Resilient and Scalable Android Malware Fingerprinting and Detection

Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming. The diversity of targeted systems in terms of architecture and platforms compounds the challenges of Android malware detection and malware in general. This highlights the need to design and implement new scalable and robust methods, techniques, and tools to detect Android malware. In this thesis, we develop a malware fingerprinting framework to cover accurate Android malware detection and family attribution. In this context, we emphasize the following: (i) the scalability over a large malware corpus; (ii) the resiliency to common obfuscation techniques; (iii) the portability over different platforms and architectures. In the context of bulk and offline detection on the laboratory/vendor level: First, we propose an approximate fingerprinting technique for Android packaging that captures the underlying static structure of the Android apps. We also propose a malware clustering framework on top of this fingerprinting technique to perform unsupervised malware detection and grouping by building and partitioning a similarity network of malicious apps. Second, we propose an approximate fingerprinting technique for Android malware's behavior reports generated using dynamic analyses leveraging natural language processing techniques. Based on this fingerprinting technique, we propose a portable malware detection and family threat attribution framework employing supervised machine learning techniques. Third, we design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. We leverage graph analysis techniques to generate relevant, actionable, and granular intelligence that can be used to identify the threat effects induced by malicious Internet activity associated to Android malicious apps. In the context of the single app and online detection on the mobile device level, we further propose the following: Fourth, we design a portable and effective Android malware detection system that is suitable for deployment on mobile and resource constrained devices, using machine learning classification on raw method call sequences. Fifth, we elaborate a framework for Android malware detection that is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. We also evaluate the portability of the proposed techniques and methods beyond Android platform malware, as follows: Sixth, we leverage the previously elaborated techniques to build a framework for cross-platform ransomware fingerprinting relying on raw hybrid features in conjunction with advanced deep learning techniques

Mobile graphics: SIGGRAPH Asia 2017 course

Peer ReviewedPostprint (published version

ClouNS - A Cloud-native Application Reference Model for Enterprise Architects

The capability to operate cloud-native applications can generate enormous business growth and value. But enterprise architects should be aware that cloud-native applications are vulnerable to vendor lock-in. We investigated cloud-native application design principles, public cloud service providers, and industrial cloud standards. All results indicate that most cloud service categories seem to foster vendor lock-in situations which might be especially problematic for enterprise architectures. This might sound disillusioning at first. However, we present a reference model for cloud-native applications that relies only on a small subset of well standardized IaaS services. The reference model can be used for codifying cloud technologies. It can guide technology identification, classification, adoption, research and development processes for cloud-native application and for vendor lock-in aware enterprise architecture engineering methodologies

VComputeBench: A Vulkan Benchmark Suite for GPGPU on Mobile and Embedded GPUs

GPUs have become immensely important computational units on embedded and mobile devices. However, GPGPU developers are often not able to exploit the compute power offered by GPUs on these devices mainly due to the lack of support of traditional programming models such as CUDA and OpenCL. The recent introduction of the Vulkan API provides a new programming model that could be explored for GPGPU computing on these devices, as it supports compute and promises to be portable across different architectures. In this paper we propose VComputeBench, a set of benchmarks that help developers understand the differences in performance and portability of Vulkan. We also evaluate the suitability of Vulkan as an emerging cross-platform GPGPU framework by conducting a thorough analysis of its performance compared to CUDA and OpenCL on mobile as well as on desktop platforms. Our experiments show that Vulkan provides better platform support on mobile devices and can be regarded as a good crossplatform GPGPU framework. It offers comparable performance and with some low-level optimizations it can offer average speedups of 1.53x and 1.66x compared to CUDA and OpenCL respectively on desktop platforms and 1.59x average speedup compared to OpenCL on mobile platforms. However, while Vulkan’s low-level control can enhance performance, it requires a significantly higher programming effort.EC/H2020/688759/EU/Low-Power Parallel Computing on GPUs 2/LPGPU

PrivacyGuard: A VPN-Based Approach to Detect Privacy Leakages on Android Devices

The Internet is now the most important and efficient way to gain information, and mobile devices are the easiest way to access the Internet. Furthermore, wearable devices, which can be considered to be the next generation of mobile devices, are becoming popular. The more people rely on mobile devices, the more private information about these people can be gathered from their devices. If a device is lost or compromised, much private information is revealed. Although today’s smartphone operating systems are trying to provide a secure environment, they still fail to provide users with adequate control over and visibility into how third-party applications use their private data. The privacy leakage problem on mobile devices is still severe. For example, according a field study [1] done by CMU recently, Android applications track users’ location every three minutes in average. After the PRISM program, a surveillance program done by NSA, is exposed, people are becoming increasingly aware of the mobile privacy leakages. However, there are few tools available to average users for privacy preserving. Most tools developed by recent work have some problems (details can be found in chapter 2). To address these problems, we present PrivacyGuard, an efficient way to simultaneously detect leakage of multiple types of sensitive data, such as a phone’s IMEI number or location data. PrivacyGuard provides real-time protection. It is possible to modify the leaked information and replace it with crafted data to achieve protection. PrivacyGuard is configurable, extensible and useful for other research. We implement PrivacyGuard on the Android platform by taking advantage of the VPNService class provided by the Android SDK. PrivacyGuard does not require root per- missions to run on a device and does not require any knowledge about VPN technology from users either. The VPN server runs on the device locally. No external servers are required. According to our experiments, PrivacyGuard can effectively detect privacy leak- ages of most applications and advertisement libraries with almost no overhead on power consumption and reasonable overhead on network speed