A Novel Feature Reduction Technique for Detection of DoS Attack on Dataset | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. Supriya Dicholkar: Doctor, K. J. Somaiya College of Engineering,Vidyavihar,India

2. Jagannath Nirmal: Somaiya College of Engineering, Vidyavihar, India

Abstract:

The big datasets with numerous features characterizing network traffic parameters are used when creating an Intrusion Detection System (IDS) for machine learning-based IoT attack detection, Thus, one crucial stage in the dataset preprocessing procedure is feature reduction, as datasets with larger feature sets required more computation, storage, and processing time. The current work suggests a filter-based feature reduction technique that aggregates features selected from correlation (CR), relief factor (ReF), and information gain ratio (IGR). Initially, we select a feature subset from the CIE-CICIDS2018 dataset by applying all the above techniques using the average weight threshold. Then further feature optimization is carried out with a novel proposed subset aggregate strategy (SAS). The proposed SAS feature reduction method with three variants (SAS1, SAS2, and SAS3) efficiently selects common features from three feature subsets formed with three feature reduction techniques. With the SAS strategy, only 57, 27, and 4 features are selected out of a total of 78 features in the CIC-IDS2018 dataset, respectively. Following the feature set reduction, a rule-based PART classifier is used. When metrics like accuracy, precision, recall, and model-building time are compared to the performance of state-of-the-art systems, the proposed novel method outperforms them.

Page(s): 1-100

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 3, Year: 2024

Keywords:

Feature selection , Internet of Things , Classification algorithms , Intrusion Detection , Pattern Analysis

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