Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron

Sheeraz Ahmed, Zahoor Ali Khan, Syed Muhammad Mohsin (), Shahid Latif, Sheraz Aslam (), Hana Mujlid, Muhammad Adil and Zeeshan Najam
Additional contact information
Sheeraz Ahmed: Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan
Zahoor Ali Khan: Faculty of Computer Information Science, Higher Colleges of Technology, Fujairah 4114, United Arab Emirates
Syed Muhammad Mohsin: Department of Computer Science, COMSATS University Islamabad, Islamabad 45550, Pakistan
Shahid Latif: Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan
Sheraz Aslam: Department of Electrical Engineering, Computer Engineering, and Informatics, Cyprus University of Technology, Limassol 3036, Cyprus
Hana Mujlid: Department of Computer Engineering, Taif University, Taif 11099, Saudi Arabia
Muhammad Adil: Department of Computer Science, Iqra National University, Peshawar 25000, Pakistan
Zeeshan Najam: CEO, Ultimate Engineering Consultants Private Limited, Peshawar 25000, Pakistan

Future Internet, 2023, vol. 15, issue 2, 1-24

Abstract: Distributed denial of service (DDoS) attacks pose an increasing threat to businesses and government agencies. They harm internet businesses, limit access to information and services, and damage corporate brands. Attackers use application layer DDoS attacks that are not easily detectable because of impersonating authentic users. In this study, we address novel application layer DDoS attacks by analyzing the characteristics of incoming packets, including the size of HTTP frame packets, the number of Internet Protocol (IP) addresses sent, constant mappings of ports, and the number of IP addresses using proxy IP. We analyzed client behavior in public attacks using standard datasets, the CTU-13 dataset, real weblogs (dataset) from our organization, and experimentally created datasets from DDoS attack tools: Slow Lairs, Hulk, Golden Eyes, and Xerex. A multilayer perceptron (MLP), a deep learning algorithm, is used to evaluate the effectiveness of metrics-based attack detection. Simulation results show that the proposed MLP classification algorithm has an efficiency of 98.99% in detecting DDoS attacks. The performance of our proposed technique provided the lowest value of false positives of 2.11% compared to conventional classifiers, i.e., Naïve Bayes, Decision Stump, Logistic Model Tree, Naïve Bayes Updateable, Naïve Bayes Multinomial Text, AdaBoostM1, Attribute Selected Classifier, Iterative Classifier, and OneR.

Keywords: DDoS attack; attack; attack detection; botnet; MLP classifier (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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