Intercept the Cloud Network From Brute Force and DDoS Attacks via Intrusion Detection and Prevention System

被引:35
作者
Nadeem, Muhammad [1 ]
Arshad, Ali [2 ]
Riaz, Saman [3 ]
Band, Shahab S. [4 ]
Mosavi, Amir [5 ,6 ]
机构
[1] Abasyn Univ, Dept Comp, Islamabad 75660, Pakistan
[2] Inst Space & Technol, Dept Comp Sci, Islamabad 44000, Pakistan
[3] Natl Univ Technol, Dept Comp Sci, Islamabad 44000, Pakistan
[4] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan
[5] Tech Univ Dresden, Fac Civil Engn, D-01069 Dresden, Germany
[6] Obuda Univ, John von Neumann Fac Informat, H-1034 Budapest, Hungary
关键词
Cloud computing; Servers; Intrusion detection; Computational modeling; Security; Monitoring; Denial-of-service attack; Intrusion detection system; network based intrusion detection system; host based intrusion detection system; spam; DDoS; Bot; cloud security; ENSEMBLE; MODEL;
D O I
10.1109/ACCESS.2021.3126535
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cloud computing is considered to be the best technique for storing data online instead of using a hard drive. It includes three different types of computing services that are provided to remote users via the Internet. Cloud computing offers its end users a variety of options, such as cost savings, access to online resources and performance, but as the number of users in cloud computing grows, so does the likelihood of an attack. Various researchers have researched and provided many solutions to prevent these attacks. One of the best ways to detect an attack is through an Intrusion Detection System. This article will develop an efficient framework in which will use and discuss various security solutions for a network. Every device on the network will be attacked and the attack rate of the entire network will be monitored. After that, various solutions will be provided to protect the cloud server from attacks. Different principles will be used at the end of the article to test the accuracy of the results and from each conclusion it will be concluded to what extent the results of this paper are better than others.
引用
收藏
页码:152300 / 152309
页数:10
相关论文
共 55 条
[1]   Features Dimensionality Reduction Approaches for Machine Learning Based Network Intrusion Detection [J].
Abdulhammed, Razan ;
Musafer, Hassan ;
Alessa, Ali ;
Faezipour, Miad ;
Abuzneid, Abdelshakour .
ELECTRONICS, 2019, 8 (03)
[2]  
Aryachandra A. A., 2016, P 4 INT C INF COMM T, P1, DOI 10.1109/ICoICT.2016.7571954
[3]   Clustering Based DDoS Attack Detection Using The Relationship Between Packet Headers [J].
Ates, Cagatay ;
Ozdel, Suleyman ;
Anarim, Emin .
2019 INNOVATIONS IN INTELLIGENT SYSTEMS AND APPLICATIONS CONFERENCE (ASYU), 2019, :473-478
[4]  
Bakhareva N., 2019, 2019 INT RUSS AUT C
[5]  
Can O, 2015, INT CONF MODEL SIM
[6]   DDoS Attack Modeling and Detection Using SMO [J].
Daneshgadeh, Salva ;
Baykal, Nazife ;
Ertekin, Seyda .
2017 16TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2017, :432-436
[7]   Smart Detection: An Online Approach for DoS/DDoS Attack Detection Using Machine Learning [J].
de Lima Filho, Francisco Sales ;
Silveira, Frederico A. F. ;
Brito Junior, Agostinho de Medeiros ;
Vargas-Solar, Genoveva ;
Silveira, Luiz F. .
SECURITY AND COMMUNICATION NETWORKS, 2019, 2019
[8]   DDoS Attack Detection Method Based on Improved KNN With the Degree of DDoS Attack in Software-Defined Networks [J].
Dong, Shi ;
Sarem, Mudar .
IEEE ACCESS, 2020, 8 :5039-5048
[9]   On the combination of genetic fuzzy systems and pairwise learning for improving detection rates on Intrusion Detection Systems [J].
Elhag, Salma ;
Fernandez, Alberto ;
Bawakid, Abdullah ;
Alshomrani, Saleh ;
Herrera, Francisco .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (01) :193-202
[10]  
Farahnakian Fahimeh., 2018, International Journal of Digital Content Technology and its Applications, V12, P70