1D-CNN-IDS: 1D CNN-based Intrusion Detection System for IIoT
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866913498914619392 |
|---|---|
| author | Arslan, Muhammad Mubeen, Muhammad Bilal, Muhammad Abbasi, Saadullah Farooq |
| author_facet | Arslan, Muhammad Mubeen, Muhammad Bilal, Muhammad Abbasi, Saadullah Farooq |
| contents | The demand of the Internet of Things (IoT) has witnessed exponential growth. These progresses are made possible by the technological advancements in artificial intelligence, cloud computing, and edge computing. However, these advancements exhibit multiple challenges, including cyber threats, security and privacy concerns, and the risk of potential financial losses. For this reason, this study developed a computationally inexpensive one-dimensional convolutional neural network (1DCNN) algorithm for cyber-attack classification. The proposed study achieved an accuracy of 99.90% to classify nine cyber-attacks. Multiple other performance metrices have been evaluated to validate the efficacy of the proposed scheme. In addition, comparison has been done with existing state-of-the-art schemes. The findings of the proposed study can significantly contribute to the development of secure intrusion detection for IIoT systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08529 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 1D-CNN-IDS: 1D CNN-based Intrusion Detection System for IIoT Arslan, Muhammad Mubeen, Muhammad Bilal, Muhammad Abbasi, Saadullah Farooq Cryptography and Security The demand of the Internet of Things (IoT) has witnessed exponential growth. These progresses are made possible by the technological advancements in artificial intelligence, cloud computing, and edge computing. However, these advancements exhibit multiple challenges, including cyber threats, security and privacy concerns, and the risk of potential financial losses. For this reason, this study developed a computationally inexpensive one-dimensional convolutional neural network (1DCNN) algorithm for cyber-attack classification. The proposed study achieved an accuracy of 99.90% to classify nine cyber-attacks. Multiple other performance metrices have been evaluated to validate the efficacy of the proposed scheme. In addition, comparison has been done with existing state-of-the-art schemes. The findings of the proposed study can significantly contribute to the development of secure intrusion detection for IIoT systems. |
| title | 1D-CNN-IDS: 1D CNN-based Intrusion Detection System for IIoT |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2409.08529 |