Routing Attack Detection Using Ensemble Deep Learning Model for IIoT
Journal Title: Information Dynamics and Applications - Year 2023, Vol 2, Issue 1
Abstract
Smart cities, ITS, supply chains, and smart industries may all be developed with minimal human interaction thanks to the increasing prevalence of automation enabled by machine-type communication (MTC). Yet, MTC has substantial security difficulties because of diverse data, public network access, and an insufficient security mechanism. In this study, we develop a novel IIOT attack detection basis by joining the following four main steps: (a) data collection, (b) pre-processing, (c) attack detection, and (d) optimisation for high classification accuracy. At the initial stage of processing, known as "pre-processing," the collected raw data (input) is normalised. Attack detection requires the creation of an intelligent security architecture for IIoT networks. In this work, we present a learning model that can recognise previously unrecognised attacks on an IIoT network without the use of a labelled training set. An IoT network intrusion detection system-generated labelled dataset. The study also introduces a hybrid optimisation algorithm for pinpointing the optimal LSTM weight when it comes to intrusion detection. When trained on the labelled dataset provided by the proposed method, the improved LSTM outperforms the other models with a finding accuracy of 95%, as exposed in the research.
Authors and Affiliations
Ramesh Vatambeti, Gowtham Mamidisetti
Keywords
Related Articles
- EP ID EP732635
- DOI https://doi.org/10.56578/ida020104
- Views 61
- Downloads 0
A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans
Brain tumors are a critical public health concern, often resulting in limited life expectancy for patients. Accurate diagnosis of brain tumors is crucial to develop effective treatment strategies and improve patients' qu...
Multi-Channel Scheduling for Short-Range Wireless Communication Networks Using a Q-Learning Feedback Mechanism
The traditional channel scheduling methods in short-range wireless communication networks are often constrained by fixed rules, resulting in inefficient channel resource utilization and unstable data communication. To...
Enhancing Pneumonia Diagnosis with Transfer Learning: A Deep Learning Approach
The significant impact of pneumonia on public health, particularly among vulnerable populations, underscores the critical need for early detection and treatment. This research leverages the National Institutes of Health...
Enhanced Defect Detection in Insulator Iron Caps Using Improved YOLOv8n
To address the challenges in detecting surface defects on insulator iron caps, particularly due to the complex backgrounds that hinder accurate identification, an improved defect detection algorithm based on YOLOv8n, w...
Advancements in Cow Health Monitoring: A Systematic Literature Review of IoT Applications
The landscape of livestock farming is undergoing a significant transformation, primarily influenced by the integration of the Internet of Things (IoT) technology. This systematic literature review (SLR) critically examin...