Analysis of Bayesian classification-based approaches for Android malware detection

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware

Android Malware Characterization using Metadata and Machine Learning Techniques

Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets. While most previous work focuses on inherent characteristics of Android apps to detect malware, this study analyses indirect features and meta-data to identify patterns in malware applications. Our experiments show that: (1) the permissions used by an application offer only moderate performance results; (2) other features publicly available at Android Markets are more relevant in detecting malware, such as the application developer and certificate issuer, and (3) compact and efficient classifiers can be constructed for the early detection of malware applications prior to code inspection or sandboxing.Comment: 4 figures, 2 tables and 8 page

Selecting Root Exploit Features Using Flying Animal-Inspired Decision

Malware is an application that executes malicious activities to a computer system, including mobile devices. Root exploit brings more damages among all types of malware because it is able to run in stealthy mode. It compromises the nucleus of the operating system known as kernel to bypass the Android security mechanisms. Once it attacks and resides in the kernel, it is able to install other possible types of malware to the Android devices. In order to detect root exploit, it is important to investigate its features to assist machine learning to predict it accurately. This study proposes flying animal-inspired (1) bat, 2) firefly, and 3) bee) methods to search automatically the exclusive features, then utilizes these flying animal-inspired decision features to improve the machine learning prediction. Furthermore, a boosting method (Adaboost) boosts the multilayer perceptron (MLP) potential to a stronger classification. The evaluation jotted the best result is from bee search, which recorded 91.48 percent in accuracy, 82.2 percent in true positive rate, and 0.1 percent false positive rate

Machine-Learning Classifiers for Malware Detection Using Data Features

The spread of ransomware has risen exponentially over the past decade, causing huge financial damage to multiple organizations. Various anti-ransomware firms have suggested methods for preventing malware threats. The growing pace, scale and sophistication of malware provide the anti-malware industry with more challenges. Recent literature indicates that academics and anti-virus organizations have begun to use artificial learning as well as fundamental modeling techniques for the research and identification of malware. Orthodox signature-based anti-virus programs struggle to identify unfamiliar malware and track new forms of malware. In this study, a malware evaluation framework focused on machine learning was adopted that consists of several modules: dataset compiling in two separate classes (malicious and benign software), file disassembly, data processing, decision making, and updated malware identification. The data processing module uses grey images, functions for importing and Opcode n-gram to remove malware functionality. The decision making module detects malware and recognizes suspected malware. Different classifiers were considered in the research methodology for the detection and classification of malware. Its effectiveness was validated on the basis of the accuracy of the complete process