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%.
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页数:16
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