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A deep learning approach for detecting malicious JavaScript code

https://doi.org/10.1002/sec.1441
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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JSOD: JavaScript obfuscation detector
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Mitigating Drive-By Download Attacks: Challenges and Open Problems
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Comparisons of machine learning techniques for detecting malicious webpages
inferredcites →https://doi.org/10.1016/j.future.2014.06.001openalex-snapshot
Hybrids of support vector machine wrapper and filter based framework for malware detection
inferredcites →https://doi.org/10.1016/j.jss.2014.10.031openalex-snapshot
Profiling and classifying the behavior of malicious codes
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Independent component analysis: algorithms and applications
inferredcites →https://doi.org/10.1080/19393555.2011.624160openalex-snapshot
Analysis and Identification of Malicious JavaScript Code
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
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20th Annual Computer Security Applications Conference
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Honeyware: A Web-Based Low Interaction Client Honeypot
inferredcites →https://doi.org/10.1109/malware.2009.5403020openalex-snapshot
Obfuscated malicious javascript detection using classification techniques
inferredcites →https://doi.org/10.1145/1150402.1150436openalex-snapshot
Very sparse random projections
inferredcites →https://doi.org/10.1145/1772690.1772720openalex-snapshot
Detection and analysis of drive-by-download attacks and malicious JavaScript code
inferredcites →https://doi.org/10.1145/1920261.1920267openalex-snapshot
Cujo
inferredcites →https://doi.org/10.1145/1963405.1963436openalex-snapshot
Prophiler
inferredcites →https://doi.org/10.1145/2381896.2381911openalex-snapshot
Autonomous learning for detection of JavaScript attacks
inferredcites →https://doi.org/10.1162/neco.2006.18.7.1527openalex-snapshot
A Fast Learning Algorithm for Deep Belief Nets
inferredcites →https://doi.org/10.1162/neco.2010.08-09-1081openalex-snapshot
Deep Belief Networks Are Compact Universal Approximators
inferredcites →https://doi.org/10.13140/2.1.1324.2249openalex-snapshot
Hulk: Eliciting Malicious Behavior in Browser Extensions
inferredcites →https://doi.org/10.20982/tqmp.09.2.p079openalex-snapshot
A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis
inferredcites →https://doi.org/10.3115/v1/p14-1068openalex-snapshot
Learning Grounded Meaning Representations with Autoencoders
inferredcites →https://doi.org/10.4018/978-1-4666-2083-4openalex-snapshot
IT Security Governance Innovations: Theory and Research
inferredcites →https://doi.org/10.4018/978-1-4666-2083-4.ch011openalex-snapshot
Information Security Governance
inferredcites →https://doi.org/10.5555/1756006.1953039openalex-snapshot
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
inferredcites →https://doi.org/10.6688/jise.2012.28.5.6openalex-snapshot
Efficient Detection of Malicious Web Pages Using High-Interaction Client Honeypots *
inferredcites →https://doi.org/10.7551/mitpress/7503.003.0024openalex-snapshot
Greedy Layer-Wise Training of Deep Networks

What points at this work

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MM-ConvBERT-LMS: Detecting Malicious Web Pages via Multi-Modal Learning and Pre-Trained Model
inferred← citeshttps://doi.org/10.3390/app13074365openalex-snapshot
Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks
inferred← citeshttps://doi.org/10.3390/app15063348openalex-snapshot
XSShield: Defending Against Stored XSS Attacks Using LLM-Based Semantic Understanding
inferred← citeshttps://doi.org/10.3390/electronics14040810openalex-snapshot
Improving Cyber Defense Against Ransomware: A Generative Adversarial Networks-Based Adversarial Training Approach for Long Short-Term Memory Network Classifier
inferred← citeshttps://doi.org/10.3390/fi14040118openalex-snapshot
Deep Learning for Vulnerability and Attack Detection on Web Applications: A Systematic Literature Review
inferred← citeshttps://doi.org/10.3390/risks10080151openalex-snapshot
An Efficient Method for Pricing Analysis Based on Neural Networks
inferred← citeshttps://doi.org/10.3390/s22207896openalex-snapshot
Network Threat Detection Using Machine/Deep Learning in SDN-Based Platforms: A Comprehensive Analysis of State-of-the-Art Solutions, Discussion, Challenges, and Future Research Direction
inferred← citeshttps://doi.org/10.35940/ijitee.b1121.1292s219openalex-snapshot
Hybrid Optimization Driven Technique for Malicious Javascript Detection Based on Deep Learning Classifier
inferred← citeshttps://doi.org/10.4018/978-1-6684-6275-1.ch006openalex-snapshot
Approaches for Detecting and Predicting Attacks Based on Deep and Reinforcement Learning to Improve Information Security
inferred← citeshttps://doi.org/10.4018/979-8-3693-9919-4.ch004openalex-snapshot
Autoencoder-Based Anomaly Detection for Cyber Threat Monitoring
inferred← citeshttps://doi.org/10.4018/ijdst.300354openalex-snapshot
An Approach for Malicious JavaScript Detection Using Adaptive Taylor Harris Hawks Optimization-Based Deep Convolutional Neural Network
inferred← citeshttps://doi.org/10.4018/ijirr.2021100102openalex-snapshot
Comprehensive Examination of Network Intrusion Detection Models on Data Science
inferred← citeshttps://doi.org/10.48550/arxiv.1610.06249openalex-snapshot
Multilevel Anomaly Detection for Mixed Data
inferred← citeshttps://doi.org/10.48550/arxiv.1701.07179openalex-snapshot
Malicious URL Detection using Machine Learning: A Survey
inferred← citeshttps://doi.org/10.48550/arxiv.1807.04739openalex-snapshot
When deep learning meets security
inferred← citeshttps://doi.org/10.48550/arxiv.1905.09538openalex-snapshot
AMSI-Based Detection of Malicious PowerShell Code Using Contextual Embeddings
inferred← citeshttps://doi.org/10.5121/ijcseit.2017.7600openalex-snapshot
inferred← citeshttps://doi.org/10.5121/ijcseit.2017.7601openalex-snapshot
Malicious URL Detection Using Convolutional Neural Network
inferred← citeshttps://doi.org/10.5121/ijnsa.2021.13602openalex-snapshot
Malicious Javascript Detection based on Clustering Techniques
inferred← citeshttps://doi.org/10.59266/houjs.2023.272openalex-snapshot
PHÁT HIỆN TẤN CÔNG XSS SỬ DỤNG HỌC MÁY KẾT HỢP
inferred← citeshttps://doi.org/10.7717/peerj-cs.328openalex-snapshot
Data augmentation-based conditional Wasserstein generative adversarial network-gradient penalty for XSS attack detection system
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