Biblio

The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.

Many popular web applications incorporate end-toend secure messaging protocols, which seek to ensure that messages sent between users are kept confidential and authenticated, even if the web application's servers are broken into or otherwise compelled into releasing all their data. Protocols that promise such strong security guarantees should be held up to rigorous analysis, since protocol flaws and implementations bugs can easily lead to real-world attacks. We propose a novel methodology that allows protocol designers, implementers, and security analysts to collaboratively verify a protocol using automated tools. The protocol is implemented in ProScript, a new domain-specific language that is designed for writing cryptographic protocol code that can both be executed within JavaScript programs and automatically translated to a readable model in the applied pi calculus. This model can then be analyzed symbolically using ProVerif to find attacks in a variety of threat models. The model can also be used as the basis of a computational proof using CryptoVerif, which reduces the security of the protocol to standard cryptographic assumptions. If ProVerif finds an attack, or if the CryptoVerif proof reveals a weakness, the protocol designer modifies the ProScript protocol code and regenerates the model to enable a new analysis. We demonstrate our methodology by implementing and analyzing a variant of the popular Signal Protocol with only minor differences. We use ProVerif and CryptoVerif to find new and previously-known weaknesses in the protocol and suggest practical countermeasures. Our ProScript protocol code is incorporated within the current release of Cryptocat, a desktop secure messenger application written in JavaScript. Our results indicate that, with disciplined programming and some verification expertise, the systematic analysis of complex cryptographic web applications is now becoming practical.