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A Proactive Method of the Webshell Detection and Prevention based on Deep Traffic Analysis

Le, Viet Ha and Du, Phuong Hanh and Nguyen, Ngoc Cuong and Nguyen, Ngoc Hoa and Hoang, Viet Long (2021) A Proactive Method of the Webshell Detection and Prevention based on Deep Traffic Analysis. International Journal of Web and Grid Services . ISSN 1741-1114 (In Press)

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The popularity of today's web application has led to web servers are frequently objects to injecting webshell attacks. In this paper, we propose a new deep inspection method, namely DLWD, to detect in real-time and proactively prevent webshell attacks. DLWSD is composed of both signature-based and DNN deep learning-based detection. Moreover, to avoid bottlenecks, DLWSD built-in DeepInspector inspects in real-time the large-scale traffic flows with a strategy of periodic sampling at a defined frequency and interval for only flows that do not satisfy any signature. DeepInspector can create/update rules from webshell attacking alert results to prevent in future. We also proposed a mechanism using the cross-entropy loss function to regulate the training imbalanced dataset. Our experiments allow validating the performance of DLWSD using a popular dataset CSE-CIC-IDS2018 with the metrics (Accuracy, F1-score, FPR) of (99.99%, 99.98%, 0.01%) respectively. It is also better compared with other studies using the same dataset.

Item Type: Article
Subjects: Scopus-indexed journals
ISI-indexed journals
Divisions: Faculty of Information Technology (FIT)
Depositing User: Assoc.Prof Hoá NGUYỄN Ngọc
Date Deposited: 10 Dec 2021 01:12
Last Modified: 10 Dec 2021 01:12

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