Scholarlink

<p>Nowadays, Cloud Computing has attracted a lot of interest from both individual users and organization. However, cloud computing applications face certain security issues, such as data integrity, user privacy, and service availability. The only better solution to such issues is to identify and prevent cyber threats before they can cause seriously harm the cloud computing system. Several methods have been suggested so far to detect the attack in cloud computing (CC), but no one method attains satisfactory results. To overwhelm these drawbacks, a Cyber Security Attack Detection using Deep Kernel Machine Learning optimized with Chaotic Invasive Weed Optimization algorithm is proposed in this paper for Cloud Computing. Here, the data is amassed from CSE-CIC-IDS2018 and Bot-IoT datasets. Subsequently, a pre-processing step involving redundancy reduction and missing value replacement for uncertainty removal is achieved through the Developed Random Forest with Local Least Squares (DRFLLS) method. By utilizing Entropy-Kurtosis based feature selection approach, the pre-processing data is given to the feature selection to identify the optimal features. The selected features are supplied to the Deep Kernel Machine Learning classifier (DK-ML), which is optimized using the Chaotic Invasive Weed Optimization algorithm (Chaotic-IWOA) for classification. This classification process classifies the data as normal or anomalous categories with anomalies encompassing, like DoS, DDoS, Theft attacks, and normal attacks. The proposed technique is activated in MATLAB. The proposed technique achieves 24.88%, 17.98%, 45.65%, 35.95% better accuracy for CSE-CIC-IDS2018 dataset, and 23.93%, 13.94%, 32.94%, and 29.04% better accuracy for Bot-IoT dataset.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cyber-attack detection based on a deep chaotic invasive weed kernel optimized machine learning classifier in cloud computing

  • 期刊论文

  • 发表日期：

    2025年2月28日

查看全文
  • M. Indrasena Reddy,
  • A. P. Siva Kumar,
  • K. Subba Reddy

摘要

Nowadays, Cloud Computing has attracted a lot of interest from both individual users and organization. However, cloud computing applications face certain security issues, such as data integrity, user privacy, and service availability. The only better solution to such issues is to identify and prevent cyber threats before they can cause seriously harm the cloud computing system. Several methods have been suggested so far to detect the attack in cloud computing (CC), but no one method attains satisfactory results. To overwhelm these drawbacks, a Cyber Security Attack Detection using Deep Kernel Machine Learning optimized with Chaotic Invasive Weed Optimization algorithm is proposed in this paper for Cloud Computing. Here, the data is amassed from CSE-CIC-IDS2018 and Bot-IoT datasets. Subsequently, a pre-processing step involving redundancy reduction and missing value replacement for uncertainty removal is achieved through the Developed Random Forest with Local Least Squares (DRFLLS) method. By utilizing Entropy-Kurtosis based feature selection approach, the pre-processing data is given to the feature selection to identify the optimal features. The selected features are supplied to the Deep Kernel Machine Learning classifier (DK-ML), which is optimized using the Chaotic Invasive Weed Optimization algorithm (Chaotic-IWOA) for classification. This classification process classifies the data as normal or anomalous categories with anomalies encompassing, like DoS, DDoS, Theft attacks, and normal attacks. The proposed technique is activated in MATLAB. The proposed technique achieves 24.88%, 17.98%, 45.65%, 35.95% better accuracy for CSE-CIC-IDS2018 dataset, and 23.93%, 13.94%, 32.94%, and 29.04% better accuracy for Bot-IoT dataset.