Recently, cybersecurity experts and researchers have given special attention to developing cost-effective deep learning (DL)-based algorithms for Android malware detection (AMD) systems. However, the conventional AMD solutions necessitate extensive computations to achieve high accuracy in detecting Android malware apps. Consequently, there is a significant benefit in utilizing convolution neural networks (CNNs) in vision-based AMD applications to quickly and efficiently learn without prior stages of reverse engineering processes. Thus, this paper introduces an efficient and automated vision-based AMD model composed of 16 well-developed and fine-tuned CNN algorithms. This model precludes the need for a pre-designated features extraction process while generating accurate predictions of malware images with minimum cost and high detection speed. Such performance is achieved with colored or grayscale malware images, whether by using balanced or imbalanced datasets. Firstly, the bytecodes of the “classes.dex” files extracted from the Android benign and malware apps were converted to color and grayscale visual images before forwarding them to the developed CNN algorithms for classification. Then, the detection efficiency of the proposed AMD model was examined and evaluated using the imbalanced benchmark Leopard Android dataset that composes 14733 samples of malware apps and 2486 samples of benign apps. Finally, different experimental scenarios were conducted using balanced and imbalanced Android samples of color and grayscale images generated from the Leopard dataset; to extensively and sufficiently validate the detection and classification performance of the suggested model. Comprehensive assessment classification parameters in the evaluation experiments were applied to prove the high capability of the developed fine-tuned CNN algorithms in recognizing Android malware attacks with low computational overhead. As a result, the detection accuracy reached 99.40% for balanced samples and 98.05% for imbalanced samples. Furthermore, the proposed AMD model outperforms the existing approaches that utilize conventional vision-based algorithms and are tested on the same benchmark Android dataset.

There is a massive growth in malicious software (Malware) development, which causes substantial security threats to individuals and organizations. Cybersecurity researchers makes continuous efforts to defend against these malware risks. This research aims to exploit the significant advantages of Transfer Learning (TL) and Fine-Tuning (FT) methods to introduce efficient malware detection in the context of imbalanced families without the need to apply complex features extraction or data augmentation processes. Therefore, this paper proposes a visualized malware multi-classification framework to avoid false positives and imbalanced datasets’ challenges through using the fine-tuned convolutional neural network (CNN)-based TL models. The proposed framework comprises eight different FT CNN models including VGG16, AlexNet, DarkNet-53, DenseNet-201, Inception-V3, Places365-GoogleNet, ResNet-50, and MobileNet-V2. First, the binary files of different malware families were transformed into 2D images and then forwarded to the FT CNN models to detect and classify the malware families. The detection and classification performance was examined on a benchmark Malimg imbalanced dataset using different, comprehensive evaluation metrics. The evaluation results prove the FT CNN models’ significance in detecting malware types with high accuracy that reached 99.97% which also outperforms the performance of related machine learning (ML) and deep learning (DL)-based malware multi-classification approaches tested on the same malware dataset.

Android is one of the most essential and highly used operating systems. Android permissions system is a core security component that offers an access-control mechanism to protect system resources and users’ privacy. As such, it has experienced continuous change over each Android release. However, previous research on the permissions system has employed static analysis techniques. Furthermore, most of these studies are outdated, covering older versions of Android. This paper aims to discuss the permissions system intensively to provide a nutshell overview of the Android platform’s access-control mechanism. The paper presents a comprehensive analysis of the Android permissions system since it was introduced in 2008 until now, accompanied by a formal model of its components. The results of the analysis reveal a continuous growth in the number of permissions since the original release—a growth of seven times in some permission categories. A case study has been conducted for the last five years’ versions of the top Android apps to examine the permissions system’s evolution and its attendant security issues from the applications’ perspective. Some apps showed an increase in permissions usage of 73.33% by the 2020 release. Additionally, the results of the case study contribute to the understanding of permissions deployment by both vendors and developers. Finally, a discussion of the permission-based security enhancements discloses that the Android permissions system faces various security issues. In general, this paper provides researchers and academics an up-to-date, comprehensive, self-contained reference study of the Android permissions system.

The rapid growth of multimedia communication systems has expanded the High-Efficiency Video Coding (HEVC) security applications precipitously. Therefore, there is an urgent, elevated need to protect and secure the HEVC content during streaming and communication over insecure channels to ensure the privacy of HEVC data against intruders and attackers. This paper introduces an optical HEVC cipher algorithm based on bit-plane 3D-JST (Three-Dimensional Jigsaw Transform) and multistage 2D-FrFT (Two-Dimensional Fractional Fourier Transform) encryption. The main advantage of employing 3D-JST is its unitary transform that has an inverse transform used to reorganize the HEVC frame-blocks in an indiscriminately way. The proposed algorithm embraces the cascaded 2D-FrFT encryption in the optical domain using a single arbitrary phase code; to be executed all optically with a lone lens. The suggested algorithm utilizes the two 2D-FrFT stages with distinct kernels in mutually dimensions separated by employing the arbitrary phase code. A foregoing bit-plane permutation stage is conducted on the input HEVC frames before the 3D-JST and 2D-FrFT processes to accomplish a high robustness and security level. To validate the efficacy of the proposed cryptography algorithm for secure HEVC streaming, a comprehensive evaluation framework has been introduced and followed to (a) test HEVC streams against different statistical cryptographic metrics, (b) compare the proposed algorithm with recent related works whether optical-based or digital-based algorithms and (c) study the impact of different security attacks on its performance. The evaluation results show a secure and efficient proposed cryptography algorithm that outperforms the conventional and related cryptography algorithms in terms of all examined evaluation metrics.

Nowadays, ransomware is considered one of the most critical cyber-malware categories. In recent years various malware detection and classification approaches have been proposed to analyze and explore malicious software precisely. Malware originators implement innovative techniques to bypass existing security solutions. This paper introduces an efficient End-to-End Ransomware Detection System (E2E-RDS) that comprehensively utilizes existing Ransomware Detection (RD) approaches. E2E-RDS considers reverse engineering the ransomware code to parse its features and extract the important ones for prediction purposes, as in the case of static-based RD. Moreover, E2E-RDS can keep the ransomware in its executable format, convert it to an image, and then analyze it, as in the case of vision-based RD. In the static-based RD approach, the extracted features are forwarded to eight various ML models to test their detection efficiency. In the vision-based RD approach, the binary executable files of the benign and ransomware apps are converted into a 2D visual (color and gray) images. Then, these images are forwarded to 19 different Convolutional Neural Network (CNN) models while exploiting the substantial advantages of Fine-Tuning (FT) and Transfer Learning (TL) processes to differentiate ransomware apps from benign apps. The main benefit of the vision-based approach is that it can efficiently detect and identify ransomware with high accuracy without using data augmentation or complicated feature extraction processes. Extensive simulations and performance analyses using various evaluation metrics for the proposed E2E-RDS were investigated using a newly collected balanced dataset that composes 500 benign and 500 ransomware apps. The obtained outcomes demonstrate that the static-based RD approach using the AB (Ada Boost) model achieved high classification accuracy compared to other examined ML models, which reached 97%. While the vision-based RD approach achieved high classification accuracy, reaching 99.5% for the FT ResNet50 CNN model. It is declared that the vision-based RD approach is more cost-effective, powerful, and efficient in detecting ransomware than the static-based RD approach by avoiding feature engineering processes. Overall, E2E-RDS is a versatile solution for end-to-end ransomware detection that has proven its high efficiency from computational and accuracy perspectives, making it a promising solution for real-time ransomware detection in various systems.