A Design of an Integrated Cloud-based Intrusion Detection System with Third Party Cloud Service

Author:

Elmasry Wisam1,Akbulut Akhan2,Zaim Abdul Halim1

Affiliation:

1. Department of Computer Engineering , Istanbul Commerce University , , Istanbul , Turkey

2. Department of Computer Engineering , Istanbul Kultur University , , Istanbul , Turkey

Abstract

Abstract Although cloud computing is considered the most widespread technology nowadays, it still suffers from many challenges, especially related to its security. Due to the open and distributed nature of the cloud environment, this makes the cloud itself vulnerable to various attacks. In this paper, the design of a novel integrated Cloud-based Intrusion Detection System (CIDS) is proposed to immunise the cloud against any possible attacks. The proposed CIDS consists of five main modules to do the following actions: monitoring the network, capturing the traffic flows, extracting features, analyzing the flows, detecting intrusions, taking a reaction, and logging all activities. Furthermore an enhanced bagging ensemble system of three deep learning models is utilized to predict intrusions effectively. Moreover, a third-party Cloud-based Intrusion Detection System Service (CIDSS) is also exploited to control the proposed CIDS and provide the reporting service. Finally, it has been shown that the proposed approach overcomes all problems associated with attacks on the cloud raised in the literature.

Publisher

Walter de Gruyter GmbH

Subject

General Computer Science

Reference70 articles.

1. Alguliev R. Abdullaeva F., Illegal access detection in the cloud computing environment, Journal of Information Security, 2014, 5(02), 65.

2. Alsafi H. M., Abduallah W. M., Pathan A.-S. K., Idps: An integrated intrusion handling model for cloud, arXiv preprint arXiv:1203.3323, 2012.

3. Ambusaidi M. A., He X., Nanda P., Tan Z., Building an intrusion detection system using a filter-based feature selection algorithm, IEEE transactions on computers, 2016, 65(10), 2986–2998.

4. Aminanto E. Kim K., Deep learning in intrusion detection system: An overview, 2016 International Research Conference on Engineering and Technology (2016 IRCET), Higher Education Forum, 2016.

5. Aminanto M. E. Kim K., Deep learning-based feature selection for intrusion detection system in transport layer, Proceedings of the Korea Institutes of Information Security and Cryptology Conference, 2016, 740–743.

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