PMID- 38067756
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231209
IS  - 1424-8220 (Electronic)
IS  - 1424-8220 (Linking)
VI  - 23
IP  - 23
DP  - 2023 Nov 24
TI  - Multi-Objective Seagull Optimization Algorithm with Deep Learning-Enabled 
      Vulnerability Detection for Secure Cloud Environments.
LID - 10.3390/s23239383 [doi]
LID - 9383
AB  - 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.
FAU - Aljebreen, Mohammed
AU  - Aljebreen M
AD  - Department of Computer Science, Community College, King Saud University, P.O. Box 
      28095, Riyadh 11437, Saudi Arabia.
FAU - Alohali, Manal Abdullah
AU  - Alohali MA
AD  - Department of Information Systems, College of Computer and Information Sciences, 
      Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi 
      Arabia.
FAU - Mahgoub, Hany
AU  - Mahgoub H
AD  - Department of Computer Science, College of Science & Art at Mahayil, King Khalid 
      University, Abha 61413, Saudi Arabia.
FAU - Aljameel, Sumayh S
AU  - Aljameel SS
AUID- ORCID: 0000-0001-8246-4658
AD  - SAUDI ARAMCO Cybersecurity Chair, Department of Computer Science, College of 
      Computer Science and Information Technology, Imam Abdulrahman Bin Faisal 
      University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
FAU - Alsumayt, Albandari
AU  - Alsumayt A
AUID- ORCID: 0000-0002-2137-260X
AD  - Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal 
      University, P.O. Box 1982, Dammam 31441, Saudi Arabia.
FAU - Sayed, Ahmed
AU  - Sayed A
AD  - Research Center, Future University in Egypt, New Cairo 11835, Egypt.
LA  - eng
GR  - PNURSP2023R330/Princess Nourah bint Abdulrahman University/
GR  - RSP2023R459/King Saud University/
PT  - Journal Article
DEP - 20231124
PL  - Switzerland
TA  - Sensors (Basel)
JT  - Sensors (Basel, Switzerland)
JID - 101204366
SB  - IM
PMC - PMC10708797
OTO - NOTNLM
OT  - cloud computing
OT  - deep learning
OT  - intrusion detection system
OT  - seagull optimization algorithm
OT  - sooty tern optimization algorithm
COIS- The authors declare without conflict of interest. This manuscript was written 
      with the contributions of all authors. All authors have approved the final 
      version of this manuscript.
EDAT- 2023/12/09 10:45
MHDA- 2023/12/09 10:46
PMCR- 2023/11/24
CRDT- 2023/12/09 01:06
PHST- 2023/08/08 00:00 [received]
PHST- 2023/11/08 00:00 [revised]
PHST- 2023/11/14 00:00 [accepted]
PHST- 2023/12/09 10:46 [medline]
PHST- 2023/12/09 10:45 [pubmed]
PHST- 2023/12/09 01:06 [entrez]
PHST- 2023/11/24 00:00 [pmc-release]
AID - s23239383 [pii]
AID - sensors-23-09383 [pii]
AID - 10.3390/s23239383 [doi]
PST - epublish
SO  - Sensors (Basel). 2023 Nov 24;23(23):9383. doi: 10.3390/s23239383.