Enhanced Convolution Neural Network with Optimized Pooling and Hyperparameter Tuning for Network Intrusion Detection

TL;DR

This paper introduces an Enhanced CNN model with optimized pooling and hyperparameter tuning that significantly improves network intrusion detection accuracy over existing methods, demonstrating its effectiveness on the KDDCUP'99 dataset.

Contribution

The paper presents a novel EnCNN architecture with optimized pooling and hyperparameters specifically designed for NIDS, outperforming traditional machine learning models.

Findings

EnCNN achieves 10% higher detection accuracy than state-of-the-art methods.

The approach effectively identifies various attack types in real-time.

Comprehensive data preprocessing and feature engineering enhance model performance.

Abstract

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.