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

Practical Fine-grained Privilege Separation in Multithreaded Applications

An inherent security limitation with the classic multithreaded programming model is that all the threads share the same address space and, therefore, are implicitly assumed to be mutually trusted. This assumption, however, does not take into consideration of many modern multithreaded applications that involve multiple principals which do not fully trust each other. It remains challenging to retrofit the classic multithreaded programming model so that the security and privilege separation in multi-principal applications can be resolved. This paper proposes ARBITER, a run-time system and a set of security primitives, aimed at fine-grained and data-centric privilege separation in multithreaded applications. While enforcing effective isolation among principals, ARBITER still allows flexible sharing and communication between threads so that the multithreaded programming paradigm can be preserved. To realize controlled sharing in a fine-grained manner, we created a novel abstraction named ARBITER Secure Memory Segment (ASMS) and corresponding OS support. Programmers express security policies by labeling data and principals via ARBITER's API following a unified model. We ported a widely-used, in-memory database application (memcached) to ARBITER system, changing only around 100 LOC. Experiments indicate that only an average runtime overhead of 5.6% is induced to this security enhanced version of application

Sound and Precise Malware Analysis for Android via Pushdown Reachability and Entry-Point Saturation

We present Anadroid, a static malware analysis framework for Android apps. Anadroid exploits two techniques to soundly raise precision: (1) it uses a pushdown system to precisely model dynamically dispatched interprocedural and exception-driven control-flow; (2) it uses Entry-Point Saturation (EPS) to soundly approximate all possible interleavings of asynchronous entry points in Android applications. (It also integrates static taint-flow analysis and least permissions analysis to expand the class of malicious behaviors which it can catch.) Anadroid provides rich user interface support for human analysts which must ultimately rule on the "maliciousness" of a behavior. To demonstrate the effectiveness of Anadroid's malware analysis, we had teams of analysts analyze a challenge suite of 52 Android applications released as part of the Auto- mated Program Analysis for Cybersecurity (APAC) DARPA program. The first team analyzed the apps using a ver- sion of Anadroid that uses traditional (finite-state-machine-based) control-flow-analysis found in existing malware analysis tools; the second team analyzed the apps using a version of Anadroid that uses our enhanced pushdown-based control-flow-analysis. We measured machine analysis time, human analyst time, and their accuracy in flagging malicious applications. With pushdown analysis, we found statistically significant (p < 0.05) decreases in time: from 85 minutes per app to 35 minutes per app in human plus machine analysis time; and statistically significant (p < 0.05) increases in accuracy with the pushdown-driven analyzer: from 71% correct identification to 95% correct identification.Comment: Appears in 3rd Annual ACM CCS workshop on Security and Privacy in SmartPhones and Mobile Devices (SPSM'13), Berlin, Germany, 201

Sub-graph based joint sparse graph for sparse code multiple access systems

Sparse code multiple access (SCMA) is a promising air interface candidate technique for next generation mobile networks, especially for massive machine type communications (mMTC). In this paper, we design a LDPC coded SCMA detector by combining the sparse graphs of LDPC and SCMA into one joint sparse graph (JSG). In our proposed scheme, SCMA sparse graph (SSG) defined by small size indicator matrix is utilized to construct the JSG, which is termed as sub-graph based joint sparse graph of SCMA (SG-JSG-SCMA). In this paper, we first study the binary-LDPC (B-LDPC) coded SGJSG- SCMA system. To combine the SCMA variable node (SVN) and LDPC variable node (LVN) into one joint variable node (JVN), a non-binary LDPC (NB-LDPC) coded SG-JSG-SCMA is also proposed. Furthermore, to reduce the complexity of NBLDPC coded SG-JSG-SCMA, a joint trellis representation (JTR) is introduced to represent the search space of NB-LDPC coded SG-JSG-SCMA. Based on JTR, a low complexity joint trellis based detection and decoding (JTDD) algorithm is proposed to reduce the computational complexity of NB-LDPC coded SGJSG- SCMA system. According to the simulation results, SG-JSGSCMA brings significant performance improvement compare to the conventional receiver using the disjoint approach, and it can also outperform a Turbo-structured receiver with comparable complexity. Moreover, the joint approach also has advantages in terms of processing latency compare to the Turbo approaches

Vulnerable GPU Memory Management: Towards Recovering Raw Data from GPU

In this paper, we present that security threats coming with existing GPU memory management strategy are overlooked, which opens a back door for adversaries to freely break the memory isolation: they enable adversaries without any privilege in a computer to recover the raw memory data left by previous processes directly. More importantly, such attacks can work on not only normal multi-user operating systems, but also cloud computing platforms. To demonstrate the seriousness of such attacks, we recovered original data directly from GPU memory residues left by exited commodity applications, including Google Chrome, Adobe Reader, GIMP, Matlab. The results show that, because of the vulnerable memory management strategy, commodity applications in our experiments are all affected

Identity in research infrastructure and scientific communication: Report from the 1st IRISC workshop, Helsinki Sep 12-13, 2011

Motivation for the IRISC workshop came from the observation that identity and digital identification are increasingly important factors in modern scientific research, especially with the now near-ubiquitous use of the Internet as a global medium for dissemination and debate of scientific knowledge and data, and as a platform for scientific collaborations and large-scale e-science activities.&#xd;&#xa;&#xd;&#xa;The 1 1/2 day IRISC2011 workshop sought to explore a series of interrelated topics under two main themes: i) unambiguously identifying authors/creators &#x26; attributing their scholarly works, and ii) individual identification and access management in the context of identity federations. Specific aims of the workshop included:&#xd;&#xa;&#xd;&#xa;&#x2022; Raising overall awareness of key technical and non-technical challenges, opportunities and developments.&#xd;&#xa;&#x2022; Facilitating a dialogue, cross-pollination of ideas, collaboration and coordination between diverse &#x2013; and largely unconnected &#x2013; communities.&#xd;&#xa;&#x2022; Identifying &#x26; discussing existing/emerging technologies, best practices and requirements for researcher identification.&#xd;&#xa;&#xd;&#xa;This report provides background information on key identification-related concepts &#x26; projects, describes workshop proceedings and summarizes key workshop findings

Security Code Smells in Android ICC

Android Inter-Component Communication (ICC) is complex, largely unconstrained, and hard for developers to understand. As a consequence, ICC is a common source of security vulnerability in Android apps. To promote secure programming practices, we have reviewed related research, and identified avoidable ICC vulnerabilities in Android-run devices and the security code smells that indicate their presence. We explain the vulnerabilities and their corresponding smells, and we discuss how they can be eliminated or mitigated during development. We present a lightweight static analysis tool on top of Android Lint that analyzes the code under development and provides just-in-time feedback within the IDE about the presence of such smells in the code. Moreover, with the help of this tool we study the prevalence of security code smells in more than 700 open-source apps, and manually inspect around 15% of the apps to assess the extent to which identifying such smells uncovers ICC security vulnerabilities.Comment: Accepted on 28 Nov 2018, Empirical Software Engineering Journal (EMSE), 201