Integrating Ebola optimization search algorithm for enhanced deep learning-based ransomware detection in Internet of Things security

Ibrahim R. Alzahrani; Randa Allafi; Ibrahim R. Alzahrani; Randa Allafi

doi:10.3934/math.2024331

AIMS Mathematics

2024, Volume 9, Issue 3: 6784-6802. doi: 10.3934/math.2024331

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Integrating Ebola optimization search algorithm for enhanced deep learning-based ransomware detection in Internet of Things security

Ibrahim R. Alzahrani ¹,
Randa Allafi ^{2
,
,}

1.
Department of Computer Science and Engineering, College of Computer Science and Engineering, University of Hafr Al Batin, Al Jamiah, Hafar Al Batin 39524, Saudi Arabia
2.
Department of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar, Saudi Arabia

Received: 05 December 2023 Revised: 19 January 2024 Accepted: 23 January 2024 Published: 19 February 2024
MSC : 11Y40

With the widespread use of Internet, Internet of Things (IoT) devices have exponentially increased. These devices become vulnerable to malware attacks with the enormous amount of data on IoT devices; as a result, malware detection becomes a major problem in IoT devices. A reliable and effective mechanism is essential for malware detection. In recent years, research workers have developed various techniques for the complex detection of malware, but accurate detection continues to be a problem. Ransomware attacks pose major security risks to corporate and personal information and data. The owners of computer-based resources can be influenced by monetary losses, reputational damage, and privacy and verification violations due to successful assaults of ransomware. Therefore, there is a need to swiftly and accurately detect the ransomware. With this motivation, the study designs an Ebola optimization search algorithm for enhanced deep learning-based ransomware detection (EBSAEDL-RD) technique in IoT security. The purpose of the EBSAEDL-RD method is to recognize and classify the ransomware to achieve security in the IoT platform. To accomplish this, the EBSAEDL-RD technique employs min-max normalization to scale the input data into a useful format. Also, the EBSAEDL-RD technique makes use of the EBSA technique to select an optimum set of features. Meanwhile, the classification of ransomware takes place using the bidirectional gated recurrent unit (BiGRU) model. Then, the sparrow search algorithm (SSA) can be applied for optimum hyperparameter selection of the BiGRU model. The wide-ranging experiments of the EBSAEDL-RD approach are performed on benchmark data. The obtained results highlighted that the EBSAEDL-RD algorithm reaches better performance over other models on IoT security.
- Ransomware detection,
- Internet of Things,
- Sparrow Search Algorithm,
- deep learning,
- feature selection
Citation: Ibrahim R. Alzahrani, Randa Allafi. Integrating Ebola optimization search algorithm for enhanced deep learning-based ransomware detection in Internet of Things security[J]. AIMS Mathematics, 2024, 9(3): 6784-6802. doi: 10.3934/math.2024331

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Abstract

With the widespread use of Internet, Internet of Things (IoT) devices have exponentially increased. These devices become vulnerable to malware attacks with the enormous amount of data on IoT devices; as a result, malware detection becomes a major problem in IoT devices. A reliable and effective mechanism is essential for malware detection. In recent years, research workers have developed various techniques for the complex detection of malware, but accurate detection continues to be a problem. Ransomware attacks pose major security risks to corporate and personal information and data. The owners of computer-based resources can be influenced by monetary losses, reputational damage, and privacy and verification violations due to successful assaults of ransomware. Therefore, there is a need to swiftly and accurately detect the ransomware. With this motivation, the study designs an Ebola optimization search algorithm for enhanced deep learning-based ransomware detection (EBSAEDL-RD) technique in IoT security. The purpose of the EBSAEDL-RD method is to recognize and classify the ransomware to achieve security in the IoT platform. To accomplish this, the EBSAEDL-RD technique employs min-max normalization to scale the input data into a useful format. Also, the EBSAEDL-RD technique makes use of the EBSA technique to select an optimum set of features. Meanwhile, the classification of ransomware takes place using the bidirectional gated recurrent unit (BiGRU) model. Then, the sparrow search algorithm (SSA) can be applied for optimum hyperparameter selection of the BiGRU model. The wide-ranging experiments of the EBSAEDL-RD approach are performed on benchmark data. The obtained results highlighted that the EBSAEDL-RD algorithm reaches better performance over other models on IoT security.

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