Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment
Malware detection in Internet of Things (IoT) cloud platforms is a crucial security system for securing data and devices' integrity, secrecy, and availability. IoT devices are linked to cloud-based services offering storage, calculating, and analytics abilities. However, these devices are also...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824012675 |
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| author | Moneerah Alotaibi Ghadah Aldehim Mashael Maashi Mashael M. Asiri Faheed A.F. Alrslani Sultan Refa Alotaibi Ayman Yafoz Raed Alsini |
| author_facet | Moneerah Alotaibi Ghadah Aldehim Mashael Maashi Mashael M. Asiri Faheed A.F. Alrslani Sultan Refa Alotaibi Ayman Yafoz Raed Alsini |
| author_sort | Moneerah Alotaibi |
| collection | DOAJ |
| description | Malware detection in Internet of Things (IoT) cloud platforms is a crucial security system for securing data and devices' integrity, secrecy, and availability. IoT devices are linked to cloud-based services offering storage, calculating, and analytics abilities. However, these devices are also exposed to malware attacks that could cause significant damage. Malware detection in IoT cloud platforms involves analyzing and identifying potential threats like Trojans, viruses, ransomware, and worms. It is done through several processes, including behavior-based detection, signature-based detection, and anomaly-based detection. The study proposes a Chaos Game Optimization with improved deep learning for Malware Detection (CGOIDL-MD) technique in the IoT cloud platform. The proposed CGOIDL-MD technique majorly concentrates on the automated detection and classification of malware in the IoT cloud framework. The CGOIDL-MD method applies the CGO-based feature subset selection (CGO-FSS) approach to select features. Besides, the stacked long short-term memory sequence-to-sequence autoencoder (SLSTM-SSAE) approach was exploited for malware classification and detection. Moreover, the arithmetic optimization algorithm (AOA) technique was exploited for the hyperparameter selection technique. The simulation outcomes of the CGOIDL-MD technique were tested on the malware dataset, and the outcome can be studied from different perspectives. The experimentation outcomes illustrate the betterment of the CGOIDL-MD technique under various measures. |
| format | Article |
| id | doaj-art-5f8298754ad04f81826379ea6b7300d6 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-5f8298754ad04f81826379ea6b7300d62025-01-29T05:00:12ZengElsevierAlexandria Engineering Journal1110-01682025-01-01112688700Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environmentMoneerah Alotaibi0Ghadah Aldehim1Mashael Maashi2Mashael M. Asiri3Faheed A.F. Alrslani4Sultan Refa Alotaibi5Ayman Yafoz6Raed Alsini7Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Saudi ArabiaDepartment of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of Software Engineering, College of Computer and Information Sciences, King Saud University, Po Box 103786, Riyadh 11543, Saudi ArabiaDepartment of Computer Science, Applied College at Mahayil, King Khalid University, Saudi ArabiaDepartment of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha, Saudi Arabia; Corresponding author.Department of Computer Science, College of Science and Humanities Dawadmi, Shaqra University, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaDepartment of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi ArabiaMalware detection in Internet of Things (IoT) cloud platforms is a crucial security system for securing data and devices' integrity, secrecy, and availability. IoT devices are linked to cloud-based services offering storage, calculating, and analytics abilities. However, these devices are also exposed to malware attacks that could cause significant damage. Malware detection in IoT cloud platforms involves analyzing and identifying potential threats like Trojans, viruses, ransomware, and worms. It is done through several processes, including behavior-based detection, signature-based detection, and anomaly-based detection. The study proposes a Chaos Game Optimization with improved deep learning for Malware Detection (CGOIDL-MD) technique in the IoT cloud platform. The proposed CGOIDL-MD technique majorly concentrates on the automated detection and classification of malware in the IoT cloud framework. The CGOIDL-MD method applies the CGO-based feature subset selection (CGO-FSS) approach to select features. Besides, the stacked long short-term memory sequence-to-sequence autoencoder (SLSTM-SSAE) approach was exploited for malware classification and detection. Moreover, the arithmetic optimization algorithm (AOA) technique was exploited for the hyperparameter selection technique. The simulation outcomes of the CGOIDL-MD technique were tested on the malware dataset, and the outcome can be studied from different perspectives. The experimentation outcomes illustrate the betterment of the CGOIDL-MD technique under various measures.http://www.sciencedirect.com/science/article/pii/S1110016824012675Malware detectionCloud environmentInternet of ThingsChaos game optimizerDeep learningSecurity |
| spellingShingle | Moneerah Alotaibi Ghadah Aldehim Mashael Maashi Mashael M. Asiri Faheed A.F. Alrslani Sultan Refa Alotaibi Ayman Yafoz Raed Alsini Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment Alexandria Engineering Journal Malware detection Cloud environment Internet of Things Chaos game optimizer Deep learning Security |
| title | Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment |
| title_full | Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment |
| title_fullStr | Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment |
| title_full_unstemmed | Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment |
| title_short | Chaos Game Optimization with stacked LSTM sequence to sequence autoencoder for malware detection in IoT cloud environment |
| title_sort | chaos game optimization with stacked lstm sequence to sequence autoencoder for malware detection in iot cloud environment |
| topic | Malware detection Cloud environment Internet of Things Chaos game optimizer Deep learning Security |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824012675 |
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