![]() ![]() JavaScript code obfuscation has become a major technique used by malware writers to evade static analysis techniques. We remark that it is solid and ready for practical use. Our evaluation based on 200 large-scale websites showed that our birthmark system exhibits 100% accuracy. ![]() We propose a redesigned system with improved robustness and performed extensive experiments to justify the effectiveness and robustness of it. We extend two recent birthmark systems that extract the birthmark of a software from the run-time heap. A birthmark is a unique characteristic a program possesses that can be used to identify the program. ![]() In this paper, we use a relatively new technique, software birthmark, to help detect code theft of JavaScript programs. However, code obfuscation cannot avoid the source code being copied and a watermark can be defaced. There are techniques like code obfuscation and watermarking which can make the source code of a program difficult to understand by humans and prove the ownership of the program. However, the source code of JavaScript programs can be easily obtained and plagiarism of JavaScript programs is a serious threat to the industry. Therefore, malicious attackers often insert malicious JavaScript code into web pages to attack users.Īs JavaScript is becoming more and more popular, JavaScript programs are valuable assets to many companies. The main function of malicious JavaScript code is to discover vulnerabilities in Internet applications and use Cross Site Scripting (XSS) to attack user systems. ![]() This strong dependence creates an opportunity for malicious attackers to invade the victim’s system. In addition, there are many other applications that use JavaScript (e.g., Portable Document Format (PDF) tables, Hypertext Markup Language (HTML) emails, etc.), which plays an important role in these applications. The JavaScript language is mainly embedded on web pages and can be executed on the web, which is ubiquitous in Internet web pages and used in almost all websites. In general, an attacker who wants to implement an attack on a network user must embed the attack code into the network site or let users download an attack program. In recent years, the leakage of personal information on the Internet and the increasing assaults on social infrastructure have become the main threats to cyber security. Keywords: JavaScript code obfuscation detection Finally, the simulation results show that this method can not only reduce the requirements for the features, but also effectively improve the accuracy of the detection of JavaScript code obfuscation. Secondly, a CNN model is applied to the JavaScript code obfuscation detection, which overcomes the high requirement of the machine code learning and the low accuracy of the obfuscation feature extraction of JavaScript code. Firstly, the character matrix feature method of Bigram is used to extract features of JavaScript code. This paper proposes a method for detecting JavaScript code obfuscation based on Convolutional Neural Networks (CNNs). In order to further confront malicious webpage attacks, detecting JavaScript obfuscation scripts has become one of the most urgent issues to be addressed. Malicious webpage attacks occur frequently, and most of the JavaScript attack code is implemented through obfuscation. ![]()
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