SAMLDroid: A Static Taint Analysis and Machine Learning Combined High-Accuracy Method for Identifying Android Apps with Location Privacy Leakage Risks

Insecure applications (apps) are increasingly used to steal users’ location information for illegal purposes, which has aroused great concern in recent years. Although the existing methods, i.e., static and dynamic taint analysis, have shown great merit for identifying such apps, which mainly rely on statically analyzing source code or dynamically monitoring the location data flow, identification accuracy is still under research, since the analysis results contain a certain false positive or true negative rate. In order to improve the accuracy and reduce the misjudging rate in the process of vetting suspicious apps, this paper proposes SAMLDroid, a combined method of static code analysis and machine learning for identifying Android apps with location privacy leakage, which can effectively improve the identification rate compared with existing methods. SAMLDroid first uses static analysis to scrutinize source code to investigate apps with location acquiring intentions. Then it exploits a well-trained classifier and integrates an app’s multiple features to dynamically analyze the pattern and deliver the final verdict about the app’s property. Finally, it is proved by conducting experiments, that the accuracy rate of SAMLDroid is up to 98.4%, which is nearly 20% higher than Apparecium.

TaintBench: Automatic real-world malware benchmarking of Android taint analyses

Due to the lack of established real-world benchmark suites for static taint analyses of Android applications, evaluations of these analyses are often restricted and hard to compare. Even in evaluations that do use real-world apps, details about the ground truth in those apps are rarely documented, which makes it difficult to compare and reproduce the results. To push Android taint analysis research forward, this paper thus recommends criteria for constructing real-world benchmark suites for this specific domain, and presents TaintBench, the first real-world malware benchmark suite with documented taint flows. TaintBench benchmark apps include taint flows with complex structures, and addresses static challenges that are commonly agreed on by the community. Together with the TaintBench suite, we introduce the TaintBench framework, whose goal is to simplify real-world benchmarking of Android taint analyses. First, a usability test shows that the framework improves experts’ performance and perceived usability when documenting and inspecting taint flows. Second, experiments using TaintBench reveal new insights for the taint analysis tools Amandroid and FlowDroid: (i) They are less effective on real-world malware apps than on synthetic benchmark apps. (ii) Predefined lists of sources and sinks heavily impact the tools’ accuracy. (iii) Surprisingly, up-to-date versions of both tools are less accurate than their predecessors.

SESCon: Secure Ethereum Smart Contracts by Vulnerable Patterns’ Detection

Ethereum smart contracts have been gaining popularity toward the automation of so many domains, i.e., FinTech, IoT, and supply chain, which are based on blockchain technology. The most critical domain, e.g., FinTech, has been targeted by so many successful attacks due to its financial worth of billions of dollars. In all attacks, the vulnerability in the source code of smart contracts is being exploited and causes the steal of millions of dollars. To find the vulnerability in the source code of smart contracts written in Solidity language, a state-of-the-art work provides a lot of solutions based on dynamic or static analysis. However, these tools have shown a lot of false positives/negatives against the smart contracts having complex logic. Furthermore, the output of these tools is not reported in a standard way with their actual vulnerability names as per standards defined by the Ethereum community. To solve these problems, we have introduced a static analysis tool, SESCon (secure Ethereum smart contract), applying the taint analysis techniques with XPath queries. Our tool outperforms other analyzers and detected up to 90% of the known vulnerability patterns. SESCon also reports the detected vulnerabilities with their titles, descriptions, and remediations as per defined standards by the Ethereum community. SESCon will serve as a foundation for the standardization of vulnerability detection.

WTA: A Static Taint Analysis Framework for PHP Webshell

Webshells are a malicious scripts that can remotely control a webserver to execute arbitrary commands, steal sensitive files, and further invade the internal network. Existing webshell detection methods, such as using pattern matching for webshell detection, can be easily bypassed by attackers using the file include and user-defined functions. Furthermore, detecting unknown webshells has always been a problem in the field of webshell detection. In this paper, we propose a static webshell detection method based on taint analysis, which realizes accurate taint analysis based on ZendVM. We first converted the PHP code into Opline sequences, analyzed the Opline sequences in order, and marked the externally imported taint source. Then, the propagation of the taint variables was tracked, and the interprocedural analysis of the taint variables was performed. Finally, considering the dangerous functions’ call and the referencing of the taint variables at the point of the taint sink, we completed the webshell judgment. Based on this method, we constructed a taint analysis prototype system named WTA and evaluated it with a benchmark dataset by comparing its performance with popular webshell detection tools. The results showed that our method supports interprocedural analysis and has the ability to detect unknown webshells and that WTA’s performance surpasses well-known webshell detection tools such as D-shield, SHELLPUB, WebshellKiller, CloudWalker, ClamAV, LoKi, and findbot.pl.