Title: Forecasting DDoS attack with machine learning for network forensic investigation

Authors: Saswati Chatterjee; Suneeta Satpathy; Bijay Kumar Paikaray

Addresses: Faculty of Emerging Technologies, Sri Sri University, Cuttack, India ' Faculty of Emerging Technologies, Sri Sri University, Cuttack, India ' Center for Data Science, SOA University, Odisha, India

Abstract: The recognition of intrusion attempts is the fundamental region of network security, with the objective of identifying the impact of these actions on the distinctive variations of the captured traffic. Innovation and inquiry are now necessary for most of the attacks. In this paper, a machine-learning approach has been used to track anomalous network traffic. Also, the statistical parameters are employed to enhance the performance based on learning models. The paper also represents a detection pattern through machine learning experiments for DDoS attacks on KDD Cup 99 dataset where ten features are used for accuracy measures. The study further classifies the hidden forms to sense the DDoS attack patterns. The paper further concludes with the experimental outcomes that establish the improved performance assessment of the K-nearest neighbour algorithm in comparison to other predictable learning approaches and thus the proposed model can reach the uppermost correctness.

Keywords: distributed denial of service; DDoS; K-nearest neighbour; KNN; classification; machine learning; support vector machine; SVM; true positive rate; TPR.

DOI: 10.1504/IJRIS.2024.143163

International Journal of Reasoning-based Intelligent Systems, 2024 Vol.16 No.5, pp.352 - 359

Received: 01 Apr 2023
Accepted: 27 Apr 2023
Published online: 05 Dec 2024 *

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