Predictive machine learning-based integrated approach for DDoS detection and prevention

Distributed Denial of Service attack has been a huge threat to the Internet and may carry extreme losses to systems, companies, and national security. The invader can disseminate Distributed denial of service (DDoS) attacks easily, and it ends up being significantly harder to recognize and forestall...

Full description

Saved in:
Bibliographic Details
Published in:Multimedia tools and applications 2022, Vol.81 (3), p.4185-4211
Main Authors: Kebede, Solomon Damena, Tiwari, Basant, Tiwari, Vivek, Chandravanshi, Kamlesh
Format: Article
Language:English
Subjects:
Algorithms
Computer Communication Networks
Computer Science
Cybersecurity
Data mining
Data Structures and Information Theory
Datasets
Denial of service attacks
Machine learning
Malware
Multimedia Information Systems
Security management
Special Purpose and Application-Based Systems
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Distributed Denial of Service attack has been a huge threat to the Internet and may carry extreme losses to systems, companies, and national security. The invader can disseminate Distributed denial of service (DDoS) attacks easily, and it ends up being significantly harder to recognize and forestall DDoS attacks. In recent years, many IT-based companies are attacked by DDoS attacks. In this view, the primary concern of this work is to detect and prevent DDoS attacks. To fulfill the objective, various data mining techniques such that Jrip, J48, and k-NN have been employed for DDoS attacks detection. These algorithms are implemented and thoroughly evaluated individually to validate their performance in this domain. The presented work has been evaluated using the latest dataset CICIDS2017. The dataset characterizes different DDoS attacks viz. brute force SSH, brute force FTP, Heartbleed, infiltration, botnet TCP, UDP, and HTTP with port scan attack. Further, the prevention method takes place in progress to block the malicious nodes participates in any of the said attacks. The proposed DDoS prevention works in a proactive mode to defend all these attack types and gets evaluated concerning various parameters such as Throughput, PDR, End-to-End Delay, and NRL. This study claimed that the proposed technique outperforms with respect to the AODV routing algorithm.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-11740-z