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Energy-aware ACO-DNN optimization model for intrusion detection of unmanned aerial vehicle (UAVs)

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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Correspondence to Rajeev Tiwari.

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The authors declare that no conflict of interest and they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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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 (2022). https://doi.org/10.1007/s12652-022-04362-2

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  • DOI: https://doi.org/10.1007/s12652-022-04362-2

Keywords

  • Unmanned aerial vehicles
  • Network intrusion detection system (NIDS)
  • Dimension reduction
  • Deep neural network (DNN)
  • Ant colony optimization (ACO)
  • DVFS
  • NSLKDD dataset