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.

翻译：网络入侵检测系统（NIDS）对于保护计算机网络免受恶意活动（包括拒绝服务攻击、探测攻击、用户到根权限攻击以及远程到本地攻击）至关重要。若缺乏有效的NIDS，网络将极易遭受严重的安全漏洞和数据丢失。机器学习技术通过自动化威胁检测与提升准确率，为增强NIDS提供了前景广阔的方法。本研究提出一种用于NIDS的增强型卷积神经网络（EnCNN），并采用KDDCUP'99数据集评估其性能。我们的方法包括全面的数据预处理、探索性数据分析以及特征工程。我们将EnCNN与多种机器学习算法进行比较，包括逻辑回归、决策树、支持向量机以及集成方法（如随机森林、AdaBoost和投票集成）。结果表明，EnCNN显著提升了检测准确率，较现有最优方法实现了10%的显著提升。这证明了EnCNN在实时网络入侵检测中的有效性，为识别与缓解安全威胁、增强整体网络韧性提供了稳健的解决方案。