Evaluation of Deep Learning Models in ITS Software-Defined Intrusion Detection Systems

Document Type

Conference Proceeding

Source of Publication

NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium

Publication Date

4-29-2022

Abstract

Intelligent Transportation Systems (ITS), mainly Autonomous Vehicles (AV's), are susceptible to security and safety problems that risk the users' lives. Sophisticated threats can damage the security of AV's communications and computational capabilities, slowing down their integration into our daily lives. Cyber-attacks are getting more complex, posing greater hurdles in identifying intrusions effectively. Failing to prevent the intrusions could tarnish the security services' reliability, including data confidentiality, authenticity, and reliability. IDS is an overall prediction paradigm for detecting malicious network traffic in the ITS. This article studies the role of machine or deep learning in Software Defined-Intrusion Detection System (SD-IDS) in ITS; discusses the mathematical analysis of existing deep learning models and evaluates their performances on the basis of the various metrics (i.e., accuracy, precision, recall, f-measure) to observe which model gives the best results for the existing state of art. The results show that improved Recurrent Neural Networks (RNN) is best suited for the detection of SD-IDS attacks in the data plane and control plane.

Publisher

IEEE

Volume

00

First Page

1

Last Page

6

Disciplines

Computer Sciences

Keywords

Deep learning, Measurement, Analytical models, Recurrent neural networks, Transportation, Throughput, Software

Indexed in Scopus

no

Open Access

no

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