DeepRoughNetID: A Robust Framework for Network Anomaly Intrusion Detection with High Detection Rates

IF 1.3 4区计算机科学 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

IETE Journal of Research Pub Date : 2024-05-26 DOI:10.1080/03772063.2024.2350932

M. Nalini, B. Yamini, P. Sinthia, Praveena Rachel Kamala S

引用次数: 0

Abstract

Network security faces challenges, including reduced true positives, increased false positives, and inadequate anomaly detection efficacy. This paper introduces the DeepRoughNetID (DRNID) approach ...

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DeepRoughNetID：高检测率网络异常入侵检测的稳健框架

网络安全面临着各种挑战，包括真阳性降低、假阳性增加以及异常检测功效不足。本文介绍了 DeepRoughNetID（DRNID）方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IETE Journal of Research 工程技术-电信学

CiteScore

3.30

自引率

6.70%

发文量

308

期刊介绍： IETE Journal of Research is a Bimonthly journal published by the Institution of Electronics and Telecommunication Engineers (IETE), India. It publishes scientific and technical papers describing original research work or novel product/process development. Occasionally special issues are brought out on new and emerging research areas. IETE Journal of Research is useful to researchers, engineers, scientists, teachers, managers and students who are interested in keeping a track of original research and development work being carried out in the broad area of electronics, telecommunications, computer science and engineering and information technology.