Abstract
In modern militaries the Unmanned Aerial Vehicles (UAVs) are used for war fighting and also for certain civilian applications such as law enforcement, content for news outlets, situational awareness for emergency services and data collection for researchers. In UAVs, due to their ad-hoc nature, limited battery life, it needs better energy consumption techniques, that directly affect various parameters such as performance and reliability of device. Due to limited battery resource, the set-up time, flight time and speed features are needs to observe to enhance quality in terms of accessibility. There are issues regarding network security are now conspicuous with the progress of technology. This paper investigates the intrusion detection (ID) problem of high-dimensional and nonlinear data. In this study, the datasets KDD Cup 99 and NSL-KDD are used. The dataset is cleaned using the min–max normalization technique and it is processed using the 1-N encoding approach for achieving homogeneity. Dimensionality reduction is made using the Ant colony optimization (ACO) algorithm and further processing is done using the deep neural network (DNN). To minimize the energy consumption, the Dynamic Voltage and Frequency Scaling (DVFS) mechanisms are adopted. The main reason to set up this concept is to develop a balance between the energy consumption and the time of different modes of VMs and hosts. An effective solution is provided by the proposed model to handle the problem of the ID of UAV networks. The proposed model is validated and compared with ACO and Principal component analysis (PCA)-based (Naïve Bayes) NB models. The experimental outcomes prove the superiority of the ACO-DNN model over the existing state-of-the-art methods in performance, accuracy parameters, and time complexity.
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References
Albert S, Amarilla AA, Trollope B et al (2021) Assessing the potential of unmanned aerial vehicle spraying of aqueous ozone as an outdoor disinfectant for SARS-CoV-2. Environ Res. https://doi.org/10.1016/j.envres.2021.110944
Altawy R, Youssef AM (2016) Security, privacy, and safety aspects of civilian drones: a survey. ACM Transactions on Cyber-Physical Systems. https://doi.org/10.1145/3001836
Arthur MP (2019) Detecting signal spoofing and jamming attacks in UAV networks using a lightweight IDS. In: 2019 international conference on computer, information and telecommunication systems (CITS). IEEE. https://doi.org/10.1109/CITS.2019.8862148
Bangui H, Buhnova B (2021) Recent advances in machine-learning driven intrusion detection in transportation: survey. Procedia Computer Science. https://doi.org/10.1016/j.procs.2021.04.014
Bhati BS, Rai CS, Balamurugan B, Al-Turjman F (2020) An intrusion detection scheme based on the ensemble of discriminant classifiers. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2020.106742
Chithaluru P, Tiwari R, Kumar K (2021) Performance analysis of energy efficient opportunistic routing protocols in wireless sensor network. International Journal of Sensors Wireless Communications and Control 11(1):24–41
Choudhary G, Sharma V, You I, Yim K, Chen R, Cho JH (2018) Intrusion detection systems for networked unmanned aerial vehicles: a survey. In: 2018 14th international wireless communications & mobile computing conference (IWCMC). IEEE. https://doi.org/10.1109/IWCMC.2018.8450305
Condomines JP, Zhang R, Larrieu N (2019) Network intrusion detection system for UAV ad-hoc communication: from methodology design to real test validation. Ad Hoc Netw. https://doi.org/10.1016/j.adhoc.2018.09.004
Fotohi R, Nazemi E, Aliee FS (2020) An agent-based self-protective method to secure communication between UAVs in unmanned aerial vehicle networks. Vehicular Communications. https://doi.org/10.1016/j.vehcom.2020.100267
Hoang TM, Nguyen NM, Duong TQ (2019) Detection of eavesdropping attack in UAV-aided wireless systems: unsupervised learning with one-class SVM and k-means clustering. IEEE Wireless Communications Letters. https://doi.org/10.1109/LWC.2019.2945022
Hong T, Yang Q, Wang P, Zhang J, Sun W, Tao L, Cao J (2021) Multitarget real-time tracking algorithm for UAV IoT. Wirel Commun Mob Comput. https://doi.org/10.1155/2021/9999596
Jupri M, Sarno R (2020) Data mining, fuzzy AHP and TOPSIS for optimizing taxpayer supervision. Indonesian Journal of Electrical Engineering and Computer Science. https://doi.org/10.11591/ijeecs.v18.i1.pp75-87
Khan E, Garg D, Tiwari R, Upadhyay S (2018) Automated toll tax collection system using cloud database. In: 2018 3rd international conference on internet of things: smart innovation and usages (IoT-SIU). IEEE, pp 1–5
Lal G, Goel T, Tanwar V, Tiwari R (2016) Performance tuning approach for cloud environment. In: The international symposium on intelligent systems technologies and applications. Springer, Cham, pp 317–326
Lee J, Kim J, Kim I, Han K (2019) Cyber threat detection based on artificial neural networks using event profiles. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2953095
Min M, Xiao L, Xu D, Huang L, Peng M (2018) Learning-based defense against malicious unmanned aerial vehicles. In: 2018 IEEE 87th vehicular technology conference (VTC Spring). IEEE. https://doi.org/10.1109/VTCSpring.2018.8417685
Muniraj D, Farhood M (2017) A framework for detection of sensor attacks on small unmanned aircraft systems. In: 2017 international conference on unmanned aircraft systems (ICUAS).IEEE. https://doi.org/10.1109/ICUAS.2017.7991465
Ouiazzane S, BarramoU F, Addou M (2020) Towards a multi-agent based network intrusion detection system for a fleet of drones. Int J Adv Comput Sci Appl (IJACSA). https://doi.org/10.14569/IJACSA.2020.0111044
Rael K, Fragkos G, Plusquellic J, Tsiropoulou EE (2020) UAV-enabled Human Internet of Things. In: 2020 16th international conference on distributed computing in sensor systems (DCOSS). IEEE. https://doi.org/10.1109/DCOSS49796.2020.00056
Rose T, Kifayat K, Abbas S, Asim M (2020) A hybrid anomaly-based intrusion detection system to improve time complexity in the Internet of Energy environment. Journal of Parallel and Distributed Computing. https://doi.org/10.1016/j.jpdc.2020.06.012
Satheesh N, Rathnamma MV, Rajeshkumar G, Sagar PV et al (2020) Flow-based anomaly intrusion detection using machine learning model with software defined networking for OpenFlow network. Microprocess Microsyst. https://doi.org/10.1016/j.micpro.2020.103285
Sedjelmaci H, Senouci SM, Messous MA (2016a) How to detect cyber-attacks in unmanned aerial vehicles network?. In: 2016 IEEE global communications conference (GLOBECOM). IEEE. https://doi.org/10.1109/GLOCOM.2016.7841878
Sedjelmaci H, Senouci SM, Ansari N (2016b) Intrusion detection and ejection framework against lethal attacks in UAV-aided networks: a Bayesian game-theoretic methodology. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2016.2600370
Sedjelmaci H, Senouci SM, Ansari N (2017) A hierarchical detection and response system to enhance security against lethal cyber-attacks in UAV networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. https://doi.org/10.1109/TSMC.2017.2681698
Seyfollahi A, Ghaffari A (2021) A review of intrusion detection systems in RPL routing protocol based on machine learning for internet of things applications. Wirel Commun Mob Comput. https://doi.org/10.1155/2021/8414503
Seyfollahi A, Abeshloo H, Ghaffari A (2021) Enhancing mobile crowdsensing in Fog-based Internet of Things utilizing Harris hawks optimization. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-021-03344-0
Song J, Takakura H, Okabe Y, Nakao K (2013) Toward a more practical unsupervised anomaly detection system. Inf Sci. https://doi.org/10.1016/j.ins.2011.08.011
Tan X, Su S, Zuo Z, Guo X, Sun X (2019) Intrusion detection of UAVs based on the deep belief network optimized by PSO. Sensors. https://doi.org/10.3390/s19245529
Thakkar A, Lohiya R (2021) A review on machine learning and deep learning perspectives of IDS for IoT: recent updates, security issues, and challenges. Archives of Computational Methods in Engineering 28(4):3211–3243. https://doi.org/10.1007/s11831-020-09496-0
Xiao L, Xie C, Min M, Zhuang W (2017) User-centric view of unmanned aerial vehicle transmission against smart attacks. IEEE Trans Veh Technol. https://doi.org/10.1109/TVT.2017.2785414
Yao ACC, Zhao Y (2012) Online/offline signatures for low-power devices. IEEE Trans Inf Forensics Secur. https://doi.org/10.1109/TIFS.2012.2232653
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Samriya, J.K., Kumar, M. & Tiwari, R. Energy-aware ACO-DNN optimization model for intrusion detection of unmanned aerial vehicle (UAVs). J Ambient Intell Human Comput 14, 10947–10962 (2023). https://doi.org/10.1007/s12652-022-04362-2
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DOI: https://doi.org/10.1007/s12652-022-04362-2