Open Access
Subscription Access
Open Access
Subscription Access
Investigation of Deep Learning Optimizers for False Window Size Injection Attack Detection in Unmanned Aerial Vehicle Network Architecture
Subscribe/Renew Journal
The Unmanned Aerial Vehicle (UAV) network plays a prominent role in this pandemic era. Nowadays UAVs are applied in various applications like military, civil etc. This article works on the Search and Rescue application part. UAV networks are applied in search and rescue operations in order to find the missing people in Hill areas. Due to false data dissemination attacks some UAVs in the network will lost the data so the rescue will become an issue. In order to detect those attacks this work uses Feed Forward Neural network with back propagation algorithm. This work experiments chosen optimizers to get the accurate detection of attack and compares the results among the optimizers All the more explicitly this examination did in the Delay- Tolerant based Decentralized Multi-Layer UAV ad-hoc organization Assisting VANET (DDMUAV) design utilizing Opportunistic Network Environment (ONE) test system.
Keywords
Unmanned Aerial Vehicle, Delay Tolerant, Neural Network, Optimizer, Simulation.
Subscription
Login to verify subscription
User
Font Size
Information
- Alireza Abbaspour, Kang K. Yen, Shirin Noei and Arman Sargolzaei, “Detection of Fault Data Injection Attack on UAV Using Adaptive Neural Network”, Procedia Computer Science, Vol. 95, pp. 193-200, 2016.
- A. Alsarhan, A.R. Al-Ghuwairi and I.T. Almalkawi, “Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks”, Wireless Personal Communications, Vol. 117, pp. 3129-3152, 2021.
- F. Nordemann and R. Tonjes, “Transparent and Autonomous Store-Carry-Forward Communication in Delay Tolerant Networks (DTNs)”, Proceedings of International Conference on Computing, Networking and Communications, pp. 761-765, 2012.
- A. Shukla, G. Kalnoor and A. Kumar, “Improved Recognition Rate of Different Material Category using Convolutional Neural Networks”, Materials Today: Proceedings, pp. 1-7, 2021.
- H. Sedjelmaci, S. M. Senouci and N. Ansari, “A Hierarchical Detection and Response System to Enhance Security Against Lethal Cyber-Attacks in UAV Networks”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 48, No. 9, pp. 1594-1606, 2018.
- V. Chang, B. Gobinathan and S. Kannan, “Automatic Detection of Cyberbullying using Multi-Feature based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 1-18, 2021.
- Ilker Bekmezci, Ozgur Koray,Sahingoz and Samil Temel, “Flying Ad-Hoc Networks (FANETs): A Survey”, Ad-Hoc Networks, Vol. 1, pp. 1254-1270, 2013.
- T. Karthikeyan, K. Praghash and K.H. Reddy, “Binary Flower Pollination (BFP) Approach to Handle the Dynamic Networking Conditions to Deliver Uninterrupted Connectivity”, Wireless Personal Communications, Vol. 117, pp. 1-20, 2021.
- M.J. Kang and J.W. Kang, “Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security”, PLoS ONE, Vol. 11, No. 6, pp. 1-14, 2016.
- Alan Kim, B. Wampler and H. Aldridge, “Cyber Attack Vulnerabilities Analysis for Unmanned Aerial Vehicles”, Infotech Aerospace, pp. 1-12, 2012.
- Kuldeep Singh and Karandeep Singh, “A Survey and Analysis of Mobility Models in Mobile Adhoc Network”, International Journal of Advances in Electronics and Computer Science, Vol. 2, No. 1, pp. 29-33, 2015.
- S. Misra, B.K. Saha nd S. Pal, “A Developer’s Guide to the ONE Simulator. In: Opportunistic Mobile Networks”, Proceedings of International Conference on Computer Communications and Networks, pp. 53-88, 2016.
- S. Kitada, G. Hirakawa, G. Sato, N. Uchida and Y. Shibata, “DTN Based MANET for Disaster Information Transport by Smart Devices”, Proceedings of International Conference on Network-Based Information Systems, pp. 26-31, 2015.
- Sixiao Wei, Linqiang Ge, Wei Yu, Genshe Chen, Khanh Pham, Erik Blasch, Dan Shen and Chao Lu, “Simulation study of Unmanned Aerial Vehicle Communication Networks Addressing Bandwidth Disruptions”, Proceedings of International Conference on Sensors and Systems for Space Applications, pp. 1-8, 2014.
- Stephen George, “FAA Unmanned Aircraft Systems (UAS) Cyber Security Initiatives”, Federal Aviation Administration, pp. 1-19, 2015.
- Yi Zhou, Nan Cheng, Ning Lu, and Xuemin Shen, “Multi-UAV-Aided Networks: Aerial-Ground Cooperative Vehicular Networking Architecture”, IEEE Vehicular Technology Magazine, Vol. 10, No. 4, pp. 36-44, 2015.
- Yirui Wu, Dabao Wei and Jun Feng, “Network Attacks Detection Methods Based on Deep Learning Techniques: A Survey”, Security and Communication Networks, Vol. 2020, pp. 1-18, 2020.
- N. Vanitha and G. Padmavathi, “A Study on Various Cyber-Attacks and their Classification in UAV Assisted Vehicular Ad-Hoc Networks”, Proceedings of International Conference on Computational Intelligence, Cyber Security and Computational Models, pp. 1-13, 2018.
- N. Vanitha and P. Ganapathi, “Traffic Analysis of UAV Networks Using Enhanced Deep Feed Forward Neural Networks (EDFFNN)”, Proceedings of International Conference on Research on Machine and Deep Learning Applications for Cyber Security, pp. 219-244, 2020.
Abstract Views: 368
PDF Views: 1