An Improved Algorithm for Network Intrusion Detection Based on Deep Residual Networks

被引：1

作者：

Hu, Xuntao ^{[1
]}

Meng, Xiancai ^{[2
]}

Liu, Shaoqing ^{[2
]}

Liang, Lizhen ^{[2
]}

机构：

[1] Anhui Univ Sci & Technol, Huainan 232001, Peoples R China

[2] Hefei Comprehens Natl Sci Ctr, Inst Energy, Hefei 230031, Anhui, Peoples R China

来源：

IEEE ACCESS | 2024年 / 12卷

关键词：

Logic gates; Feature extraction; Intrusion detection; Adaptation models; Convolutional neural networks; Residual neural networks; Data models; Long short term memory; Bidirectional control; residual networks; hybrid attention mechanisms; bidirectional long and short memory networks; AUTHORIZATION USAGE CONTROL; SAFETY DECIDABILITY; INTERNET; THINGS;

D O I：

10.1109/ACCESS.2024.3398007

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The goal of current research will be to increase the accuracy and generalisation capacity of intrusion detection models in order to better handle the complex network security issues of today. In this paper, a new hybrid attention mechanism is introduced along with an enhanced algorithm. Through the effective channel layer and curve space layer, the feature information will be concentrated on the necessary feature information, allowing the model to concentrate more on the features linked to classification and become more broadly applicable. Increase the model's precision. The experimental results demonstrated that the accuracy can achieve 100%, 99.79%, and 98.10% on binary classification problems and 96.37%, 98.12%, and 99.06% on multiclassification problems, respectively, using the UNSW-NB15, CICIDS-2017, and CICIDS-2018 datasets for validation.

引用

页码：66432 / 66441

页数：10

共 50 条

[1] Intrusion Detection for IoT Based on Improved Genetic Algorithm and Deep Belief Network
Zhang, Ying
Li, Peisong
Wang, Xinheng
IEEE ACCESS, 2019, 7 : 31711 - 31722
[2] Innovative Defense: Deep Learning-Powered Intrusion Detection for IoT Networks
Binbusayyis, Adel
IEEE ACCESS, 2025, 13 : 31105 - 31120
[3] An intrusion detection approach based on improved deep belief network
Tian, Qiuting
Han, Dezhi
Li, Kuan-Ching
Liu, Xingao
Duan, Letian
Castiglione, Arcangelo
APPLIED INTELLIGENCE, 2020, 50 (10) : 3162 - 3178
[4] Enhanced Multigrained Scanning-Based Deep Stacking Network for Intrusion Detection in IoMT Networks
Musikawan, Pakarat
Kongsorot, Yanika
Aimtongkham, Phet
So-In, Chakchai
IEEE ACCESS, 2024, 12 : 152482 - 152497
[5] Intrusion Detection in IoT Networks Using Deep Learning Algorithm
Susilo, Bambang
Sari, Riri Fitri
INFORMATION, 2020, 11 (05)
[6] An Intrusion Detection Approach Based on Improved Deep Belief Network and LightGBM
Tian, Qiuting
2022 6TH INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND INTELLIGENT CONTROL, ISCSIC, 2022, : 40 - 44
[7] Feedback deer hunting optimization algorithm for intrusion detection in cloud based deep residual network
Soniya, Sobin S.
Vigila, Maria Celestin S.
INTERNATIONAL JOURNAL OF MODELING SIMULATION AND SCIENTIFIC COMPUTING, 2021, 12 (06)
[8] Pelican: A Deep Residual Network for Network Intrusion Detection
Wu, Peilun
Guo, Hui
Moustafa, Nour
50TH ANNUAL IEEE/IFIP INTERNATIONAL CONFERENCE ON DEPENDABLE SYSTEMS AND NETWORKS WORKSHOPS (DSN-W 2020), 2020, : 55 - 62
[9] An intrusion detection approach based on improved deep belief network
Qiuting Tian
Dezhi Han
Kuan-Ching Li
Xingao Liu
Letian Duan
Arcangelo Castiglione
Applied Intelligence, 2020, 50 : 3162 - 3178
[10] An efficient deep learning-based solution for network intrusion detection in wireless sensor network
Sharma, Hanjabam Saratchandra
Sarkar, Arindam
Singh, Moirangthem Marjit
INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT, 2023, 14 (06) : 2423 - 2446

← 1 2 3 4 5 →