Suchergebnisse - Malicious JavaScript code detection

JACLNet:Application of adaptive code length network in JavaScript malicious code detection von Zhang, Zhining, Wan, Liang, Chu, Kun, Li, Shusheng, Wei, Haodong, Tang, Lu

“… Currently, JavaScript malicious code detection methods are becoming more and more …”
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Detecting malicious JavaScript code based on semantic analysis von Fang, Yong, Huang, Cheng, Su, Yu, Qiu, Yaoyao

“… However, attackers use the dynamics feature of JavaScript language to embed malicious code into web pages for the purpose of drive-by-download, redirection, etc …”
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Detection of Obfuscated Malicious JavaScript Code von Alazab, Ammar, Khraisat, Ansam, Alazab, Moutaz, Singh, Sarabjot

“… Websites on the Internet are becoming increasingly vulnerable to malicious JavaScript code because of its strong impact and dramatic effect …”
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Malicious JavaScript Code Detection Based on Hybrid Analysis von He, Xincheng, Xu, Lei, Cha, Chunliu

“… However, since the heavy use of obfuscation techniques, many methods no longer apply to malicious JavaScript code detection, and it has been a huge challenge to de-obfuscate obfuscated malicious Java …”
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JStrong: Malicious JavaScript detection based on code semantic representation and graph neural network von Fang, Yong, Huang, Chaoyi, Zeng, Minchuan, Zhao, Zhiying, Huang, Cheng

“… However, the attacker uses the dynamic characteristics of the JavaScript language to embed malicious code into web pages to achieve the purpose of smuggling, redirection, and so on …”
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MOJI: Character-level convolutional neural networks for Malicious Obfuscated JavaScript Inspection von Ishida, Minato, Kaneko, Naoshi, Sumi, Kazuhiko

“… Many malicious JavaScript detection methods perform code abstraction and prior feature extraction to uncover the functionality hidden by obfuscation …”
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Detection Approach of Malicious JavaScript Code Based on deep learning von Zheng, Liyuan, Zhang, Dongcheng, Xie, Xin, Wang, Chen, Hou, Boyuan

“… Traditional machine learning methods for detecting JavaScript malicious code have the problems of complex feature extraction process, extensive computation, and difficult detection due to malicious …”
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Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation von Rozi, Muhammad Fakhrur, Ban, Tao, Ozawa, Seiichi, Yamada, Akira, Takahashi, Takeshi, Kim, Sangwook, Inoue, Daisuke

“… Malicious JavaScript code in web applications poses a significant threat as cyber attackers exploit it to perform various malicious activities …”
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Behavior Analysis Usage with Behavior Tures Adoption for Malicious Code Detection on JAVASCRIPT Scenarios Example von Y. M. Tumanov, S.V. Gavrilyuk

“… The article offers the method of malicious JavaScript code detection, based on behavior analysis …”
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AMA: Static Code Analysis of Web Page for the Detection of Malicious Scripts von Seshagiri, Prabhu, Vazhayil, Anu, Sriram, Padmamala

“… like Vigenere, Caesar and Atbash. The malicious iframes are injected to the websites using JavaScript and are also made hidden from the users perspective in-order to prevent detection …”
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Detecting malicious JavaScript code in Mozilla von Hallaraker, O., Vigna, G.

“… ). We propose an approach to solve this problem that is based on monitoring JavaScript code execution and comparing the execution to high-level policies, to detect malicious code behavior …”
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ZipAST: Enhancing malicious JavaScript detection with sequence compression von Chen, Zixian, Wang, Weiping, Qin, Yan, Zhang, Shigeng

“… Presently, mainstream detection methods usually extract the Abstract Syntax Tree (AST) from JavaScript code, which captures …”
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An Approach for Malicious JavaScript Detection Using Adaptive Taylor Harris Hawks Optimization-Based Deep Convolutional Neural Network von Alex, Scaria, T, Dhiliphan Rajkumar

“… This paper devises a novel technique for detecting malicious JavaScript. Here, JavaScript codes are fed to the feature extraction phase for extracting the noteworthy …”
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Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement von Ndichu, Samuel, Kim, Sangwook, Ozawa, Seiichi

“… Obfuscation is rampant in both benign and malicious JavaScript (JS) codes. It generates an obscure and undetectable code that hinders comprehension and analysis …”
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Spider bird swarm algorithm with deep belief network for malicious JavaScript detection von Alex, Scaria, Dhiliphan Rajkumar, T

“… However, the flexibility of JavaScript made these applications more prone to attacks that induce malicious behaviors in the code …”
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Research on Malicious JavaScript Detection Technology Based on LSTM von Fang, Yong, Huang, Cheng, Liu, Liang, Xue, Min

“… It can distinguish malicious JavaScript code and combat obfuscated code effectively. Experiments showed that the accuracy of detection model based on LSTM is 99.51 …”
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Deep Neural Networks for Malicious JavaScript Detection Using Bytecode Sequences von Rozi, Muhammad Fakhrur, Kim, Sangwook, Ozawa, Seiichi

“… To protect users from such cyberattacks, we propose a deep neural network for detecting malicious JavaScript codes by examining their bytecode sequences …”
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TransAST: A Machine Translation-Based Approach for Obfuscated Malicious JavaScript Detection von Qin, Yan, Wang, Weiping, Chen, Zixian, Song, Hong, Zhang, Shigeng

“… As an essential part of the website, JavaScript greatly enriches its functions. At the same time, JavaScript has become the most common attack payload on malicious website …”
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PyRHOH: A meta-learning analysis framework for determining the impact of compilation on malicious JavaScript identification von Fulkerson, Eli, Yocam, Eric, Vaidyan, Varghese, Kamepalli, Mahesh, Wang, Yong, Comert, Gurcan

“… Automated identification of malicious JavaScript is a core problem within modern malware analysis …”
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Statically Detecting JavaScript Obfuscation and Minification Techniques in the Wild von Moog, Marvin, Demmel, Markus, Backes, Michael, Fass, Aurore

“… While malware developers transform their JavaScript code to hide its malicious intent and impede detection, well-intentioned developers also transform their code to, e.g …”
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