Suchergebnisse - "Malicious JavaScript"

Detecting Malicious JavaScript Using Structure-Based Analysis of Graph Representation

Autoren: Muhammad Fakhrur Rozi Tao Ban Seiichi Ozawa et al.

Quelle: IEEE Access, Vol 11, Pp 102727-102745 (2023)

Schlagwörter: source code representation, cyber security, graph neural network, Abstract syntax tree, Electrical engineering. Electronics. Nuclear engineering, malicious JavaScript detection, TK1-9971

Zugangs-URL: https://doaj.org/article/96b921f1abd0426fa01fe22f24802029

Breaking Obfuscation: Cluster-Aware Graph with LLM-Aided Recovery for Malicious JavaScript Detection

Autoren: Liang, Zhihong Wang, Xin Hu, Zhenhuang et al.

Schlagwörter: Machine Learning, FOS: Computer and information sciences, Cryptography and Security, Cryptography and Security (cs.CR), Machine Learning (cs.LG)

Zugangs-URL: http://arxiv.org/abs/2507.22447

Pyrhoh: A Meta-Learning Analysis Framework for Determining the Impact of Compilation on Malicious Javascript Identification

Autoren: Eli Fulkerson Eric Yocam Varghese Vaidyan et al.

Malicious JavaScript Detection in Realistic Environments with SVM and MLP Models

Autoren: Ngoc Minh Phung Mamoru Mimura

Quelle: Journal of Information Processing. 32:748-756

JSMBox—A Runtime Monitoring Framework for Analyzing and Classifying Malicious JavaScript

Autoren: Phu H. Phung Allen Varghese Bojue Wang et al.

Quelle: Communications in Computer and Information Science ISBN: 9783031752001

JSHint: Revealing API Usage to Improve Detection of Malicious JavaScript

Autoren: Shaown Sarker Kasimir Schulz Aleksandr Nahapetyan et al.

Quelle: Lecture Notes in Computer Science ISBN: 9783031757631

Malicious Javascript Detection based on Clustering Techniques

Autoren: Nguyen Hong Son Dung, Ha Thanh

Quelle: International Journal of Network Security & Its Applications. 13:11-21

Schlagwörter: Malicious JavaScript, Detection model, Prediction support parameter, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology, K-means clustering algorithm

An Empirical Study on the Effects of Obfuscation on Static Machine Learning-Based Malicious JavaScript Detectors

Autoren: Kunlun Ren Weizhong Qiang Yueming Wu et al.

Quelle: Proceedings of the 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. :1420-1432

TransAST: A Machine Translation-Based Approach for Obfuscated Malicious JavaScript Detection

Autoren: Yan Qin Weiping Wang Zixian Chen et al.

Quelle: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :327-338

JSRevealer: A Robust Malicious JavaScript Detector against Obfuscation

Autoren: Kunlun Ren Weizhong Qiang Yueming Wu et al.

Quelle: 2023 53rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN). :339-351

Detection Approach of Malicious JavaScript Code Based on deep learning

Autoren: Liyuan Zheng Dongcheng Zhang Xin Xie et al.

Quelle: 2023 IEEE 3rd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA). :1075-1079

ZipAST: Enhancing malicious JavaScript detection with sequence compression

Autoren: Zixian Chen Weiping Wang Yan Qin et al.

Quelle: Computers & Security. 153:104390

Research on Evasion and Detection of Malicious JavaScript Code

Autoren: Yujie Ma Haokai Wu Yu-an Tan et al.

Quelle: Lecture Notes in Computer Science ISBN: 9789819724574

An Approach for Malicious JavaScript Detection Using Adaptive Taylor Harris Hawks Optimization-Based Deep Convolutional Neural Network

Autoren: Scaria Alex Dhiliphan Rajkumar T.

Quelle: International Journal of Distributed Systems and Technologies. 13:1-20

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

DeepSecure: Malicious JavaScript and NPM Package Scanner

Autoren: Lim, Jun Wei Fabian Chia, Wen Ming Micah Ong, Jun Leng et al.

Quelle: Proceedings of the 9th IRC Conference on Science, Engineering, and Technology ISBN: 9789819983681

Schlagwörter: System and network security, Software and application security

Taylor–HHO algorithm: A hybrid optimization algorithm with deep long short‐term for malicious JavaScript detection

Autoren: Scaria Alex T. Dhiliphan Rajkumar

Quelle: International Journal of Intelligent Systems. 36:7153-7176

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://onlinelibrary.wiley.com/doi/10.1002/int.22584

Malicious JavaScript Detection Based on AST Analysis and Key Feature Re-sampling in Realistic Environments

Autoren: Ngoc Minh Phung Mamoru Mimura

Quelle: Lecture Notes in Computer Science ISBN: 9783031330162

HideNoSeek: Camouflaging Malicious JavaScript in Benign ASTs

Autoren: Michael Backes Ben Stock Aurore Fass

Quelle: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. :1899-1913

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://dl.acm.org/doi/pdf/10.1145/3319535.3345656
https://dblp.uni-trier.de/db/conf/ccs/ccs2019.html#Fass0S19
https://publications.cispa.saarland/2936/

Adaptive Spider Bird Swarm Algorithm-Based Deep Recurrent Neural Network for Malicious JavaScript Detection Using Box-Cox Transformation

Autoren: Scaria Alex T. Dhiliphan Rajkumar

Quelle: International Journal of Open Source Software and Processes. 11:46-59

Zugangs-URL: https://www.igi-global.com/article/adaptive-spider-bird-swarm-algorithm-based-deep-recurrent-neural-network-for-malicious-javascript-detection-using-box-cox-transformation/270895
https://doi.org/10.4018/IJOSSP.2020100103
https://dblp.uni-trier.de/db/journals/ijossp/ijossp11.html#AlexR20

Deep Neural Networks for Malicious JavaScript Detection Using Bytecode Sequences

Autoren: Muhammad Fakhrur Rozi Seiichi Ozawa Sangwook Kim

Quelle: 2020 International Joint Conference on Neural Networks (IJCNN). :1-8

Schlagwörter: 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Zugangs-URL: https://ieeexplore.ieee.org/document/9207134
https://dblp.uni-trier.de/db/conf/ijcnn/ijcnn2020.html#RoziKO20