Malware applications (Apps) targeting mobile devices are widespread, and compromise the sensitive and private information stored on the devices. This is due to the asymmetry between informative permissions and irrelevant and redundant permissions for benign Apps. It also depends on the characteristics of the Android platform, such as adopting an open-source policy, supporting unofficial App stores, and the great tolerance for App verification; therefore the Android platform is destined to face such malicious intrusions. In this paper, we propose a permissions-based malware detection system (PerDRaML) that determines the App’s maliciousness based on the usage of suspicious permissions. The system uses a multi-level based methodology; we first extract and identify the significant features such as permissions, smali sizes, and permission rates from a manually collected dataset of 10,000 applications. Further, we employ various machine learning models to categorize the Apps into their malicious or benign categories. Through extensive experimentations, the proposed method successfully identifies the 5× most significant features to predict malicious Apps. The proposed method outperformed the existing techniques by achieving high accuracies of malware detection i.e., 89.7% with Support Vector Machine, 89.96% with Random Forest, 86.25% with Rotation Forest, and 89.52% with Naïve Bayes models. Moreover, the proposed method optimized up to ~77% of the feature set as compared to the recent approaches, while improving the evaluation metrics such as precision, sensitivity, accuracy, and F-measure. The experimental results show that the proposed system provides a high level of symmetry between irrelevant permissions and malware Apps. Further, the proposed system is promising and may provide a low-cost alternative for Android malware detection for malicious or repackaged Apps.

Steganography is the art and practice of communication using hidden messages. The least significant bits (LSB) based method is the well-known type of steganography in the spatial domain. Usually, achieving the larger embedding capacity in LSB-based methods requires a large number of LSB bits modification which indirectly reduces the visual quality of stego-image and increases the risk of steganalysis detection attacks. In this study, we propose a novel steganography method with data mapping strategy which can reduce the number of bits modification per pixel. In the proposed method, four secret data bits are mapped with the four most significant bits of a cover pixel. Furthermore, the only two LSBs of a pixel are modified to indicate the mapping strategy. Experimental results show that the proposed method is able to achieve 3.48% larger embedding capacity while enhancing the visual quality (i.e., peak signal to noise ratio (PSNR) 3.73 dB) and reducing the modification of 0.76 bits per pixel. Moreover, the proposed method provides security against basic Regular and Singular groups (RS) steganalysis and histogram steganalysis detection attacks.