一种基于混合哈里斯鹰与多层感知器的有效网络入侵检测方法
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron
AI总结：
本文提出HHO-MLP方法，利用哈里斯鹰优化算法优化多层感知器的权重与偏置，在KDD数据集上取得93.17%准确率，优于多种进化算法和集成学习方法。
摘要：
本文提出了一种入侵检测系统（IDS），该系统采用哈里斯鹰优化算法（HHO），通过优化偏置和权重参数来优化多层感知器学习。HHO-MLP旨在学习过程中选择最优参数，以最小化网络中的入侵检测误差。HHO-MLP已使用EvoloPy NN框架实现，这是一个开源Python工具，专门用于使用进化算法训练多层感知器。为了将HHO模型与当前可用的其他进化方法进行比较，使用KDD数据集计算了特异性、灵敏度、准确率以及均方误差（mse）和均方根误差（rmse）指标。实验表明，HHO-MLP方法能有效识别恶意模式。HHO-MLP已针对蝴蝶优化算法（BOA）、蚱蜢优化算法（GOA）和黑寡妇优化算法（BOW）等进化算法进行了测试，并通过随机森林（RF）和XG-Boost进行了验证。HHO-MLP表现出优越性能，取得了最高分数，准确率为93.17%，灵敏度为89.25%，特异性为95.41%。
英文摘要：
This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XG-Boost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
