Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement

Deobfuscation, unpacking, and decoding of obfuscated malicious JavaScript for machine learning models detection performance improvement

Obfuscation is rampant in both benign and malicious JavaScript (JS) codes. It generates an obscure and undetectable code that hinders comprehension and analysis. Therefore, accurate detection of JS codes that masquerade as innocuous scripts is vital. The existing deobfuscation methods assume that a...

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Bibliographic Details
Main Authors: Samuel Ndichu, Sangwook Kim, Seiichi Ozawa
Format: Article
Language:English
Published: Wiley 2020-06-01
Series:CAAI Transactions on Intelligence Technology
Subjects:
invasive software
java
internet
feature extraction
text analysis
vectors
learning (artificial intelligence)
formatted js code
deobfuscation methods
unpacking
dud-preprocessed obfuscated malicious js codes
term frequency–inverse document frequency model
long short-term memory model
paragraph vector models
obfuscated benign js codes
learned feature vectors
fasttext model
js code editor
multilayer obfuscation
original js code
undetectable code
obscure code
machine learning models detection
Online Access:https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0026

Internet

https://digital-library.theiet.org/content/journals/10.1049/trit.2020.0026

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