Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled Vulnerability Detection for Secure Cloud Environments

被引:2
作者
Aljebreen, Mohammed [1 ]
Alohali, Manal Abdullah [2 ]
Mahgoub, Hany [3 ]
Aljameel, Sumayh S. [4 ]
Alsumayt, Albandari [5 ]
Sayed, Ahmed [6 ]
机构
[1] King Saud Univ, Community Coll, Dept Comp Sci, POB 28095, Riyadh 11437, Saudi Arabia
[2] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[3] King Khalid Univ, Coll Sci & Art Mahayil, Dept Comp Sci, Abha 61413, Saudi Arabia
[4] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, SAUDI ARAMCO Cybersecur Chair, Dept Comp Sci, POB 1982, Dammam 31441, Saudi Arabia
[5] Imam Abdulrahman Bin Faisal Univ, Appl Coll, Dept Comp Sci, POB 1982, Dammam 31441, Saudi Arabia
[6] Future Univ Egypt, Res Ctr, New Cairo 11835, Egypt
关键词
cloud computing; deep learning; intrusion detection system; sooty tern optimization algorithm; seagull optimization algorithm;
D O I
10.3390/s23239383
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Cloud computing (CC) is an internet-enabled environment that provides computing services such as networking, databases, and servers to clients and organizations in a cost-effective manner. Despite the benefits rendered by CC, its security remains a prominent concern to overcome. An intrusion detection system (IDS) is generally used to detect both normal and anomalous behavior in networks. The design of IDS using a machine learning (ML) technique comprises a series of methods that can learn patterns from data and forecast the outcomes consequently. In this background, the current study designs a novel multi-objective seagull optimization algorithm with a deep learning-enabled vulnerability detection (MOSOA-DLVD) technique to secure the cloud platform. The MOSOA-DLVD technique uses the feature selection (FS) method and hyperparameter tuning strategy to identify the presence of vulnerabilities or attacks in the cloud infrastructure. Primarily, the FS method is implemented using the MOSOA technique. Furthermore, the MOSOA-DLVD technique uses a deep belief network (DBN) method for intrusion detection and its classification. In order to improve the detection outcomes of the DBN algorithm, the sooty tern optimization algorithm (STOA) is applied for the hyperparameter tuning process. The performance of the proposed MOSOA-DLVD system was validated with extensive simulations upon a benchmark IDS dataset. The improved intrusion detection results of the MOSOA-DLVD approach with a maximum accuracy of 99.34% establish the proficiency of the model compared with recent methods.
引用
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页数:18
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