{"title":"利用混沌调谐群算法优化云计算环境中基于深度学习的入侵检测","authors":"C. Jansi Sophia Mary, K. Mahalakshmi","doi":"10.3233/jifs-237900","DOIUrl":null,"url":null,"abstract":"Intrusion Detection (ID) in cloud environments is vital to maintain the safety and integrity of data and resources. However, the presence of class imbalance, where normal samples significantly outweigh intrusive instances, poses a challenge in constructing a potential ID system. Deep Learning (DL) methods, with their capability to automatically study complex patterns and features, present a promising solution in various ID tasks. Such methods can automatically learn intricate features and patterns from the input dataset, making them suitable for detecting anomalies and finding intrusions in cloud environments. Therefore, this study proposes a Class Imbalance Data Handling with an Optimal Deep Learning-Based Intrusion Detection System (CIDH-ODLIDS) in a cloud computing atmosphere. The CIDH-ODLIDS technique leverages optimal DL-based classification and addresses class imbalance. Primarily, the CIDH-ODLIDS technique preprocesses the input data using a Z-score normalization approach to ensure data quality and consistency. To handle class imbalance, the CIDH-ODLIDS technique employs oversampling techniques, particularly focused on synthetic minority oversampling techniques such as Adaptive Synthetic (ADASYN) sampling. ADASYN generates synthetic instances for the minority class depending on the available data instances, effectively balancing the class distribution and mitigating the impact of class imbalance. For the ID process, the CIDH-ODLIDS technique utilizes a Fuzzy Deep Neural Network (FDNN) model, and its tuning procedure is performed using the Chaotic Tunicate Swarm Algorithm (CTSA). CTSA is employed to choose the learning rate of the FDNN methods optimally. The experimental assessment of the CIDH-ODLIDS method is extensively conducted on three IDS datasets. The comprehensive comparison results confirm the superiority of the CIDH-ODLIDS algorithm over existing techniques.","PeriodicalId":509313,"journal":{"name":"Journal of Intelligent & Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing deep learning-based intrusion detection in cloud computing environment with chaotic tunicate swarm algorithm\",\"authors\":\"C. Jansi Sophia Mary, K. Mahalakshmi\",\"doi\":\"10.3233/jifs-237900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Intrusion Detection (ID) in cloud environments is vital to maintain the safety and integrity of data and resources. However, the presence of class imbalance, where normal samples significantly outweigh intrusive instances, poses a challenge in constructing a potential ID system. 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引用次数: 0
摘要
云环境中的入侵检测(ID)对于维护数据和资源的安全性和完整性至关重要。然而,由于存在类不平衡(正常样本明显多于入侵实例),这给构建潜在的 ID 系统带来了挑战。深度学习(DL)方法具有自动研究复杂模式和特征的能力,为各种 ID 任务提供了一种前景广阔的解决方案。这些方法可以自动学习输入数据集中的复杂特征和模式,因此适合在云环境中检测异常和查找入侵。因此,本研究提出了一种在云计算环境下基于优化深度学习的类失衡数据处理入侵检测系统(CIDH-ODLIDS)。CIDH-ODLIDS 技术利用基于深度学习的最优分类来解决类不平衡问题。首先,CIDH-ODLIDS 技术使用 Z 分数归一化方法对输入数据进行预处理,以确保数据质量和一致性。为了处理类不平衡问题,CIDH-ODLIDS 技术采用了超采样技术,尤其侧重于合成少数群体超采样技术,例如自适应合成(ADASYN)采样。ADASYN 会根据可用的数据实例生成少数群体的合成实例,从而有效平衡类别分布,减轻类别失衡的影响。在 ID 过程中,CIDH-ODLIDS 技术使用了模糊深度神经网络(FDNN)模型,其调整过程使用混沌调谐群算法(CTSA)进行。CTSA 用于优化选择 FDNN 方法的学习率。CIDH-ODLIDS 方法在三个 IDS 数据集上进行了广泛的实验评估。综合比较结果证实了 CIDH-ODLIDS 算法优于现有技术。
Optimizing deep learning-based intrusion detection in cloud computing environment with chaotic tunicate swarm algorithm
Intrusion Detection (ID) in cloud environments is vital to maintain the safety and integrity of data and resources. However, the presence of class imbalance, where normal samples significantly outweigh intrusive instances, poses a challenge in constructing a potential ID system. Deep Learning (DL) methods, with their capability to automatically study complex patterns and features, present a promising solution in various ID tasks. Such methods can automatically learn intricate features and patterns from the input dataset, making them suitable for detecting anomalies and finding intrusions in cloud environments. Therefore, this study proposes a Class Imbalance Data Handling with an Optimal Deep Learning-Based Intrusion Detection System (CIDH-ODLIDS) in a cloud computing atmosphere. The CIDH-ODLIDS technique leverages optimal DL-based classification and addresses class imbalance. Primarily, the CIDH-ODLIDS technique preprocesses the input data using a Z-score normalization approach to ensure data quality and consistency. To handle class imbalance, the CIDH-ODLIDS technique employs oversampling techniques, particularly focused on synthetic minority oversampling techniques such as Adaptive Synthetic (ADASYN) sampling. ADASYN generates synthetic instances for the minority class depending on the available data instances, effectively balancing the class distribution and mitigating the impact of class imbalance. For the ID process, the CIDH-ODLIDS technique utilizes a Fuzzy Deep Neural Network (FDNN) model, and its tuning procedure is performed using the Chaotic Tunicate Swarm Algorithm (CTSA). CTSA is employed to choose the learning rate of the FDNN methods optimally. The experimental assessment of the CIDH-ODLIDS method is extensively conducted on three IDS datasets. The comprehensive comparison results confirm the superiority of the CIDH-ODLIDS algorithm over existing techniques.