Detection of Distributed Denial of Service (DDoS) Attacks in IOT Based Monitoring System of Banking Sector Using Machine Learning Models

Umar Islam, Ali Muhammad, Rafiq Mansoor, Md Shamim Hossain, Ijaz Ahmad, Elsayed Tag Eldin, Javed Ali Khan, Ateeq Ur Rehman and Muhammad Shafiq
Additional contact information
Umar Islam: Department of Computer Science, IQRA National University, Swat Campus, Swat 19220, Pakistan
Ali Muhammad: Institute of Management Studies, University of Peshawar, Peshawar 25000, Pakistan
Rafiq Mansoor: Department of Mechanical Engineering, International Islamic University Islamabad, Islamabad 44000, Pakistan
Md Shamim Hossain: Department of Marketing, Hajee Mohammad Danesh Science & Technology University, Dinajpur 5200, Bangladesh
Ijaz Ahmad: Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen 518040, China
Elsayed Tag Eldin: Electrical Engineering Department, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
Javed Ali Khan: Department of Software Engineering, University of Science & Technology, Bannu 28100, Pakistan
Ateeq Ur Rehman: Department of Electrical Engineering, Government College University, Lahore 54000, Pakistan
Muhammad Shafiq: Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea

Sustainability, 2022, vol. 14, issue 14, 1-18

Abstract: Cyberattacks can trigger power outages, military equipment problems, and breaches of confidential information, i.e., medical records could be stolen if they get into the wrong hands. Due to the great monetary worth of the data it holds, the banking industry is particularly at risk. As the number of digital footprints of banks grows, so does the attack surface that hackers can exploit. This paper aims to detect distributed denial-of-service (DDOS) attacks on financial organizations using the Banking Dataset. In this research, we have used multiple classification models for the prediction of DDOS attacks. We have added some complexity to the architecture of generic models to enable them to perform well. We have further applied a support vector machine (SVM), K-Nearest Neighbors (KNN) and random forest algorithms (RF). The SVM shows an accuracy of 99.5%, while KNN and RF scored an accuracy of 97.5% and 98.74%, respectively, for the detection of (DDoS) attacks. Upon comparison, it has been concluded that the SVM is more robust as compared to KNN, RF and existing machine learning (ML) and deep learning (DL) approaches.

Keywords: machine learning; support vector machine; distributed denial-of-service (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/14/8374/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/14/8374/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:14:p:8374-:d:858705

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().