The availability of high resolution images allows for using steganography to hide the presence of secret information. An issue of importance is to guarantee that the hidden data neither affects the quality of the images nor is it visually perceived. Another factor to consider is the capacity of embedding in the cover images without detection. In this paper we employ the Discrete Cosine Transform compression with quantization for lossy compression. We use the Salp Swarm Algorithm in conjunction with chaotic maps to find optimal locations in the cover images for data embedding in the LSBs. When chaotic maps are inserted into the SSA algorithm environment, the proposed method has achieved satisfactory results with minimal impact on the PSNR.

This work investigates the problems of extending the sensors network lifetime in smart cities. The limited capacity of the sensors’ batteries, and the difficulty of replacing the sensors’ batteries in hard-to-reach areas are some of the main challenges that contribute in reducing the lifetimes of the networks. The direction of this study is to use renewable energy as an energy source for collecting data from various infrastructures that are distributed throughout these cities. We present a model for data collection based on combining energy harvesting (EH) with the cluster head rotation feature, which results in flexible and sustainable networks that can be used in smart cities. Simulation results depict the performance of the proposed model with and without EH technology. The metrics used to compare the performance of the proposed model with and without EH technology include the consumed energy by sensors, number of live and dead sensors, and energy variance. The results show that the network lifetime increases when EH technology is used.

Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic analysis, and others are based on static analysis, while some are completely dependent on both. In this paper, we studied static, dynamic, and hybrid analyses to identify malicious applications. We leverage machine learning classifiers to detect malware activities as we explain the effectiveness of these classifiers in the classification process. Our results prove the efficiency of permissions and the action repetition feature set and their influential roles in detecting malware in Android applications. Our results show empirically very close accuracy results when using static, dynamic, and hybrid analyses. Thus, we use static analyses due to their lower cost compared to dynamic and hybrid analyses. In other words, we found the best results in terms of accuracy and cost (the trade-off) make us select static analysis over other techniques.

Image steganography has been widely adopted to protect confidential data. Researchers have been seeking to improve the steganographic techniques in order to increase the embedding capacity while preserving the stego-image quality. In this paper, we propose a steganography method using particle swarm optimization and chaos theory aiming at finding the best pixel locations in the cover image to hide the secret data while maintaining the quality of the resultant stego-image. To enhance the embedding capacity, the host and secret images are divided into blocks and each block stores an appropriate amount of secret bits. Experimental results show that the proposed scheme outperforms existing methods in terms of the PSNR and SSIM image quality metrics.

5G is considered as the key solution for the continuously increasing demands for future services, including low latency connection, high data rate, and the increasing number of devices that need to be connected to the network. Network slicing is one of the most important concepts to serve the 5G capabilities in a flexible way. Many different suggested approaches describe the mechanism of how 5G devices can select and connect to various slices. In this paper, we proposed a new mechanism that enables user equipment to have a session on different network servers at the same time in order to utilize the advantages of their services. Simulation results explain and validate aspects of the proposed solution.