. Raduta, Business’ New Requirement:Information Systems Integration – Methods and Technologies, INTERNATIONAL JOURNAL OF COMPUTERS AND COMMUNICATIONS Issue 3, Volume 5, 2011
- Inter systèmes Ensemble: Intégration pour l’entreprise en évolution rapide d’aujourd’hui, livre blanc, Sandra Rogers, IDC, Décembre 2003.
- BARR Mohamed, BELLATRECHE Ladjel, Approche dirigée par les fourmis pour la fragmentation horizontale dans les entrepôts de données relationnels, Nature & Technologie, page, 16 – 24, 2012
- Web Services in Distributed Information Systems: Availability, Performance and Composition; Zhao Xia, and al.,International Journal of Distributed Systems and Technologies, one (1), 1-16, January-March 2010 1
- Erl T., "Service-Oriented Architecture - A Field Guide to Integrating XMLand Web Services". Printice Hal., 2004.
- A.M. LAW & W.D. KELTON, Simulation Modelling and Analysis. Edition Mac Graw Hill Book Company, 1999
- FROM PLM TO ERP : A SOFTWARE SYSTEMS ENGINEERING INTEGRATION Nafisa Osman1 and Abd-El-Kader Sahraoui AlmedtechInc..and University SUST Khartoum, Sudan 2LAAS-CNRS, Université de Toulouse, UT2J, Toulouse, France International Journal of Software Engineering & Applications (IJSEA), Vol.9, No.1, January 2018
- DISTRIBUTED DBMS - DESIGN STRATEGIES HTTPS://WWW.TUTORIALSPOINT.COM/DISTRIBUTED_DBMS/DISTRIBUTED_DBMS_DESIGN_STRATEGIES.HTM
- DATA WAREHOUSE AND BIG DATA INTEGRATION Sonia Ordoñez Salinas and Alba Consuelo Nieto Lemus Faculty of Engineering, Distrial F.J.C University, Bogotá, Colombia, International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 2, April 2017
- A Survey on Cybersecurity in Unmanned Aerial Vehicles: Cyberattacks, Defense Techniques and Future Research Directions
Abstract Views :81 |
PDF Views:2
Authors
Affiliations
1 Mathematics and Computer Science Department, Cheikh Anta Diop University, Dakar, SN
1 Mathematics and Computer Science Department, Cheikh Anta Diop University, Dakar, SN
Source
International Journal of Computer Networks and Applications, Vol 10, No 5 (2023), Pagination: 688-705Abstract
Today, Unmanned Aerial Vehicles (UAV), also known as drones, are increasingly used by organizations, businesses and governments in a variety of military and civilian applications, including reconnaissance, border surveillance, port security, transportation, public safety surveillance, agriculture, scientific research, rescue and more. However, drone cybersecurity has become a major concern due to the growing risk of cyberattacks aimed at compromising the confidentiality, integrity and availability of drone systems. These cyberattacks can have serious consequences, such as disclosure or theft of sensitive data, loss of drones, disruption of drone performance, etc. In the existing literature, little work has been devoted to the cybersecurity of UAV systems. To fill this gap, a taxonomy of cyberattacks in UAV is proposed focusing on the three main categories, namely interception attacks against confidentiality, modification or fabrication attacks against integrity and disruption attacks against data availability. Next, a survey of defense techniques that can be used to protect UAV systems is carried out. Finally, a discussion is held on technologies for improving drone cybersecurity, such as Blockchain and Machine Learning, as well as the challenges and future direction of research.Keywords
Cybersecurity, UAV, Taxonomy, Cyberattacks, Defense Techniques, Machine Learning, Blockchain.Full Text
References
- W. Hayat Adnan and M. Fadly Khamis, “Drone Use in Military and Civilian Application: Risk to National Security,” J. Media Inf. Warf., vol. 15, no. 1, pp. 60–70, 2022.
- A. Hamza, U. Akram, A. Samad, S. N. Khosa, R. Fatima, and M. F. Mushtaq, “Unmaned Aerial Vehicles Threats and Defence Solutions,” Proc. - 2020 23rd IEEE Int. Multi-Topic Conf. INMIC 2020, 2020, doi: 10.1109/INMIC50486.2020.9318207.
- E. G. JELER, “Military and civilian applications of UAV systems,” Strateg. XXI Int. Sci. Conf. Complex Dyn. Nat. Secur. Environ., vol. 1, no. November 2017, pp. 379–386, 2019.
- A. Utsav, A. Abhishek, P. Suraj, and R. K. Badhai, “An IoT Based UAV Network for Military Applications,” 2021 Int. Conf. Wirel. Commun. Signal Process. Networking, WiSPNET 2021, pp. 122–125, 2021, doi: 10.1109/WiSPNET51692.2021.9419470.
- M. Sivakumar and T. Y. J. Naga Malleswari, “A literature survey of unmanned aerial vehicle usage for civil applications,” J. Aerosp. Technol. Manag., vol. 13, pp. 1–23, 2021, doi: 10.1590/jatm.v13.1233.
- J. P. Yaacoub, H. Noura, O. Salman, and A. Chehab, “Security analysis of drones systems: Attacks, limitations, and recommendations,” Internet of Things (Netherlands), vol. 11, p. 100218, 2020, doi: 10.1016/j.iot.2020.100218.
- V. Chamola, P. Kotesh, A. Agarwal, Naren, N. Gupta, and M. Guizani, “A Comprehensive Review of Unmanned Aerial Vehicle Attacks and Neutralization Techniques,” Ad Hoc Networks, vol. 111, p. 102324, 2021, doi: 10.1016/j.adhoc.2020.102324.
- O. M. Alhawi, M. A. Mustafa, and L. C. Cordiro, “Finding Security Vulnerabilities in Unmanned Aerial Vehicles Using Software Verification,” Proc. - 2019 Int. Work. Secur. Internet Things, SIoT 2019, pp. 1–17, 2019, doi: 10.1109/SIOT48044.2019.9637109.
- C. Yinka-Banjo and O. Ajayi, “Sky-Farmers: Applications of Unmanned Aerial Vehicles (UAV) in Agriculture,” Auton. Veh., 2020, doi: 10.5772/intechopen.89488.
- Y. Yazid, I. Ez-Zazi, A. Guerrero-González, A. El Oualkadi, and M. Arioua, “Uav-enabled mobile edge-computing for iot based on ai: A comprehensive review,” Drones, vol. 5, no. 4, 2021, doi: 10.3390/drones5040148.
- M. Gašparović and D. Gajski, “Unmanned Aerial Photogrammetric Systems in the Service of Engineering Geodesy,” SIG 2016 - Int. Symp. Eng. Geod., no. May, pp. 561–572, 2016.
- A. Y. Javaid, W. Sun, V. K. Devabhaktuni, and M. Alam, “Cyber security threat analysis and modeling of an unmanned aerial vehicle system,” 2012 IEEE Int. Conf. Technol. Homel. Secur. HST 2012, no. November, pp. 585–590, 2012, doi: 10.1109/THS.2012.6459914.
- M. Riahi Manesh and N. Kaabouch, “Cyber-attacks on unmanned aerial system networks: Detection, countermeasure, and future research directions,” Comput. Secur., vol. 85, pp. 386–401, 2019, doi: 10.1016/j.cose.2019.05.003.
- H. Benkraouda, E. Barka, and K. Shuaib, “Cyber-Attacks on the Data Communication of Drones Monitoring Critical Infrastructure,” pp. 83-93, 2018, doi: 10.5121/csit.2018.81708.
- N. A. Khan, S. N. Brohi, and N. Jhanjhi, UAV’s Applications, Architecture, Security Issues and Attack Scenarios: A Survey, vol. 118. Springer Singapore, 2020. doi: 10.1007/978-981-15-3284-9_86.
- M. Cosar, “Cyber attacks on unmanned aerial vehicles and cyber security measures,” Eurasia Proc. Sci. Technol. Eng. Math., vol. 21, pp. 258–265, 2022, doi: 10.55549/epstem.1226251.
- P. Y. Kong, “A Survey of Cyberattack Countermeasures for Unmanned Aerial Vehicles,” IEEE Access, vol. 9, pp. 148244–148263, 2021, doi: 10.1109/ACCESS.2021.3124996.
- X. Tang, P. Ren, Y. Wang, and Z. Han, “Combating full-duplex active eavesdropper: A hierarchical game perspective,” IEEE Trans. Commun., vol. 65, no. 3, pp. 1379–1395, 2017, doi: 10.1109/TCOMM.2016.2645679.
- Y. Zeng and R. Zhang, “Active eavesdropping via spoofing relay attack,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2016-May, pp. 2159–2163, 2016, doi: 10.1109/ICASSP.2016.7472059.
- W. Wu, Y. Wang, J. Mo, and J. Liu, “Robust proactive eavesdropping in UAV-enabled wireless communication networking,” Eurasip J. Wirel. Commun. Netw., vol. 2019, no. 1, 2019, doi: 10.1186/s13638-019-1599-6.
- A. Shen, J. Luo, J. Ning, Y. Li, Z. Wang, and B. Duo, “Safeguarding UAV Networks against Active Eavesdropping: An Elevation Angle-Distance Trade-Off for Secrecy Enhancement,” Drones, vol. 7, no. 2, pp. 1–18, 2023, doi: 10.3390/drones7020109.
- Y. Ahmed and A. Abdullahi, “Mitigating of Malicious Insider Keylogger Threats,” Researchgate.Net, no. February 2013, 2016, [Online]. Available: https://www.researchgate.net/profile/Yahye_Abukar2/publication/301197154_Mitigating_of_Malicious_Insider_Keylogger_Threats/links/570b696e08aea660813881c4/Mitigating-of-Malicious-Insider-Keylogger-Threats.pdf
- C. Drive, “of SINGAPORE Security Analysis of Unmanned Aircraft Systems Manh-Dung Nguyen , Naipeng Dong and Abhik Roychoudhury,” no. January, 2017.
- M. Software, “Chapter 7,” pp. 183–184, 2021.
- J. H. Wang, P. S. Deng, Y. S. Fan, L. J. Jaw, and Y. C. Liu, “Virus detection using data mining techinques,” IEEE Annu. Int. Carnahan Conf. Secur. Technol. Proc., pp. 71–76, 2003, doi: 10.1109/ccst.2003.1297538.
- C. Gudla, M. Shohel Rana, and A. H. Sung, “Defense Techniques Against Cyber Attacks on Unmanned Aerial Vehicles Malware Detection View project E-Learning View project Defense Techniques Against Cyber Attacks on Unmanned Aerial Vehicles,” no. October, 2018, [Online]. Available: https://www.researchgate.net/publication/328135272
- R. Zhang, “Intrusion Detection System In A Fleet Of Drones,” 2022.
- O. Westerlund and R. Asif, “Drone Hacking with Raspberry-Pi 3 and WiFi Pineapple: Security and Privacy Threats for the Internet-of-Things,” 2019 1st Int. Conf. Unmanned Veh. Syst. UVS 2019, no. c, pp. 1–10, 2019, doi: 10.1109/UVS.2019.8658279.
- N. M. Rodday, R. O. De Schmidt, and A. Pras, “Exploring security vulnerabilities of unmanned aerial vehicles,” Proc. NOMS 2016 - 2016 IEEE/IFIP Netw. Oper. Manag. Symp., no. Noms, pp. 993–994, 2016, doi: 10.1109/NOMS.2016.7502939.
- P. Dhomane and R. Mathew, “Counter-measures to spoofing and jamming of drone signals,” SSRN Electron. J., pp. 1–10, 2021, doi: 10.2139/ssrn.3774955.
- S. M. Giray, “Anatomy of unmanned aerial vehicle hijacking with signal spoofing,” RAST 2013 - Proc. 6th Int. Conf. Recent Adv. Sp. Technol., pp. 795–800, 2013, doi: 10.1109/RAST.2013.6581320.
- E. Basan, O. Makarevich, M. Lapina, and M. Mecella, “Analysis of the Impact of a GPS Spoofing Attack on a UAV,” CEUR Workshop Proc., vol. 3094, pp. 6–16, 2022.
- S. Mahfoudhi, M. A. Khodja, and F. O. Mahroogi, “A second-order sliding mode controller tuning employing particle swarm optimization,” Int. J. Intell. Eng. Syst., vol. 13, no. 3, pp. 212–221, 2020, doi: 10.22266/IJIES2020.0630.20.
- M. O. Demir, G. K. Kurt, and A. E. Pusane, “On the Limitations of GPS Time-Spoofing Attacks,” 2020 43rd Int. Conf. Telecommun. Signal Process. TSP 2020, pp. 313–316, 2020, doi: 10.1109/TSP49548.2020.9163444.
- S. Behal and K. Kumar, “Characterization and comparison of DDoS attack tools and traffic generators - a review,” Int. J. Netw. Secur., vol. 19, no. 3, pp. 383–393, 2017, doi: 10.6633/IJNS.201703.19(3).07.
- G. Vasconcelos, G. Carrijo, R. Miani, J. Souza, and V. Guizilini, “The Impact of DoS Attacks on the AR.Drone 2.0,” Proc. - 13th Lat. Am. Robot. Symp. 4th Brazilian Symp. Robot. LARS/SBR 2016, pp. 127–132, 2016, doi: 10.1109/LARS-SBR.2016.28.
- J. Feng and J. Tornert, “Denial-of-service attacks Denial-of-service attacks against the Parrot ANAFI drone,” 2021, [Online]. Available: https://kth.diva-portal.org/smash/get/diva2:1601435/FULLTEXT01.pdf
- S. Sharma, “Cryptography : An Art of Writing a Secret Code,” Int. J. Comput. Sci. Technol., vol. 8491, no. 1, pp. 26–30, 2017.
- A. Liu, P. Ning, H. Dai, Y. Liu, and C. Wang, “Defending DSSS-based broadcast communication against insider jammers via delayed seed-disclosure,” Proc. - Annu. Comput. Secur. Appl. Conf. ACSAC, pp. 367–376, 2010, doi: 10.1145/1920261.1920315.
- Q. Wang, “Defending wireless communication against eavesdropping attacks using secret spreading codes and artificial interference,” Comput. Secur., vol. 103, pp. 1–14, 2021, doi: 10.1016/j.cose.2020.102175.
- W. Stallings, Cryptography and Network Security: Principles and Practice 7th Global Edition. 2017.
- T. M. Hoang, N. M. Nguyen, and T. Q. Duong, “Detection of Eavesdropping Attack in UAV-Aided Wireless Systems: Unsupervised Learning with One-Class SVM and K-Means Clustering,” IEEE Wirel. Commun. Lett., vol. 9, no. 2, pp. 139–142, 2020, doi: 10.1109/LWC.2019.2945022.
- R. S. Sreenivas and R. Anitha, “Detecting keyloggers based on traffic analysis with periodic behaviour,” Netw. Secur., vol. 2011, no. 7, pp. 14–19, 2011, doi: 10.1016/S1353-4858(11)70076-9.
- A. Bhardwaj and S. Goundar, “Keyloggers: silent cyber security weapons,” Netw. Secur., vol. 2020, no. 2, pp. 14–19, 2020, doi: 10.1016/S1353-4858(20)30021-0.
- S. Chahrvin, “Keyloggers - your security nightmare?,” Comput. Fraud Secur., vol. 2007, no. 7, pp. 10–11, 2007, doi: 10.1016/S1361-3723(07)70090-8.
- M. Ahsan, K. E. Nygard, R. Gomes, M. M. Chowdhury, N. Rifat, and J. F. Connolly, “Cybersecurity Threats and Their Mitigation Approaches Using Machine Learning—A Review,” J. Cybersecurity Priv., vol. 2, no. 3, pp. 527–555, 2022, doi: 10.3390/jcp2030027.
- Z. Yangchun, Y. Zhao, and J. Yang, “New Virus Infection Technology and Its Detection,” Proc. IEEE Int. Conf. Softw. Eng. Serv. Sci. ICSESS, vol. 2020-Octob, pp. 388–394, 2020, doi: 10.1109/ICSESS49938.2020.9237708.
- Institute of Electrical and Electronics Engineers, “2017_Haris-A.-Khan_Computer Virus and Protection Methods Using Lab Analysis,” pp. 882–886, 2017.
- H. Kasban, O. Zahran, S. M. Elaraby, M. El-Kordy, and F. E. Abd El-Samie, “A comparative study of Virus detection techniques,” Sens. Imaging, vol. 11, no. 3, pp. 89–112, 2010, doi: 10.1007/s11220-010-0054-x.
- A. Thengade, A. Khaire, D. Mitra, and A. Goyal, “Virus Detection Techniques and Their Limitations,” Int. J. Sci. Eng. Res., vol. 5, no. 10, pp. 1334–1337, 2014.
- S. Chakraborty, “A Comparison study of Computer Virus and Detection Techniques,” Res. J. Eng. Technol., vol. 8, no. 1, p. 49, 2017, doi: 10.5958/2321-581x.2017.00008.3.
- O. Asiru, M. Dlamini, and J. Blackledge, “Application of artificial intelligence for detecting derived viruses,” Eur. Conf. Inf. Warf. Secur. ECCWS, pp. 647–655, 2017.
- B. B. Madan, M. Banik, and D. Bein, “Securing unmanned autonomous systems from cyber threats,” J. Def. Model. Simul., vol. 16, no. 2, pp. 119–136, 2019, doi: 10.1177/1548512916628335.
- A. Ben-david, O. Berkman, Y. Matias, and S. Patel, “Contextual OTP : Mitigating Emerging Man-in-the-Middle Attacks,” Lncs 7341. Acns 2012, pp. 30–47, 2012.
- M. A. Siddiqi, C. Iwendi, K. Jaroslava, and N. Anumbe, “Analysis on security-related concerns of unmanned aerial vehicle: attacks, limitations, and recommendations,” Math. Biosci. Eng., vol. 19, no. 3, pp. 2641–2670, 2022, doi: 10.3934/MBE.2022121.
- T. E. Humphreys, “Statement on the Vulnerability of Civil Unmanned Aerial Vehicles and other Systems to Civil Gps Spoofing Submitted to the Subcommittee on Oversight, Investigations, and Management of the House Committee on Homeland Security,” pp. 1–16, 2012.
- L. Meng et al., “An Approach of Linear Regression-Based UAV GPS Spoofing Detection,” Wirel. Commun. Mob. Comput., vol. 2021, 2021, doi: 10.1155/2021/5517500.
- T. Talaei Khoei, S. Ismail, and N. Kaabouch, “Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs,” Sensors, vol. 22, no. 2, 2022, doi: 10.3390/s22020662.
- M. Varshosaz, A. Afary, B. Mojaradi, M. Saadatseresht, and E. G. Parmehr, “Spoofing detection of civilian UAVs using visual odometry,” ISPRS Int. J. Geo-Information, vol. 9, no. 1, 2019, doi: 10.3390/ijgi9010006.
- Y. Mekdad et al., “A survey on security and privacy issues of UAVs,” Comput. Networks, vol. 224, p. 109626, 2023, doi: 10.1016/j.comnet.2023.109626.
- R. Altawy and A. M. Youssef, “Security, privacy, and safety aspects of civilian drones: A survey,” ACM Trans. Cyber-Physical Syst., vol. 1, no. 2, 2017, doi: 10.1145/3001836.
- K. D. Vedat TÜMEN1*, “A Defense Mechanism Against DoS Attacks on Unmanned Aerial Vehicle Communication,” vol. 17, no. 2, pp. 233–239, 2022.
- S. Mujeeb, S. K. Chowdhary, A. Srivastava, R. Majumdar, and M. Kumar, “Unmanned Aerial Vehicle Attack Detection using Snort,” no. Icicis 2021, pp. 18–24, 2022, doi: 10.5220/0010789700003167.
- S. Ouiazzane, M. Addou, and F. Barramou, “A Multiagent and Machine Learning Based Denial of Service Intrusion Detection System for Drone Networks,” Adv. Sci. Technol. Innov., no. January, pp. 51–65, 2022, doi: 10.1007/978-3-030-80458-9_5.
- Z. Baig, N. Syed, and N. Mohammad, “Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning,” Futur. Internet, vol. 14, no. 7, pp. 1–19, 2022, doi: 10.3390/fi14070205.
- M. Krichen, W. Y. H. Adoni, A. Mihoub, M. Y. Alzahrani, and T. Nahhal, “Security Challenges for Drone Communications: Possible Threats, Attacks and Countermeasures,” Proc. - 2022 2nd Int. Conf. Smart Syst. Emerg. Technol. SMARTTECH 2022, no. May, pp. 184–189, 2022, doi: 10.1109/SMARTTECH54121.2022.00048.
- O. Simeone, “A Very Brief Introduction to Machine Learning with Applications to Communication Systems,” IEEE Trans. Cogn. Commun. Netw., vol. 4, no. 4, pp. 648–664, 2018, doi: 10.1109/TCCN.2018.2881442.
- S. K. Sahu, A. Mokhade, and N. D. Bokde, “An Overview of Machine Learning, Deep Learning, and Reinforcement Learning-Based Techniques in Quantitative Finance: Recent Progress and Challenges,” Appl. Sci., vol. 13, no. 3, 2023, doi: 10.3390/app13031956.
- R. Shrestha, A. Omidkar, S. A. Roudi, R. Abbas, and S. Kim, “Machine-learning-enabled intrusion detection system for cellular connected uav networks,” Electron., vol. 10, no. 13, pp. 1–28, 2021, doi: 10.3390/electronics10131549.
- A. Shrivastava and K. Sharma, “DDoS Detection for Amateur Internet of Flying Things using Machine Learnings,” SSRN Electron. J., no. Aece, pp. 124–133, 2022, doi: 10.2139/ssrn.4159111.
- A. Shafique, A. Mehmood, and M. Elhadef, “Detecting Signal Spoofing Attack in UAVs Using Machine Learning Models,” IEEE Access, vol. 9, pp. 93803–93815, 2021, doi: 10.1109/ACCESS.2021.3089847.
- S. Ouiazzane, F. Barramou, and M. Addou, “Towards a Multi-Agent based Network Intrusion Detection System for a Fleet of Drones,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 10, pp. 351–362, 2020, doi: 10.14569/IJACSA.2020.0111044.
- X. Wei, Y. Wang, and C. Sun, “PerDet: Machine-Learning-Based UAV GPS Spoofing Detection Using Perception Data,” Remote Sens., vol. 14, no. 19, pp. 1–20, 2022, doi: 10.3390/rs14194925.
- M. Roopak, G. Yun Tian, and J. Chambers, “Deep learning models for cyber security in IoT networks,” 2019 IEEE 9th Annu. Comput. Commun. Work. Conf. CCWC 2019, pp. 452–457, 2019, doi: 10.1109/CCWC.2019.8666588.
- V. Hassija et al., “Fast, Reliable, and Secure Drone Communication: A Comprehensive Survey,” IEEE Commun. Surv. Tutorials, vol. 23, no. 4, pp. 2802–2832, 2021, doi: 10.1109/COMST.2021.3097916.
- M. Dave, K. Chauhan, H. Sachdeva, S. Gupta, and A. Misra, “Privacy and Security Improvement in UAV Network using Blockchain,” Int. J. Commun. Networks Distrib. Syst., vol. 1, no. 1, p. 1, 2023, doi: 10.1504/ijcnds.2023.10048925.
- S. Javed et al., “An Efficient Authentication Scheme Using Blockchain as a Certificate Authority for the Internet of Drones,” Drones, vol. 6, no. 10, pp. 1–15, 2022, doi: 10.3390/drones6100264.
- B. Bera, A. K. Das, S. Garg, M. Jalil Piran, and M. S. Hossain, “Access Control Protocol for Battlefield Surveillance in Drone-Assisted IoT Environment,” IEEE Internet Things J., vol. 9, no. 4, pp. 2708–2721, 2022, doi: 10.1109/JIOT.2020.3049003.
- K. Gai, Y. Wu, L. Zhu, K. K. R. Choo, and B. Xiao, “BlockchainEnabled Trustworthy Group Communications in UAV Networks,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4118–4130, 2021, doi: 10.1109/TITS.2020.3015862.
- K. Hartmann and C. Steup, “The vulnerability of UAVs to cyber attacks - An approach to the risk assessment,” Int. Conf. Cyber Conflict, CYCON, 2013.
- E. Yağdereli, C. Gemci, and A. Z. Aktaş, “A study on cyber-security of autonomous and unmanned vehicles,” J. Def. Model. Simul., vol. 12, no. 4, pp. 369–381, 2015, doi: 10.1177/1548512915575803.
- C. G. Leela Krishna and R. Murphy, “A review on cybersecurity vulnerabilities for unmanned aerial vehicles,” Auvsi Xponential 2018, pp. 0–5, 2018.
- L. He, W. Li, C. Guo, and R. Niu, “Civilian unmanned aerial vehicle vulnerability to GPS spoofing attacks,” Proc. - 2014 7th Int. Symp. Comput. Intell. Des. Isc. 2014, vol. 2, pp. 212–215, 2015, doi: 10.1109/ISCID.2014.131.