ABSTRACT This research delves into the intricacies of designing trajectories for unmanned aerial vehicles (UAVs) within a multi‐UAV system, specifically addressing the challenges presented during simultaneous rescue operations in neighboring states. The unique scenario introduces a potential risk of UAVs from one state intersecting with those from others, leading to communication issues and the looming threat of collisions. These collisions not only cause delays in emergency operations but also result in additional costs for repairing damaged UAV components. In response to this critical challenge, the study proposes an innovative approach utilizing Genetic Algorithms to facilitate collision avoidance in a multi‐UAV environment, tailored explicitly for disaster mitigation scenarios. This technique is an efficient solution to enhance the safety and effectiveness of UAV operations during disaster response and relief efforts. The proposed trajectory planning method uses a genetic algorithm, with the fitness function strategically designed to optimize two pivotal objectives: utility (maximizing the number of people saved postdisaster) and collision avoidance (minimizing conflicts between multiple UAVs as they navigate predetermined paths). The overarching goal of this approach is to strike a balance, aiming to maximize utility while concurrently minimizing the risk of collisions. By adopting this approach, the research significantly contributes to advancing the field of disaster response strategies, enhancing the overall efficiency of multi‐UAV systems in complex and dynamic environments. The proposed solution not only addresses the immediate challenges posed by potential collisions but also underscores the importance of optimizing UAV trajectories to achieve maximum utility in postdisaster scenarios.

Summary In the current landscape, the rapid expansion of the internet has brought about a corresponding surge in the number of data consumers. As user volume and diversity have escalated, the shift from conventional, uniform networks to Heterogeneous Networks (HetNets) has emerged. HetNets are designed with a primary objective: enhancing Quality of Service (QoS) standards for users. In the context of HetNets facilitated by Unmanned Aerial Vehicles (UAVs), a substantial influx of users and devices is observed. Within this multifaceted environment, the potential for malicious intruder nodes to efficiently execute and propagate harmful actions across the network is a distinct concern. Consequently, the entirety of network communication becomes susceptible to a multitude of security threats. To address these vulnerabilities and safeguard communication, the Functional Encryption (FE) technique is employed. FE empowers the protection of data against intrusion attacks. This paper presents a comprehensive methodology for implementing FE within UAV‐integrated HetNets, executed in two sequential phases. The initial phase secures communication between User Equipment (UE) and Micro Base Station (MBS), followed by the second phase, which focuses on securing communication among MBS and UAV. The viability of the proposed approach is substantiated through validation using the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The validation process involves the development of High‐Level Protocol Specification Language (HLPSL) codes. The successful security validation outcome underscores the capacity of the proposed methodology to provide the intended security measures and robustness to the network environment.

Summary Recently, unmanned aerial vehicles (UAV) technology has been utilized to monitor and capture images from disasters to analyze, process, and take action in real‐time for a speedy recovery. UAVs represent one of the critical technologies used beyond the fifth generation (B5G) of heterogeneous networks. Due to the sensitive data captured from disaster areas by UAVs, security has become a significant concern. Therefore, effective methods are needed to defend collected data against hackers and fictional activity by untrusted users. Audio encryption technologies can be applied to military communication, multimedia, medical, telemedical, and the internet. The present work introduces a novel Steganographic technique of converting an image to audio using LSB coding with XOR operation, ensuring the audio signal is protected using a key and maintains the excellent quality of the signal regenerated. Python3 is used for the implementation of the proposed technique. The findings show significant improvement in MSE and SSIM contexts.

Abstract Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter‐class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid‐Convolutional Support Machine (H‐CSM) based on ‘Support Vector Machine (SVM)’, ‘Convolutional Neural Network (CNN)’ and ‘Convolutional Block Attention Module (CBAM)’ is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method.

Summary Lightweight cryptography is a rapidly developing research field. Its main goal is to provide security for devices with fewer resources. These limited‐resource devices implement reliable ciphers that use very little power and computation. The lightweight cipher should be built for high performance while using the fewest resources possible, such as memory and power. In this article, we compare block ciphers and several other stream ciphers based on criteria such as input size, output size, structure employed, key size, number of rounds, vulnerable attacks, chip area, gate equivalent, memory use, throughput, and security features. Moreover, this article provides a detailed analysis comparing all cryptographic algorithms and their use in day‐to‐day life activities. This paper also discusses some lightweight ciphers, stream ciphers, and hybrid ciphers. Moreover, it shows the cryptanalysis of some block ciphers like DES.

Abstract Currently, Internet of Medical Things (IoMT) gained popularity because of an ongoing pandemic. A few developed countries plan to deploy the IoMT for improving the security and safety of frontline workers to decrease the mortality rates of COVID‐19 patients. However, IoMT devices share the information through an open network which leads to increased vulnerability to various attacks. Hence, electronic health management systems remain many security challenges, like recording sensitive patient data, secure communication, transferring patient information to other doctors, providing the data for future medical diagnosis, collecting data from WBAN, etc. In addition, the sensor devices attached to the human body are resource‐limited and have minimal power capacity. Hence, to protect the medical privacy of patients, confidentiality and reliability of the system, the register sensor, doctor and server need to authenticate each other. Therefore, rather than two factors, in this work, a multifactor authentication protocol has been proposed to provide more secure communication. The presented scheme uses biometric and fuzzy extractors for more security purposes. Furthermore, the scheme is proved using informal and formal security verification BAN logic, ProVerif and AVISPA tools. The ProVerif simulation result of the suggested scheme shows that the proposed protocol achieves session key secrecy and mutual authentication

Summary Recently, there has been a massive rise in the popularity of Instagram, which connects individuals globally and allows videos and images to be uploaded and exchanged, and communicated over social media. Instagram is also an online playground of deceit. The use of filters, lighting, and cunning angles transforms the mundane into something spectacular. Automated spam accounts and fake profiles use this to their malicious advantage for executing attacks targeting high‐profile executives. Creating fake Instagram identities is easy to reproduce the idea of being accepted by many fans on social media. Fake accounts are used in the marketing of fake services and products. This research focused on designing and training a unique neural network model and proposed a new algorithm for detecting automated spam and fake Instagram account profiles. The precision and accuracy of the proposed method were achieved at 93% and 91%, respectively.