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This paper presents a novel solution for detecting rare and mutating malware programs and provides a strategy to address the scarcity of datasets for modeling these types of malware. To provide sufficient training data for malware behavioral modeling, genetic algorithms are used together with an optimization strategy that selectively creates generations of mutated elite malware samples. In our unique method, a sequence of system API calls is extracted using tracker filter drivers in a sandbox environment. The most obfuscated and metamorphic malware are chosen by an elite selection method. The behavioral chromosomes are formed by mapping extracted APIs to genes using linear regression. Our analysis system includes an Internet simulator and a human emulator to deceive intelligent classes of malware to successfully execute themselves and prevent system halting. The evolution process is performed through crossover and permutation of genes, which are encoded based on the addresses of the kernel-level system functions. An objective function has been defined to optimize the vital indicators of malignancy and tracking rate with a linear time complexity. This guarantees that new generations of malware are more destructive and stealthy than their parents. J48 and deep neural networks were employed in our experiments as they are two popular modeling and classification strategies in the area of behavioral malware detection. Real-world malware samples from valid references were used for the performance evaluation of our approach. Comprehensive scenarios were involved in the experiments to evaluate the performance of our proposed strategy. The results demonstrate significant improvement in detection accuracy -up to 5% considering rare and metamorphic malware. The results also demonstrated a considerable enhancement in true positive rate for the proposed deep-learning algorithm.

Machine learning models based on sensitive data in the real-world promise advances in areas ranging from medical screening to disease outbreaks, agriculture, industry, defense science, and more. In many applications, learning participant communication rounds benefit from collecting their own private data sets, teaching detailed machine learning models on the real data, and sharing the benefits of using these models. Due to existing privacy and security concerns, most people avoid sensitive data sharing for training. Without each user demonstrating their local data to a central server, Federated Learning allows various parties to train a machine learning algorithm on their shared data jointly. This method of collective privacy learning results in the expense of important communication during training. Most large-scale machine learning applications require decentralized learning based on data sets generated on various devices and places. Such datasets represent an essential obstacle to decentralized learning, as their diverse contexts contribute to significant differences in the delivery of data across devices and locations. Researchers have proposed several ways to achieve data privacy in Federated Learning systems. However, there are still challenges with homogeneous local data. This research's approach is to select nodes (users) to share their data in Federated Learning for independent data-based equilibrium to improve accuracy, reduce training time, and increase convergence. Therefore, this research presents a combined Deep-Q-Reinforcement Learning Ensemble based on Spectral Clustering called DQRE-SCnet to choose a subset of devices in each communication round. Based on the results, it has been displayed that it is possible to decrease the number of communication rounds needed in Federated Learning. The realized reduction in the communication rounds are 51%, 25%, and 44% on the three datasets MNIST, Fashion MNIST, and CIFAR-10, respectively.

Today, unmanned aerial vehicles (UAVs), also known as drones, have become extremely popular in military applications, commercial applications, and academic research. Flying ad hoc network (FANET) is a new type of ad hoc network in which small drones are grouped as an ad hoc model. These networks have unique characteristics, including moving in a 3D space, high mobility, frequent topological changes, limited resources, low density of nodes, and so on, which impose various challenges when designing a proper and efficient routing scheme. In this paper, we present a fuzzy logic-based routing scheme for flying ad hoc networks. The proposed routing scheme has two phases: route discovery phase and route maintenance phase. In the first phase, we propose a technique for calculating the score of each node in the network to prevent the broadcast storm problem and control flooding the control messages, which have been broadcast to discover a new route in the network. This score is calculated based on various parameters such as movement direction, residual energy of nodes, link quality and node stability. Moreover, in the route selection process, we design a fuzzy system to select routes with more fitness, less delay, and fewer hops for data transfer. The second phase includes two steps: preventing route failure in order to detect and modify paths at the failure threshold, and reconstructing failed routes in order to recognize and quickly replace these routes. Finally, the proposed routing scheme is implemented in NS2 to evaluate its performance and determine its efficiency. The simulation results are compared with three routing methods, namely ECaD, LEPR, and AODV. These results show that the proposed routing method outperforms other routing schemes in terms of end to end delay, packet delivery rate, route stability, and energy consumption. However, it has slightly increased the routing overhead.INDEX TERMS Flying ad hoc network (FANET), Routing, Fuzzy logic, Unmanned aerial vehicle (UAV), Artificial intelligence (AI)

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