Design of a Network Intrusion Detection System Using Complex Deep Neuronal Networks

Author:

Al-Shabi Mohammed AbdulhammedORCID

Abstract

Recent years have witnessed a tremendous development in various scientific and industrial fields. As a result, different types of networks are widely introduced which are vulnerable to intrusion. In view of the same, numerous studies have been devoted to detecting all types of intrusion and protect the networks from these penetrations. In this paper, a novel network intrusion detection system has been designed to detect cyber-attacks using complex deep neuronal networks. The developed system is trained and tested on the standard dataset KDDCUP99 via pycharm program. Relevant to existing intrusion detection methods with similar deep neuronal networks and traditional machine learning algorithms, the proposed detection system achieves better results in terms of detection accuracy.

Publisher

Auricle Technologies, Pvt., Ltd.

Subject

Computer Networks and Communications

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Toward Deep Learning based Intrusion Detection System: A Survey;Proceedings of the 2024 6th International Conference on Big Data Engineering;2024-07-24

2. Influence of grey wolf optimization feature selection on gradient boosting machine learning techniques for accurate detection of liver tumor;SN Applied Sciences;2023-06-07

3. Lightweight Cryptography Approach for Multifactor Authentication in Internet of Things;2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon);2022-10-16

4. Enhancing Collaborative Intrusion detection networks against insider attack using supervised learning technique;2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon);2022-10-16

5. Neural Network based Intrusion Detection system for critical infrastructure;2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon);2022-10-16

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3