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.

The domain name system (DNS) plays a vital role in network services for name resolution. By default, this service is seldom blocked by security solutions. Thus, it has been exploited for security breaches using the DNS covert channel (tunnel). One of the greatest current data leakage techniques is DNS tunneling, which uses DNS packets to exfiltrate sensitive and confidential data. Data protection against stealthy exfiltration attacks is critical for human beings and organizations. As a result, many security techniques have been proposed to address exfiltration attacks starting with building security policies and ending with designing security solutions, such as firewalls, intrusion detection or prevention, and others. In this paper, a hybrid DNS tunneling detection system has been proposed based on the packet length and selected features for the network traffic. The proposed system takes advantage of the outcome results conducted using the testbed and Tabu-PIO feature selection algorithm. The evolution of the proposed system has already been completed using three distinct datasets. The experimental outcome results show that the proposed hybrid approach achieved 98.3% accuracy and a 97.6% F-score in the DNS tunneling datasets, which outperforms the other related works’ techniques using the same datasets. Moreover, when the packet length was added into the hybrid approach, the run-time shows better results than when Tabu-PIO was used when the size of the data increases.