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Clustering-Based Malicious JavaScript Detection by AIRCC - IJNSA is a document available to read on EtoBox.

The paper presents a method for detecting malicious JavaScript code using unsupervised clustering techniques, addressing the limitations of existing supervised learning models that rely on labeled datasets. The proposed model, based on the K-means algorithm, aims to achieve high accuracy while being computationally efficient and capable of identifying new forms of malicious code without needing constant updates to the dataset. Experimental results indicate that this approach effectively detects malicious Ja

Author

AIRCC - IJNSA

Language

EN