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
  • [21] An ensemble framework with improved hybrid breeding optimization-based feature selection for intrusion detection
    Ye, Zhiwei
    Luo, Jun
    Zhou, Wen
    Wang, Mingwei
    He, Qiyi
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2024, 151 : 124 - 136
  • [22] Ant colony optimization based network intrusion feature selection and detection
    Gao, HH
    Yang, HH
    Wang, XY
    PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, 2005, : 3871 - 3875
  • [23] Feature extraction using Deep Learning for Intrusion Detection System
    Ishaque, Mohammed
    Hudec, Ladislav
    2019 2ND INTERNATIONAL CONFERENCE ON COMPUTER APPLICATIONS & INFORMATION SECURITY (ICCAIS), 2019,
  • [24] Network Intrusion Detection System using Deep Learning
    Ashiku, Lirim
    Dagli, Cihan
    BIG DATA, IOT, AND AI FOR A SMARTER FUTURE, 2021, 185 : 239 - 247
  • [25] Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization
    Alkanhel, Reem
    El-kenawy, El-Sayed M.
    Abdelhamid, Abdelaziz A.
    Ibrahim, Abdelhameed
    Alohali, Manal Abdullah
    Abotaleb, Mostafa
    Khafaga, Doaa Sami
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 74 (02): : 2677 - 2693
  • [26] EFS-DNN: An Ensemble Feature Selection-Based Deep Learning Approach to Network Intrusion Detection System
    Wang, Zehong
    Liu, Jianhua
    Sun, Leyao
    SECURITY AND COMMUNICATION NETWORKS, 2022, 2022
  • [27] Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection
    Awotunde, Joseph Bamidele
    Chakraborty, Chinmay
    Adeniyi, Abidemi Emmanuel
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2021, 2021
  • [28] Intrusion Detection in Industrial Internet of Things Network-Based on Deep Learning Model with Rule-Based Feature Selection
    Awotunde, Joseph Bamidele
    Chakraborty, Chinmay
    Adeniyi, Abidemi Emmanuel
    Wireless Communications and Mobile Computing, 2021, 2021
  • [29] Feature Subset Selection Hybrid Deep Belief Network Based Cybersecurity Intrusion Detection Model
    Alissa, Khalid A.
    Shaiba, Hadil
    Gaddah, Abdulbaset
    Yafoz, Ayman
    Alsini, Raed
    Alghushairy, Omar
    Aziz, Amira Sayed A.
    Al Duhayyim, Mesfer
    ELECTRONICS, 2022, 11 (19)
  • [30] Optimization of Network Intrusion Detection System Using Genetic Algorithm with Improved Feature Selection Technique
    Matel, Elmer C.
    Sison, Arid M.
    Medina, Ruji P.
    2019 IEEE 11TH INTERNATIONAL CONFERENCE ON HUMANOID, NANOTECHNOLOGY, INFORMATION TECHNOLOGY, COMMUNICATION AND CONTROL, ENVIRONMENT, AND MANAGEMENT (HNICEM), 2019,