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相关实验视频

Updated: Jul 8, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

基于深度学习的HTTP TRACE在使用深光谱多层卷积神经网络的无线传感器网络中检测洪水.

S Tamilselvi¹, Chin-Shiuh Shieh², Mong-Fong Horng²

¹SRM Institute of Science and Technology, Kattankulathur, Chengalpattu, Tamil Nadu, 603203, India. tamilses3@srmist.edu.in.

Scientific reports

|March 10, 2026

概括

此摘要是机器生成的。

相关概念视频

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Applications of GIS: Disaster Management and Emergency Response

Geographic Information System (GIS) technology is essential for risk identification, action prioritization, and resource optimization in critical situations like flooding and earthquakes. By integrating spatial and demographic data, GIS provides a comprehensive framework for emergency response.GIS integrates data layers, like rainfall intensity, topography, elevation profiles, and river levels, to model high-risk flood zones. These layers assess areas susceptible to flooding based on their...

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本研究介绍了增强的深光谱多层卷积神经网络 (EDSMCNN),用于检测无线传感器网络 (WSN) 中的HTTP TRACE洪水攻击. EDSMCNN模型提高了CPU性能,减少了计算时间,改善了WSN对复杂威胁的安全性.

科学领域:

计算机科学计算机科学
网络安全网络安全
人工智能的人工智能

背景情况:

无线传感器网络 (WSN) 容易受到分布式拒绝服务 (DDoS) 攻击,特别是针对带宽和CPU处理的HTTP洪水攻击.
HTTP TRACE洪水攻击,这些攻击滥用HTTP TRACE方法与静态URL,降低WSN性能和暴露敏感数据.

研究的目的:

提出一个增强的深光谱多层卷积神经网络 (EDSMCNN),以改善在WSN中检测和减轻HTTP TRACE洪水攻击.
为了提高CPU性能,管理多个URL请求,并使用最大加权功能预测TRACE攻击流量.

主要方法:

数据预处理涉及使用基于格子服务速率访问值 (LSRAV) 的蜘蛛算法减少维度和选择特征.
在EDSMCNN模型中,使用SoftMax和物流激活功能 (SLAF) 来生成物流神经元以防止跟踪攻击.
功能选择考虑了URL,协议和IP地址等参数,社交蜘蛛比较了功能模式.

主要成果:

与标准卷积方法相比,EDSMCNN系统在检测HTTP TRACE泛滥攻击方面表现出高效率.
实验结果证实了CPU性能的改进和计算时间的减少,有效地减轻HTTP请求的流量.
该研究强调了WSN通过后门注入对TRACE攻击者的脆弱性,强调了需要采取强有力的安全措施的必要性.

关键词:

分布式拒绝服务 (DDoS)增强的深光谱多层卷积神经网络 (EDSMCNN)通过HTTP-TRACE的洪水攻击攻击.格子服务率访问值 (LURAV)软马克斯的物流激活功能 (SLAF)无线传感器网络 (WSN) 是指无线传感器网络.

相关实验视频

Last Updated: Jul 8, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

结论:

EDSMCNN模型提供了一个强大的解决方案,用于检测和减轻WSN中的HTTP TRACE洪水攻击.
拟议的方法通过提高性能和减少检测时间,显著提高了WSN的安全性.
有效的反制措施对于保护WSN的完整性和保密性,防止新出现的网络威胁至关重要.