This paper introduces a method to control JavaScript execution. The aim is to prevent or modify inappropriate behaviour caused by e.g. malicious injected scripts or poorly designed third-party code. The approach is based on modifying the code so as to make it self-protecting: the protection mechanism (security policy) is embedded into the code itself and intercepts security relevant API calls. The challenges come from the nature of the JavaScript language: any variables in the scope of the program can be redefined, and code can be created and run on-the-fly. This creates potential problems, respectively, for tamper-proofing the protection mechanism, and for ensuring that no security relevant events bypass the protection. Unlike previous approaches to instrument and monitor JavaScript to enforce or adjust behaviour, the solution we propose is lightweight in that (i) it does not require a modified browser, and (ii) it does not require any run-time parsing and transformation of code (including dynamically generated code). As a result, the method has low run-time overhead compared to other methods satisfying (i), and the lack of need for browser modifications means that the policy can even be applied on the server to mitigate some effects of cross-site scripting bugs.

Abstract. Phung et al (ASIACCS'09) describe a method for wrapping built-in methods of JavaScript programs in order to enforce security policies. The method is appealing because it requires neither deep transformation of the code nor browser modification. Unfortunately the implementation outlined suffers from a range of vulnerabilities, and policy construction is restrictive and error prone. In this paper we address these issues to provide a systematic way to avoid the identified vulnerabilities, and make it easier for the policy writer to construct declarative policies -i.e. policies upon which attacker code has no side effects.

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Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set.

Modern malware evolves various detection avoidance techniques to bypass the state‐of‐the‐art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning models to classify and detect malware. However, existing works in this field only perform simple image transformation methods. These simple transformations have not considered color encoding and pixel rendering techniques on the performance of machine learning classifiers. In this article, we propose a novel approach to encoding and arranging bytes from binary files into images. These developed images contain statistical (eg, entropy) and syntactic artifacts (eg, strings), and their pixels are filled up using space‐filling curves. Thanks to these features, our encoding method surpasses existing methods demonstrated by extensive experiments. In particular, our proposed method achieved 93.01% accuracy using the combination of the entropy encoding and character class scheme on the Hilbert curve.