Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model

被引:3
|
作者
Alghamdi, Rayed [1 ]
机构
[1] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah 21589, Saudi Arabia
关键词
network intrusion detection system; network security; lion optimization algorithm; feature selection; deep learning; 68-11;
D O I
10.3390/math11224607
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In the domain of network security, intrusion detection systems (IDSs) play a vital role in data security. While the utilization of the internet amongst consumers is increasing on a daily basis, the significance of security and privacy preservation of system alerts, due to malicious actions, is also increasing. IDS is a widely executed system that protects computer networks from attacks. For the identification of unknown attacks and anomalies, several Machine Learning (ML) approaches such as Neural Networks (NNs) are explored. However, in real-world applications, the classification performances of these approaches are fluctuant with distinct databases. The major reason for this drawback is the presence of some ineffective or redundant features. So, the current study proposes the Network Intrusion Detection System using a Lion Optimization Feature Selection with a Deep Learning (NIDS-LOFSDL) approach to remedy the aforementioned issue. The NIDS-LOFSDL technique follows the concept of FS with a hyperparameter-tuned DL model for the recognition of intrusions. For the purpose of FS, the NIDS-LOFSDL method uses the LOFS technique, which helps in improving the classification results. Furthermore, the attention-based bi-directional long short-term memory (ABiLSTM) system is applied for intrusion detection. In order to enhance the intrusion detection performance of the ABiLSTM algorithm, the gorilla troops optimizer (GTO) is deployed so as to perform hyperparameter tuning. Since trial-and-error manual hyperparameter tuning is a tedious process, the GTO-based hyperparameter tuning process is performed, which demonstrates the novelty of the work. In order to validate the enhanced solution of the NIDS-LOFSDL system in terms of intrusion detection, a comprehensive range of experiments was performed. The simulation values confirm the promising results of the NIDS-LOFSDL system compared to existing DL methodologies, with a maximum accuracy of 96.88% and 96.92% on UNSW-NB15 and AWID datasets, respectively.
引用
收藏
页数:17
相关论文
共 50 条
  • [1] A data-driven network intrusion detection system using feature selection and deep learning
    Zhang, Lianming
    Liu, Kui
    Xie, Xiaowei
    Bai, Wenji
    Wu, Baolin
    Dong, Pingping
    JOURNAL OF INFORMATION SECURITY AND APPLICATIONS, 2023, 78
  • [2] Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features
    Pingale, Subhash V.
    Sutar, Sanjay R.
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 210
  • [3] Intrusion detection system using metaheuristic fireworks optimization based feature selection with deep learning on Internet of Things environment
    Jayasankar, T.
    Buri, R. Kiruba
    Maheswaravenkatesh, P.
    JOURNAL OF FORECASTING, 2024, 43 (02) : 415 - 428
  • [4] BSLnO: Multi-agent based distributed intrusion detection system using Bat Sea Lion Optimization-based hybrid deep learning approach
    Maram, Balajee
    Mandala, Jyothi
    Satish, Aravapalli Rama
    INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2022, 36 (08) : 1909 - 1930
  • [5] Modeling network intrusion detection system using feature selection and parameters optimization
    Kim, Dong Seong
    Park, Gong Sou
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2008, E91D (04) : 1050 - 1057
  • [6] Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System
    Henry, Azriel
    Gautam, Sunil
    Khanna, Samrat
    Rabie, Khaled
    Shongwe, Thokozani
    Bhattacharya, Pronaya
    Sharma, Bhisham
    Chowdhury, Subrata
    SENSORS, 2023, 23 (02)
  • [7] Feature Selection with Deep Reinforcement Learning for Intrusion Detection System
    Priya S.
    Pradeep Mohan Kumar K.
    Computer Systems Science and Engineering, 2023, 46 (03): : 3339 - 3353
  • [8] Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection
    Ayo, Femi Emmanuel
    Folorunso, Sakinat Oluwabukonla
    Abayomi-Alli, Adebayo A.
    Adekunle, Adebola Olayinka
    Awotunde, Joseph Bamidele
    INFORMATION SECURITY JOURNAL, 2020, 29 (06): : 267 - 283
  • [9] A feed forward deep neural network model using feature selection for cloud intrusion detection system
    Sharma, Hidangmayum Satyajeet
    Singh, Khundrakpam Johnson
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE, 2024, 36 (09):
  • [10] Performance analysis and feature selection for network-based intrusion detection with deep learning
    Caner, Serhat
    Erdogmus, Nesli
    Erten, Y. Murat
    TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2022, 30 (03) : 629 - 643