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A crucial element in detecting unusual network system behavior is the network intrusion detection system (NIDS), which also helps to stop network attacks from happening. Despite the fact that a great deal of machine learning techniques has been used in intrusion detection, current solutions still struggle to provide accurate classification results. Furthermore, when dealing with imbalanced multi-category traffic data, a single classifier may not be able to produce a superior. Particularly, internet of things (IoT) gadgets is now a commonplace aspect of life. On the other hand, some problems are becoming worse and lack clear remedies. Convergence, communication speed, and security between various IoT devices are among the primary concerns. In order to achieve this goal, an enhanced artificial bee colony technique utilizing binary search equations and neural networks--known as the (BABCN) algorithm for intrusion detection in terms of convergence and communication speed--is presented in this study. The artificial bee is improved by the depthfirst search framework and binary search equations upon which the BABCN method is based. The suggested approach has a good ability to detect intrusions in the network and enhances categorization, according to the findings obtained by using the NSL-KDD dataset. [ABSTRACT FROM AUTHOR]

With the advancement in artificial intelligence (AI) technology and Internet of Things (IoT) growing popularity, the use of unmanned aerial vehicles (UAVs) as IoT devices is also being studied. However, privacy issues and limited communication resources restrict the use of unmanned aerial vehicles. Federated Learning (FL) has emerged as a promising approach for training machine learning models on decentralized devices while maintaining data privacy and reducing the communication costs. To aggregate the model on the server-side FL server uses various aggregation algorithms. One such promising aggregation algorithm is FedBA for non-identically and independently distributed (non-IID) data via UAVs. However, despite its advantages, FedBA may face challenges related to convergence speed, robustness to malicious clients, and overall efficiency. To solve these challenges, this study presents an FL model aggregation technique in which clients and servers communicate parameters as opposed to data, thereby enhancing privacy, and reducing communication costs UAVs images. This research proposes enhancements to the FedBA algorithm aimed at addressing these challenges and improving its performance. The method proposes federated learning based enhanced FedBA with MobileNet Convolutional Neural Network (CNN) for the identification of columnar cactus in the Tehuacán-Cuicatlán Valley of Mexico. Using a public dataset of over 20,000 remote sensing images, the suggested model is evaluated and found to be superior to InceptionV3 and modified MobileNet CNN. The key contributions of this work include the introduction of momentum for faster convergence, adaptive learning rates for better optimization and model aggregation clipping to prevent extreme updates. The proposed FL framework mitigates the issue of slow convergence and communication cost for non-IID data from UAVs. Enhanced FedBA more effective than typical FL algorithms. The classification accuracy before aggregation is 95% and improved to 97% after aggregation. [ABSTRACT FROM AUTHOR]

The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades. Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle (UAV) required maximum accuracy. In this paper, we designed a hybrid framework, which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures. The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient (DDPG) to receive the best reward and take actions according to 3D hand gestures input. The UAV consist of a Jetson Nano embedded test bed, Global Positioning System (GPS) sensor module, and Intel depth camera. The collision avoidance system based on the polar mask segmentation technique detects the obstacles and decides the best path according to the designed reward function. The analysis of the results has been observed providing best accuracy and computational time using novel design framework when compared with traditional Proportional Integral Derivatives (PID) flight controller. There are six reward functions estimated for 2500, 5000, 7500, and 10000 episodes of training, which have been normalized between 0 to -4000. The best observation has been captured on 2500 episodes where the rewards are calculated for maximum value. The achieved training accuracy of polar mask segmentation for collision avoidance is 86.36%. [ABSTRACT FROM AUTHOR]