In wireless sensor networks, the energy source is limited to the capacity of the sensor node’s battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms’ energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.

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.

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.

Recently, the data collection problem in wireless sensor networks (WSNs) using mobile sinks has received much attention. The main challenge in such problems is constructing the path that the mobile sink (MS) will use to collect the data. In this paper, an efficient path generation algorithm for the mobile sink based on principal component analysis (PCA) is proposed. The proposed approach was evaluated using two data collection modes—direct and multihop—and it was compared with another approach called the mobile-sink-based energy-efficient clustering algorithm for wireless sensor networks (MECA). When compared with MECA, simulation results have shown that the proposed approach improves the performance of WSN in terms of the number of live nodes and average remaining energy.

Radio Frequency Identification (RFID) is a modern approach to identify and track several assets at once in a supply chain environment. In many RFID applications, tagged items are frequently transferred from one owner to another. Thus, there is a need for secure ownership transfer protocols that can perform the transfer while, at the same time, protect the privacy of owners. Several protocols have been proposed in an attempt to fulfill this requirement. In this survey, we provide a comprehensive and systematic review of the RFID ownership transfer protocols that appeared over the years of 2005-2018. In addition, we compare these protocols based on the security goals which involve their support of ownership transfer properties and their resistance to attacks. From the presented comparison, we draw attention to the open issues in this field and provide suggestions for the direction that future research should follow. Furthermore, we suggest a set of guidelines to be considered in the design of new protocols. To the best of our knowledge, this is the first comprehensive survey that reviews the available ownership transfer protocols from the early-start up to the current state of the art.

The need to detect and classify malware on Android devices has become crucial due to the widespread use of these devices daily. This paper presents a method for detecting and classifying malware that uses static and dynamic features. Specifically, we extract permissions and intent filters from static analysis files and APK API data from dynamic analysis files. Then, we use Simhash to encode the selected parts of the analysis files to create feature vectors. These vectors are then used to train different Machine Learning algorithms for detecting and classifying malware. We conducted several experiments to evaluate the effectiveness of our approach on a dataset of 38,355 labeled Android apps and tested various hash functions and tokenizing methods in the Simhash algorithm. Our results showed that using Random Forest as a classifier, SHA-512 the hash function and 2-WORD tokenization led to high accuracy with reduced time and memory requirements for training, validating, and testing. This indicates a highly accurate model for detecting and classifying malware into 24 families.