IIFA: Modular Inter-app Intent Information Flow Analysis of Android Applications

Android apps cooperate through message passing via intents. However, when apps do not have identical sets of privileges inter-app communication (IAC) can accidentally or maliciously be misused, e.g., to leak sensitive information contrary to users expectations. Recent research considered static program analysis to detect dangerous data leaks due to inter-component communication (ICC) or IAC, but suffers from shortcomings with respect to precision, soundness, and scalability. To solve these issues we propose a novel approach for static ICC/IAC analysis. We perform a fixed-point iteration of ICC/IAC summary information to precisely resolve intent communication with more than two apps involved. We integrate these results with information flows generated by a baseline (i.e. not considering intents) information flow analysis, and resolve if sensitive data is flowing (transitively) through components/apps in order to be ultimately leaked. Our main contribution is the first fully automatic sound and precise ICC/IAC information flow analysis that is scalable for realistic apps due to modularity, avoiding combinatorial explosion: Our approach determines communicating apps using short summaries rather than inlining intent calls, which often requires simultaneously analyzing all tuples of apps. We evaluated our tool IIFA in terms of scalability, precision, and recall. Using benchmarks we establish that precision and recall of our algorithm are considerably better than prominent state-of-the-art analyses for IAC. But foremost, applied to the 90 most popular applications from the Google Playstore, IIFA demonstrated its scalability to a large corpus of real-world apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or more apps/components

ACMiner: Extraction and Analysis of Authorization Checks in Android's Middleware

Billions of users rely on the security of the Android platform to protect phones, tablets, and many different types of consumer electronics. While Android's permission model is well studied, the enforcement of the protection policy has received relatively little attention. Much of this enforcement is spread across system services, taking the form of hard-coded checks within their implementations. In this paper, we propose Authorization Check Miner (ACMiner), a framework for evaluating the correctness of Android's access control enforcement through consistency analysis of authorization checks. ACMiner combines program and text analysis techniques to generate a rich set of authorization checks, mines the corresponding protection policy for each service entry point, and uses association rule mining at a service granularity to identify inconsistencies that may correspond to vulnerabilities. We used ACMiner to study the AOSP version of Android 7.1.1 to identify 28 vulnerabilities relating to missing authorization checks. In doing so, we demonstrate ACMiner's ability to help domain experts process thousands of authorization checks scattered across millions of lines of code

NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1

Advanced Security Analysis for Emergent Software Platforms

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya