ENHANCING IOT DEVICES SECURITY: ENSEMBLE LEARNING WITH CLASSICAL APPROACHES FOR INTRUSION DETECTION SYSTEM
Date
2023-11-15
Authors
Journal Title
Journal ISSN
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Publisher
Saudi Digital Library
Abstract
The Internet of Things (IoT) refers to a network of interconnected nodes
constantly engaged in communication, data exchange, and the utilization of various
network protocols. Previous research has demonstrated that IoT devices are
highly susceptible to cyber-attacks, posing a significant threat to data security. This
vulnerability is primarily attributed to their susceptibility to exploitation and their
resource constraints. To counter these threats, Intrusion Detection Systems (IDS) are
employed. This study aims to contribute to the field by enhancing IDS detection efficiency
through the integration of Ensemble Learning (EL) methods with traditional
Machine Learning (ML) and deep learning (DL) models. To bolster IDS performance,
we initially utilize a binary ML classification approach to classify IoT network traffic
as either normal or abnormal, employing EL methods such as Stacking and Voting.
Once this binary ML model exhibits high detection rates, we extend our approach
by incorporating a ML multi-class framework to classify attack types. This further
enhances IDS performance by implementing the same Ensemble Learning methods.
Additionally, for further enhancement and evaluation of the intrusion detection system,
we employ DL methods, leveraging deep learning techniques, ensemble feature
v
selections, and ensemble methods. Our DL approach is designed to classify IoT
network traffic. This comprehensive approach encompasses various supervised ML,
and DL algorithms with ensemble methods. The proposed models are trained on
TON-IoT network traffic datasets. The ensemble approaches are evaluated using a
comprehensive metrics and compared for their effectiveness in addressing this classification
tasks. The ensemble classifiers achieved higher accuracy rates compared to
individual models, a result attributed to the diversity of learning mechanisms and
strengths harnessed through ensemble learning. By combining these strategies, we
successfully improved prediction accuracy while minimizing classification errors. The
outcomes of these methodologies underscore their potential to significantly enhance
the effectiveness of the Intrusion Detection System.
Description
This file is my dissertation of PhD degree.
Keywords
T Security, Internet of Things, Machine Learning, Intrusion Detection Systems.