Smartphone apps usually have access to sensitive user data such as contacts, geo-location, and account credentials and they might share such data to external entities through the Internet or with other apps. Confidentiality of user data could be breached if there are anomalies in the way sensitive data is handled by an app which is vulnerable or malicious. Existing approaches that detect anomalous sensitive data flows have limitations in terms of accuracy because the definition of anomalous flows may differ for different apps with different functionalities; it is normal for "Health" apps to share heart rate information through the Internet but is anomalous for "Travel" apps. In this paper, we propose a novel approach to detect anomalous sensitive data flows in Android apps, with improved accuracy. To achieve this objective, we first group trusted apps according to the topics inferred from their functional descriptions. We then learn sensitive information flows with respect to each group of trusted apps. For a given app under analysis, anomalies are identified by comparing sensitive information flows in the app against those flows learned from trusted apps grouped under the same topic. In the evaluation, information flow is learned from 11,796 trusted apps. We then checked for anomalies in 596 new (benign) apps and identified 2 previously-unknown vulnerable apps related to anomalous flows. We also analyzed 18 malware apps and found anomalies in 6 of them.
Chabada @cite_19 is a tool to find apps whose descriptions differ from their implementations. While we apply similar techniques in terms of natural language processing of apps descriptions, the goals differ. The goal of their approach is to find anomalous apps among the apps in the wild based on the inconsistencies between the advertised apps descriptions and their actual behaviors. By contrast, our approach specifically targets at identifying anomalies in the flow of sensitive information in the app code. More specifically, Chabada only identifies calls to sensitive APIs to characterize benign and anomalous apps. By contrast, we consider data flows from sensitive data sources to sensitive sinks.
@cite_19
2168649891 1982773740 1445387515 2768792654
How do we know a program does what it claims to do? After clustering Android apps by their description topics, we identify outliers in each cluster with respect to their API usage. A "weather" app that sends messages thus becomes an anomaly; likewise, a "messaging" app would typically not be expected to access the current location. Applied on a set of 22,500+ Android applications, our CHABADA prototype identified several anomalies; additionally, it flagged 56 of novel malware as such, without requiring any known malware patterns. Mobile malware attempts to evade detection during app analysis by mimicking security-sensitive behaviors of benign apps that provide similar functionality (e.g., sending SMS messages), and suppressing their payload to reduce the chance of being observed (e.g., executing only its payload at night). Since current approaches focus their analyses on the types of security-sensitive resources being accessed (e.g., network), these evasive techniques in malware make differentiating between malicious and benign app behaviors a difficult task during app analysis. We propose that the malicious and benign behaviors within apps can be differentiated based on the contexts that trigger security-sensitive behaviors, i.e., the events and conditions that cause the security-sensitive behaviors to occur. In this work, we introduce AppContext, an approach of static program analysis that extracts the contexts of security-sensitive behaviors to assist app analysis in differentiating between malicious and benign behaviors. We implement a prototype of AppContext and evaluate AppContext on 202 malicious apps from various malware datasets, and 633 benign apps from the Google Play Store. AppContext correctly identifies 192 malicious apps with 87.7 precision and 95 recall. Our evaluation results suggest that the maliciousness of a security-sensitive behavior is more closely related to the intention of the behavior (reflected via contexts) than the type of the security-sensitive resources that the behavior accesses. An app market's vetting process is expected to be scalable and effective. However, today's vetting mechanisms are slow and less capable of catching new threats. In our research, we found that a more powerful solution can be found by exploiting the way Android malware is constructed and disseminated, which is typically through repackaging legitimate apps with similar malicious components. As a result, such attack payloads often stand out from those of the same repackaging origin and also show up in the apps not supposed to relate to each other. Based upon this observation, we developed a new technique, called MassVet, for vetting apps at a massive scale, without knowing what malware looks like and how it behaves. Unlike existing detection mechanisms, which often utilize heavyweight program analysis techniques, our approach simply compares a submitted app with all those already on a market, focusing on the difference between those sharing a similar UI structure (indicating a possible repackaging relation), and the commonality among those seemingly unrelated. Once public libraries and other legitimate code reuse are removed, such diff common program components become highly suspicious. In our research, we built this "Diff-Com" analysis on top of an efficient similarity comparison algorithm, which maps the salient features of an app's UI structure or a method's control-flow graph to a value for a fast comparison. We implemented MassVet over a stream processing engine and evaluated it nearly 1.2 million apps from 33 app markets around the world, the scale of Google Play. Our study shows that the technique can vet an app within 10 seconds at a low false detection rate. Also, it outperformed all 54 scanners in VirusTotal (NOD32, Symantec, McAfee, etc.) in terms of detection coverage, capturing over a hundred thousand malicious apps, including over 20 likely zero-day malware and those installed millions of times. A close look at these apps brings to light intriguing new observations e.g., Google's detection strategy and malware authors' countermoves that cause the mysterious disappearance and reappearance of some Google Play apps. ChaCha is a family of stream ciphers that are very efficient on constrainted platforms. In this paper, we present electromagnetic side-channel analyses for two different software implementations of ChaCha20 on a 32-bit architecture: one compiled and another one directly written in assembly. On the device under test, practical experiments show that they have different levels of resistance to side-channel attacks. For the most leakage-resilient implementation, an analysis of the whole quarter round is required. To overcome this complication, we introduce an optimized attack based on a divide-and-conquer strategy named bricklayer attack.