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

A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization

Existing Android malware detection approaches use a variety of features such as security sensitive APIs, system calls, control-flow structures and information flows in conjunction with Machine Learning classifiers to achieve accurate detection. Each of these feature sets provides a unique semantic perspective (or view) of apps' behaviours with inherent strengths and limitations. Meaning, some views are more amenable to detect certain attacks but may not be suitable to characterise several other attacks. Most of the existing malware detection approaches use only one (or a selected few) of the aforementioned feature sets which prevent them from detecting a vast majority of attacks. Addressing this limitation, we propose MKLDroid, a unified framework that systematically integrates multiple views of apps for performing comprehensive malware detection and malicious code localisation. The rationale is that, while a malware app can disguise itself in some views, disguising in every view while maintaining malicious intent will be much harder. MKLDroid uses a graph kernel to capture structural and contextual information from apps' dependency graphs and identify malice code patterns in each view. Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted combination of the views which yields the best detection accuracy. Besides multi-view learning, MKLDroid's unique and salient trait is its ability to locate fine-grained malice code portions in dependency graphs (e.g., methods/classes). Through our large-scale experiments on several datasets (incl. wild apps), we demonstrate that MKLDroid outperforms three state-of-the-art techniques consistently, in terms of accuracy while maintaining comparable efficiency. In our malicious code localisation experiments on a dataset of repackaged malware, MKLDroid was able to identify all the malice classes with 94% average recall

Permission based Mobile Malware Detection System using Machine Learning Techniques

Mobile technology has grown dramatically around the world. Nowadays smart mobile devices are ubiquitous, i.e. they serve multiple purposes such as personal mobile communication, data storage, multimedia and entertainment etc. They have become important part of life. Implementing secure mobile and wireless networks is crucial for enterprises operating in the Internet-based business environment. Mobile market share has grown significantly in past few years so that we need to think about mobile security. Mobile security can be compromised due to design flaws, vulnerabilities, and protocol failures in any mobile applications, viruses, spyware, malware and other threats. In this paper we will more focus on mobile malware. Many tools are available in the market to detect malware but new research trend in the mobile security is users should be aware of app before he/she installs from the app store. Hence we propose a novel approach for permission based mobile malware detection system. It is based on static analysis. It has 3 major parts in it 1) a signature database for storing analysis results of training and testing. 2) An Android client who is used by end users for making analysis requests, and 3) a central server plays important role as it communicates with both signature database and smartphone client. We can say that he is the manager of whole analysis process. It alerts user if the app is malicious or the benign based on it user can proceed whether to continue with it or not

The Paradox of Choice: Investigating Selection Strategies for Android Malware Datasets Using a Machine-learning Approach

The increase in the number of mobile devices that use the Android operating system has attracted the attention of cybercriminals who want to disrupt or gain unauthorized access to them through malware infections. To prevent such malware, cybersecurity experts and researchers require datasets of malware samples that most available antivirus software programs cannot detect. However, researchers have infrequently discussed how to identify evolving Android malware characteristics from different sources. In this paper, we analyze a wide variety of Android malware datasets to determine more discriminative features such as permissions and intents. We then apply machine-learning techniques on collected samples of different datasets based on the acquired features’ similarity. We perform random sampling on each cluster of collected datasets to check the antivirus software’s capability to detect the sample. We also discuss some common pitfalls in selecting datasets. Our findings benefit firms by acting as an exhaustive source of information about leading Android malware datasets

Detecting Repackaged Android Applications Using Perceptual Hashing

The last decade has shown a steady rate of Android device dominance in market share and the emergence of hundreds of thousands of apps available to the public. Because of the ease of reverse engineering Android applications, repackaged malicious apps that clone existing code have become a severe problem in the marketplace. This research proposes a novel repackaged detection system based on perceptual hashes of vetted Android apps and their associated dynamic user interface (UI) behavior. Results show that an average hash approach produces 88% accuracy (indicating low false negative and false positive rates) in a sample set of 4878 Android apps, including 2151 repackaged apps. The approach is the first dynamic method proposed in the research community using image-based hashing techniques with reasonable performance to other known dynamic approaches and the possibility for practical implementation at scale for new applications entering the Android market

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page