检索结果-内蒙古大学图书馆

Enhanced Hybrid Approach for Multi-Class DDoS Attack Detection and Classification in Software-Defined Networks Using Remote Sensing and Data Analytics

引用

Remote Sensing in Earth Systems Sciences 2025年第2期8卷 530-544页

作者： Pradeesh, S. Jeyakarthic, M. Thirumalairaj, A. Department of Computer and Information Science Annamalai University Tamil Nadu Chidambaram India Department of Computer Science Kunthavai Naacchiyaar Govt Arts College for Women Tamil Nadu Ramani Nagar Thanjavur India

In the rapidly evolving landscape of Software-Defined Networks (SDNs), mitigating Distributed Denial of Service (DDoS) attacks presents significant security challenges. This paper introduces a sensor-enhanced hybrid approach for real-time multi-class detection and classification of DDoS attacks, incorporating remote sensing techniques to enhance data collection and analysis. The proposed framework integrates advanced network sensors and remote sensing technologies that continuously monitor traffic patterns and collect vital performance metrics across distributed environments, providing a comprehensive dataset for analysis. Utilizing an adaptive ensemble of modular classifiers (AEMC), the approach combines signature-based, anomaly-based, and behavior-based detection techniques, each tailored to identify specific attack vectors while adapting to evolving threats. The integration of sensor and remote sensing data enables adaptive feature selection and enhances the robustness of the ensemble learning process, allowing the system to dynamically adjust its response based on real-time network conditions. Central to this framework is the application of One-vs-Rest (OvR) classifiers, which effectively differentiate between various DDoS attack types, such as SYN flood, UDP flood, and HTTP flood, alongside normal traffic. Evaluation through real-world datasets and simulated scenarios demonstrates the framework’s efficacy, achieving high precision and recall rates in DDoS detection and classification. This research contributes to advancing network security in SDNs by providing a scalable, adaptive solution that enhances detection accuracy and enables proactive defense mechanisms against sophisticated DDoS threats. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： adaptive ensemble of modular classifiers DDoS attacks ensemble learning Multi-class detection Network security Sensors Software-Defined Networks

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：