Deepfakes are digital audio, video, or images manipulated using machine learning algorithms. These manipulated media files can convincingly depict individuals doing or saying things they never actually did. Deepfakes pose significant risks to our lives, including national security, financial markets, and personal privacy. The ability to create convincing deep fakes can also harm individuals’ reputations and can be used to spread disinformation and fake news. As such, there is a growing need for reliable and accurate methods to detect deep fakes and prevent their harmful effects. In this paper, a hybrid convolutional neural network (CNN) and recurrent neural network (RNN) with a particle swarm optimization (PSO) algorithm is utilized to demonstrate a deep learning strategy for detecting deepfake videos. High accuracy, sensitivity, specificity, and F1 score were attained by the proposed approach when tested on two publicly available datasets: Celeb-DF and the Deepfake Detection Challenge Dataset (DFDC). Specifically, the proposed method achieved an average accuracy of 97.26% on Celeb-DF and an average accuracy of 94.2% on DFDC. The results were compared to other state-of-the-art methods and showed that the proposed method outperformed many. The proposed method can effectively detect deepfake videos, which is essential for identifying and preventing the spread of manipulated content online.

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.

With the wide spread of devices that support mobile computing environment and applications, there is a need to develop new services that satisfy the needs of a mobile user. In this paper, we propose a new approach of selecting routes in a public transport networks using a combined criteria to provide a service that finds geographical routes from a source to a destination based on user public transportation and service preferences. Based on the proposed criteria we allow a path selection to depend on two main preferences. The first preference is the public transport media which could include bus, train, metro, and walking etc. The second preference is a service related one and includes the best network connection availability, such as GPRS, 3G, and 4G etc, along the available paths. We think that a combination of these two preferences is of high importance in our current daily life since a continuous availability of internet connection is crucial even while we are on the move using any public transport media. The proposed approach have been modelled and simulated via a C++ program. The simulation has been used to evaluate the proposed combined criteria in comparison with other approaches which are based just on the first preference, namely the public transport media, and with the simple shortest route selection approach. Results show that our combined criteria allow a mobile user to select geographical routes according to the preferred public transport media while being able to stay connected with higher transmission rate network connections.