Recently, the proliferation of smartphones, tablets, and smartwatches has raised security concerns from researchers. Android-based mobile devices are considered a dominant operating system. The open-source nature of this platform makes it a good target for malware attacks that result in both data exfiltration and property loss. To handle the security issues of mobile malware attacks, researchers proposed novel algorithms and detection approaches. However, there is no standard dataset used by researchers to make a fair evaluation. Most of the research datasets were collected from the Play Store or collected randomly from public datasets such as the DREBIN dataset. In this paper, a wrapper-based approach for Android malware detection has been proposed. The proposed wrapper consists of a newly modified binary Owl optimizer and a random forest classifier. The proposed approach was evaluated using standard data splits given by the DREBIN dataset in terms of accuracy, precision, recall, false-positive rate, and F1-score. The proposed approach reaches 98.84% and 86.34% for accuracy and F-score, respectively. Furthermore, it outperforms several related approaches from the literature in terms of accuracy, precision, and recall.
The problem of finding the shortest path between two nodes is a common problem that requires a solution in many applications like games, robotics, and real-life problems. Since its deals with a large number of possibilities. Therefore, parallel algorithms are suitable to solve this optimization problem that has attracted a lot of researchers from both industry and academia to find the optimal path in terms of runtime, speedup, efficiency, and cost compared to sequential algorithms. In mountain climbing, finding the shortest path from the start node under the mountain to reach the destination node is a fundamental operator, and there are some interesting issues to be studied in mountain climbing that cannot be found in a traditional two-dimensional space search. We present a parallel Ant Colony Optimization (ACO) to find the shortest path in the mountain climbing problem using Apache Spark. The proposed algorithm guarantees the security of the selected path by applying some constraints that take into account the secure slope angle for the path. A generated dataset with variable sizes is used to evaluate the proposed algorithm in terms of runtime, speedup, efficiency, and cost. The experimental results show that the parallel ACO algorithm significantly (p < 0.05) outperformed the best sequential ACO. On the other hand, the parallel ACO algorithm is compared with one of the most recent research from the literature for finding the best path for mountain climbing problems using the parallel A* algorithm with Apache Spark. The parallel ACO algorithm with Spark significantly outperformed the parallel A* algorithm.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.