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在IDSNet的草原狗优化算法的帮助下,在农业4.0中预测DDoS攻击

Ramesh Vatambeti¹, D Venkatesh², Gowtham Mamidisetti³

¹School of Computer Science and Engineering, VIT-AP University, Vijayawada, India. v2ramesh634@gmail.com.

Scientific reports

|September 16, 2023

概括

智能农业或农业物联网 (IoT) 面临安全风险. 本研究介绍了IDSNet,这是一种使用卷积神经网络和草原犬优化来对抗智能农业中的DDoS攻击的入侵检测系统.

科学领域:

农业技术农业技术
网络安全网络安全
人工智能的人工智能

背景情况:

智能农业,或农业物联网 (IoT),将先进技术与传统农业相结合.
农业4.0,工业4.0和智能农业的融合,在部署的物联网设备中存在重大安全漏洞.
像分布式拒绝服务 (DDoS) 这样的攻击可以破坏农业系统,导致无法访问和数据操纵.

研究的目的:

开发和评估专门为智能农业环境设计的入侵检测系统 (IDS).
在农业4.0.0中解决物联网设备带来的安全挑战.
提高智能农业系统对网络威胁,特别是DDoS攻击的弹性.

主要方法:

基于一维卷积神经网络 (IDSNet) 的IDS的实施.
使用草原狗优化 (PDO) 来微调 IDSNet 模型的训练参数.
使用两个最近的真实世界流量数据集进行性能评估,包括CIC-DDoS攻击.

主要成果:

拟议的IDSNet模型在智能农业环境中检测DDoS攻击方面表现出有效性.
使用PDO进行微调优化了IDSNet识别恶意网络流量的能力.
对比分析显示,与现有的检测方法相比,其性能具有竞争力或优越性.

结论:

IDSNet提供了一种强大的解决方案,用于检测智能农业中的DDoS攻击.
整合PDO可以提高入侵检测系统的准确性和效率.
确保农业物联网对农业4.0的可靠运行至关重要.

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