Modeling of Improved Sine Cosine Algorithm with Optimal Deep Learning-Enabled Security Solution

被引:0
|
作者
Almuqren, Latifah [1 ]
Maray, Mohammed [2 ]
Aljameel, Sumayh S. [3 ]
Allafi, Randa [4 ]
Alneil, Amani A. [5 ]
机构
[1] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[2] King Khalid Univ, Coll Comp Sci, Dept Informat Syst, Abha 61471, Saudi Arabia
[3] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, Comp Sci Dept, SAUDI ARAMCO Cybersecur Chair, POB 1982, Dammam 31441, Saudi Arabia
[4] Northern Border Univ, Coll Sci & Arts, Dept Comp & Informat Technol, Ar Ar 91431, Saudi Arabia
[5] Prince Sattam Bin Abdulaziz Univ, Preparatory Year Deanship, Dept Comp & Self Dev, Al Kharj 16278, Saudi Arabia
关键词
cloud computing; security; feature selection; machine learning; artificial intelligence; INTRUSION-DETECTION SYSTEM; OPTIMIZATION; NETWORK;
D O I
10.3390/electronics12194130
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Artificial intelligence (AI) acts as a vital part of enhancing network security using intrusion detection and anomaly detection. These AI-driven approaches have become essential components of modern cybersecurity strategies. Conventional IDS is based on predefined signatures of known attacks. AI improves signature-based detection by automating the signature generation and reducing false positives through pattern recognition. It can automate threat detection and response, allowing for faster reaction times and reducing the burden on human analysts. With this motivation, this study introduces an Improved Sine Cosine Algorithm with a Deep Learning-Enabled Security Solution (ISCA-DLESS) technique. The presented ISCA-DLESS technique relies on metaheuristic-based feature selection (FS) and a hyperparameter tuning process. In the presented ISCA-DLESS technique, the FS technique using ISCA is applied. For the detection of anomalous activities or intrusions, the multiplicative long short-term memory (MLSTM) approach is used. For improving the anomaly detection rate of the MLSTM approach, the fruitfly optimization (FFO) algorithm can be utilized for the hyperparameter tuning process. The simulation value of the ISCA-DLESS approach was tested on a benchmark NSL-KDD database. The extensive comparative outcomes demonstrate the enhanced solution of the ISCA-DLESS system with other recent systems with a maximum accuracy of 99.69%.
引用
收藏
页数:16
相关论文
共 50 条
  • [1] Optimal power flow solution using a learning-based sine-cosine algorithm
    Mittal, Udit
    Nangia, Uma
    Jain, Narender Kumar
    Gupta, Saket
    JOURNAL OF SUPERCOMPUTING, 2024, 80 (11): : 15974 - 16012
  • [2] Optimal Allocation of STATCOM using Improved Sine Cosine Optimization Algorithm
    Singh, Pradeep
    Tiwari, Rajive
    2018 8TH IEEE INDIA INTERNATIONAL CONFERENCE ON POWER ELECTRONICS (IICPE), 2018,
  • [3] A Simplified Sine Cosine Algorithm for the Solution of Optimal Reactive Power Dispatch
    Gupta, Sushil Kumar
    Kar, Manoj Kumar
    Kumar, Lalit
    Kumar, Sanjay
    INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2022, 2022
  • [4] Improved sine cosine algorithm combined with optimal neighborhood and quadratic interpolation strategy
    Guo Wen-yan
    Yuan, Wang
    Fang, Dai
    Peng, Xu
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2020, 94
  • [5] Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique
    Jenkinson, George
    Abbasi, Muhammad Ali Babar
    Molaei, Amir Masoud
    Yurduseven, Okan
    Fusco, Vincent
    ELECTRONICS, 2023, 12 (16)
  • [6] An intelligent deep learning-enabled recommendation algorithm for teaching music students
    Tang, Changfei
    Zhang, Jun
    SOFT COMPUTING, 2022, 26 (20) : 10591 - 10598
  • [7] An intelligent deep learning-enabled recommendation algorithm for teaching music students
    Changfei Tang
    Jun Zhang
    Soft Computing, 2022, 26 : 10591 - 10598
  • [8] Handling the Class Imbalance Problem With an Improved Sine Cosine Algorithm for Optimal Instance Selection
    Moorthy, Rajalakshmi Shenbaga
    Selvaraj, Arikumar K.
    Prathiba, Sahaya Beni
    Yenduri, Gokul
    Mohanty, Sachi Nandan
    Ramesh, Janjhyam Venkata Naga
    IEEE ACCESS, 2024, 12 : 87131 - 87151
  • [9] An improved Sine Cosine Algorithm based on Levy flight
    Ning, Li
    Gang, Li
    Deng ZhongLiang
    NINTH INTERNATIONAL CONFERENCE ON DIGITAL IMAGE PROCESSING (ICDIP 2017), 2017, 10420
  • [10] An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction
    Zhang, Chu
    Ma, Huixin
    Hua, Lei
    Sun, Wei
    Nazir, Muhammad Shahzad
    Peng, Tian
    ENERGY, 2022, 254