With the popularity of the internet and smartphones, malware on smartphones has increased dramatically. In addition, the ubiquity and openness of the Android operating system have made it a lucrative platform for cybercriminals to develop malware. Traditional malware detection techniques require a lot of time and manual effort to classify malware accurately. Recently, deep learning (DL) based malware detection and classification techniques have been developed to solve this issue. This article proposes a DL-based two-stage framework that detects Android malware and classifies its variants using image-based malware representations of the Android DEX files. The framework uses the EfficientNetB0 convolutional neural network (CNN) to extracts relevant features from the malware color images. The extracted features are then passed through a global average pooling layer and fed into a stacking classifier. The stacking classifier employs linear support vector machine (SVM) and random forest (RF) algorithms as base-level classifiers and logistic regression as the meta-level classifier. This method obtained an accuracy of 100% in the binary classification of Android malware images and a 92.9% accuracy in All authors contributed equally to this article. 5-class (Adsware, Adware + Adware, Clicker + Trojan, Spyware, and Benign) classification, and an 88.6% accuracy in 4-class (Adsware, Adware + Adware, Clicker + Trojan, and Spyware) classification. We compared our method with 26 state-of-the-art pretrained CNN models (including the original Efficient-NetB0) and large-scale learning classifiers such as EfficientNetB0-SVM and EfficientNetB0-RF. The proposed framework outperformed the compared methods in all performance metrics. Experiments also demonstrate that substituting the softmax layer of CNNs with a large-scale learning classifier or stacking classifier results in an enhanced performance over the original network.
Organisations need information security to reduce the risk of unauthorized information disclosure, use, modification and destruction. To avoid this risk and ensure security diverse solutions are available such as Cryptography, Steganography and Watermarking. Encryption changes the form of information but latter two hide records or watermark in some medium. This paper is an effort to explore one of the solutions i.e. Steganography. It is a mechanism of hiding secret information in text, image, audio or video carriers. Broadly, these are classified in various categories such as Spatial domain, Transform domain and Distortion Technique. This work intends to give an overview of above mentioned techniques in detail by comparing algorithms based on performance metrics such as Bhattacharyya Coefficient, Correlation Coefficient, Intersection Coefficient, Jaccard Index, MAE, MSE, PSNR and UIQI. After analysing the MATLAB simulation and comparison based on different performance metrics, LSB Substitution and Pseudorandom technique are best suited for generating highly matched stego image with respect to their cover image.
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