A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection

Title

A Novel Paradigm for IoT Security: ResNet-GRU Model Revolutionizes Botnet Attack Detection

Creator

Jyotsna A.; Anita M.E.A.

Description

The rapid proliferation of the Internet of Things (IoT) has engendered substantial security apprehensions, chiefly due to the emergence of botnet attacks. This research study delves into the realm of Intrusion Detection Systems (IDS) by leveraging the IoT23 dataset, with a specific emphasis on the intricate domain of IoT at the network's edge. The evolution of edge computing underscores the exigency for tailored security solutions. An array of statistical methodologies, encompassing ANOVA, Kruskal-Wallis, and Friedman tests, is systematically employed to illuminate the evolving trends across multiple facets of the study. Given the intricacies entailed in feature selection within edge environments, Chi-square analyses, Recursive Feature Elimination (RFE), and Lasso-based techniques are strategically harnessed to unearth meaningful feature subsets. A meticulous evaluation encompassing 19 classifiers, meticulously selected from both machine learning (ML) and deep learning (DL) paradigms, is rigorously conducted. Initial findings underscore the potential of the Gated Recurrent Unit (GRU) model, especially when coupled with intrinsic lasso-based feature selection. This promising outcome catalyzes the formulation of an ensemble approach that harnesses multiple LassoCV models, aimed at amplifying feature selection proficiency. Furthermore, an optimized ResNet-GRU model emerges from the fusion of the GRU and ResNet architectures, with the objective of augmenting classification performance. In response to mounting concerns regarding data privacy at the edge, a resilient federated learning ecosystem is meticulously crafted. The seamless integration of the optimized ResNet-GRU model into this framework facilitates the employment of FedAvg, a widely acclaimed federated learning methodology, to adeptly navigate the intricacies associated with data sharing challenges. A comprehensive performance evaluation is undertaken, wherein the ResNet-GRU model is benchmarked against FedAvg and a diverse array of other federated learning algorithms, including FedProx and Fed+. This extensive comparative analysis encompasses a spectrum of performance metrics and processing time benchmarks, shedding comprehensive light on the capabilities of the model. (2023), (Science and Information Organization). All Rights Reserved.

Source

International Journal of Advanced Computer Science and Applications, Vol-14, No. 12, pp. 298-310.

Date

2023-01-01

Publisher

Science and Information Organization

Subject

federated learning; Gated Recurrent Neural Networks; Internet of things; Long Short Term Memory (LSTM)

Coverage

Jyotsna A., Dept. of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India, Dept. of Computer Science and Engineering, RSET, Kochi, India; Anita M.E.A., Dept. of Computer Science and Engineering, Christ (Deemed to be University), Bangalore, India, Dept. of Computer Science and Engineering, RSET, Kochi, India

Rights

All Open Access; Gold Open Access

Relation

ISSN: 2158107X

Format

Online

Language

English

Type

Article

Identifier

https://doi.org/10.14569/IJACSA.2023.0141231

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184655403&doi=10.14569%2fIJACSA.2023.0141231&partnerID=40&md5=a90f4b74da3d7a955ef5084328602681