Search Results - malicious JavaScripts*

Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection by Alex, Scaria, Dhiliphan Rajkumar, T.

“… The malicious script, like, JavaScript, is a major threat to computer networks in terms of network security…”
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Detection of Obfuscated Malicious JavaScript Code by 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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ZipAST: Enhancing malicious JavaScript detection with sequence compression by Chen, Zixian, Wang, Weiping, Qin, Yan, Zhang, Shigeng

“… With the advancements in deep learning technologies, deep learning networks have shown the ability to automatically learn strong feature representations from malicious JavaScript…”
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Detecting malicious JavaScript code based on semantic analysis by 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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JSContana: Malicious JavaScript detection using adaptable context analysis and key feature extraction by Huang, Yunhua, Li, Tao, Zhang, Lijia, Li, Beibei, Liu, Xiaojie

“… Although malicious JavaScript detection methods are becoming increasingly effective, the existing methods based on feature matching or static word embeddings are difficult to detect different…”
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JStrong: Malicious JavaScript detection based on code semantic representation and graph neural network by 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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Malicious JavaScript Detection Based on Bidirectional LSTM Model by Song, Xuyan, Chen, Chen, Cui, Baojiang, Fu, Junsong

“… However, these dynamic natures also carry potential risks. The authors of the malicious scripts started using JavaScript to launch various attacks, such as Cross-Site Scripting (XSS…”
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JACLNet:Application of adaptive code length network in JavaScript malicious code detection by 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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Research on Malicious JavaScript Detection Technology Based on LSTM by Fang, Yong, Huang, Cheng, Liu, Liang, Xue, Min

“…The attacker injects malicious JavaScript into web pages to achieve the purpose of implanting Trojan horses, spreading viruses, phishing, and obtaining secret information…”
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A Survey on Current Malicious JavaScript Behavior of infected Web Content in Detection of Malicious Web pages by Nurulsafawati Wan Manan, Wan, Nizam Mohmad Kahar, Mohd, Mohd Ali, Noorlin

“… Recently, JavaScript has become the most common attack construction language as it is the primary browser scripting language which allow developer to develop sophisticated client-side interfaces for web application…”
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Detection of malicious javascript on an imbalanced dataset by Phung, Ngoc Minh, Mimura, Mamoru

“…In order to be able to detect new malicious JavaScript with low cost, methods with machine learning techniques have been proposed and gave positive results…”
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Spider bird swarm algorithm with deep belief network for malicious JavaScript detection by 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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JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation by Ren, Kunlun, Qiang, Weizhong, Wu, Yueming, Zhou, Yi, Zou, Deqing, Jin, Hai

“… As the main programming language for Web applications, many methods have been proposed for detecting malicious JavaScript, among which static analysis-based methods play an important role…”
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Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation by 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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Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement by 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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PyRHOH: A meta-learning analysis framework for determining the impact of compilation on malicious JavaScript identification by 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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TransAST: A Machine Translation-Based Approach for Obfuscated Malicious JavaScript Detection by 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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ScriptNet: Neural Static Analysis for Malicious JavaScript Detection by Stokes, Jack W., Agrawal, Rakshit, McDonald, Geoff, Hausknecht, Matthew

“… For internet-scale processing, static analysis offers substantial computing efficiencies. We propose the ScriptNet system for neural malicious JavaScript detection which is based on static analysis…”
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Malicious JavaScript Code Detection Based on Hybrid Analysis by 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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Detecting Malicious Javascript in PDF through Document Instrumentation by Daiping Liu, Haining Wang, Stavrou, Angelos

“… In this paper, we propose a context-aware approach for detection and confinement of malicious Javascript in PDF…”
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