A Hybrid Approach for Android Malware Detection and Family Classification

With the increase in the popularity of mobile devices, malicious applications targeting Android platform have greatly increased. Malware is coded so prudently that it has become very complicated to identify. The increase in the large amount of malware every day has made the manual approaches inadequate for detecting the malware. Nowadays, a new malware is characterized by sophisticated and complex obfuscation techniques. Thus, the static malware analysis alone is not enough for detecting it. However, dynamic malware analysis is appropriate to tackle evasion techniques but incapable to investigate all the execution paths and also it is very time consuming. So, for better detection and classification of Android malware, we propose a hybrid approach which integrates the features obtained after performing static and dynamic malware analysis. This approach tackles the problem of analyzing, detecting and classifying the Android malware in a more efficient manner. In this paper, we have used a robust set of features from static and dynamic malware analysis for creating two datasets i.e. binary and multiclass (family) classification datasets. These are made publically available on GitHub and Kaggle with the aim to help researchers and anti-malware tool creators for enhancing or developing new techniques and tools for detecting and classifying Android malware. Various machine learning algorithms are employed to detect and classify malware using the features extracted after performing static and dynamic malware analysis. The experimental outcomes indicate that hybrid approach enhances the accuracy of detection and classification of Android malware as compared to the case when static and dynamic features are considered alone

Detection of Improperly Worn Face Masks using Deep Learning – A Preventive Measure Against the Spread of COVID-19

Coronavirus disease 2019 has had a pressing impact on people all around the world. Ceasing the spread of this infectious disease is the urgent need of the hour. A vital method of protection against the virus is wearing masks in public areas. Not merely wearing masks but wearing masks properly can ensure that the respiratory droplets do not get transmitted to other people. In this paper, we have proposed a deep learning-based model, which can be used to detect people who are not wearing their face masks properly. A convolutional neural network model based on the concept of transfer learning is trained on a self-made dataset of images and implemented with light-weighted neural network called MobileNetV2 for mobile architectures. OpenCV is used with Caffe framework to detect faces in an input frame which are further forwarded to our trained convolutional neural network for classification. The method has been implemented on various input images and classification results have been obtained for the same. The experimental results show that the proposed model achieves a testing accuracy and training accuracy of 93.58% and 92.27% respectively. Optimal results with high confidence scores and correct classification have also been achieved when the proposed model was tested on individual input images

Comparative analysis of machine learning based methods for the prediction of NLR protein

In intestinal tissue repair and innate immunity, the nucleotide-binding domain leucine-rich repeat-containing (NLR) proteins play a fundamental role. The NLR protein family is a recent addition to the members of innate immunity effector molecules. It also plays an important role in intestinal microbiota, and recently emerged as a crucial hit for the development of colitis-associated cancer (CAC) and ulcerative colitis (UC). We have developed a Machine Learning based method for the prediction of NLR Proteins. This paper presents a comparative analysis of three supervised machine learning algorithms i.e. Sequential Minimal Optimization (SMO), Library for Support Vector Machine (LIBSVM) and Random Forest (RF) for prediction of NLR proteins. The dataset used for this work is created after extracting the features using ProtR package. The models are trained with the input compositional features generated using dipeptide composition, amino acid composition, etc. The dataset employed for training consists of 390 proteins. It has positive (103 sequences) set consisting of sequences from the NLR family and the remaining dataset (287 sequences) act as a negative training set, which has random protein sequences and several transporter family protein sequences retrieved from the NCBI and Uniprot.&nbsp