We present HornDroid, a new tool for the static analysis of information flow properties in Android applications. The core idea underlying HornDroid is to use Horn clauses for soundly abstracting the semantics of Android applications and to express security properties as a set of proof obligations that are automatically discharged by an off-the-shelf SMT solver. This approach makes it possible to fine-tune the analysis in order to achieve a high degree of precision while still using off-the-shelf verification tools, thereby leveraging the recent advances in this field. As a matter of fact, HornDroid outperforms state-of-the-art Android static analysis tools on benchmarks proposed by the community. Moreover, HornDroid is the first static analysis tool for Android to come with a formal proof of soundness, which covers the core of the analysis technique: besides yielding correctness assurances, this proof allowed us to identify some critical corner-cases that affect the soundness guarantees provided by some of the previous static analysis tools for Android.

Session cookies constitute one of the main attack targets against client authentication on the Web. To counter these attacks, modern web browsers implement native cookie protection mechanisms based on the HttpOnly and Secure flags. While there is a general understanding about the effectiveness of these defenses, no formal result has so far been proved about the security guarantees they convey. With the present paper we provide the first such result, by presenting a mechanized proof of noninterference assessing the robustness of the HttpOnly and Secure cookie flags against both web and network attackers with the ability to perform arbitrary XSS code injection. We then develop CookiExt, a browser extension that provides client-side protection against session hijacking, based on appropriate flagging of session cookies and automatic redirection over HTTPS for HTTP requests carrying these cookies. Our solution improves over existing client-side defenses by combining protection against both web and network attacks, while at the same time being designed so as to minimise its effects on the user’s browsing experience. Finally, we report on the experiments we carried out to practically evaluate the effectiveness of our approach

Despite its success and popularity, machine learning is now recognized as vulnerable to evasion attacks, i.e., carefully crafted perturbations of test inputs designed to force prediction errors. In this paper we focus on evasion attacks against decision tree ensembles, which are among the most successful predictive models for dealing with non-perceptual problems. Even though they are powerful and interpretable, decision tree ensembles have received only limited attention by the security and machine learning communities so far, leading to a sub-optimal state of the art for adversarial learning techniques. We thus propose Treant, a novel decision tree learning algorithm that, on the basis of a formal threat model, minimizes an evasion-aware loss function at each step of the tree construction. Treant is based on two key technical ingredients: robust splitting and attack invariance, which jointly guarantee the soundness of the learning process. Experimental results on publicly available datasets show that Treant is able to generate decision tree ensembles that are at the same time accurate and nearly insensitive to evasion attacks, outperforming state-of-the-art adversarial learning techniques.

No abstract

No abstract