改进卷积网络+优化池化与超参，检测网络入侵准确率提升10%。
Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection
- 用优化池化和超参调优增强卷积网络结构
- 在KDDCUP'99数据集上比现有方法准确率高10%
- 适合需要实时入侵检测的网络安全系统
网络入侵检测系统（NIDS）对防范拒绝服务（DoS）、探测、用户提权（U2R）及远程到本地（R2L）攻击至关重要。本文提出一种增强型卷积神经网络（EnCNN），基于KDDCUP'99数据集进行评估。研究包含全面的数据预处理、探索性数据分析（EDA）与特征工程，对比了逻辑回归、决策树、支持向量机（SVM）及随机森林、AdaBoost、投票集成等多类机器学习算法。结果表明，EnCNN显著提升检测准确率，在真实场景中表现优异，相比现有最优方法提升10%，为实时入侵检测提供高效可靠方案，有效增强网络整体安全性与韧性。
原文摘要 · Abstract (English)
Network Intrusion Detection Systems (NIDS) are essential for protecting computer networks from malicious activities, including Denial of Service (DoS), Probing, User-to-Root (U2R), and Remote-to-Local (R2L) attacks. Without effective NIDS, networks are vulnerable to significant security breaches and data loss. Machine learning techniques provide a promising approach to enhance NIDS by automating threat detection and improving accuracy. In this research, we propose an Enhanced Convolutional Neural Network (EnCNN) for NIDS and evaluate its performance using the KDDCUP'99 dataset. Our methodology includes comprehensive data preprocessing, exploratory data analysis (EDA), and feature engineering. We compare EnCNN with various machine learning algorithms, including Logistic Regression, Decision Trees, Support Vector Machines (SVM), and ensemble methods like Random Forest, AdaBoost, and Voting Ensemble. The results show that EnCNN significantly improves detection accuracy, with a notable 10% increase over state-of-art approaches. This demonstrates the effectiveness of EnCNN in real-time network intrusion detection, offering a robust solution for identifying and mitigating security threats, and enhancing overall network resilience.
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