A New IDS for Detecting DDoS Attacks in Wireless Networks using Spotted Hyena Optimization and Fuzzy Temporal CNN

被引：2

作者：

Nalayini, C. M. ^{[1
]}

Katiravan, Jeevaa ^{[1
]}

机构：

[1] Velammal Engn Coll, Dept Informat Technol, Chennai, India

来源：

JOURNAL OF INTERNET TECHNOLOGY | 2023年 / 24卷 / 01期

关键词：

Fuzzy Decision Tree; Feature selection; CNN; FT-CNN; LSTM; INTRUSION DETECTION; DEFENSE; FRAMEWORK; ALGORITHM; SYSTEM;

D O I：

10.53106/160792642023012401003

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Cyber-attacks are rapidly increasing in the internet era due to the growth of information technology. The distributed denial of service (DDoS) attacks are increasing dramatically due to the distributed services in cloud networks. In this paper, a new Intrusion Detection System (IDS) is proposed to improve the performance of the networks by detecting DDoS attacks effectively in wireless networks. In this work, Feature Selection and Spotted Hyena Optimization Based Feature Optimization Method (SFSH-FOM) to select the terms of accuracy and false alarm rate.

引用

页码：23 / 34

页数：12

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