Enhancing Cybersecurity Using Hybrid Machine Learning Techniques for Network Intrusion Detection

Abstract

Rapid advancements in digital communications and cloud computing have accelerated the risk of cyber-attacks as well as unauthorized access attempts. Traditional intrusion detection systems fail to detect the more sophisticated, rapidly evolving attacks that threaten informational systems. In addition, traditional IDS\'s have not adapted quickly enough to meet new threats and continue to produce a high number of false alarms which are often viewed as a nuisance. This study proposes an intrusion detection system framework built on hybrid machine learning techniques to improve the security of networks and detect cyber threats. Specifically, the proposed model uses various types of machine learning algorithms (e.g., supervised classification) and anomaly detection techniques to improve the efficiency and effectiveness of detecting intrusions on computer networks. In order to evaluate the performance of the intrusion detection system based on accuracy, precision, recall, F1 score, and false-positive rates, empirical testing will use publicly available datasets as benchmarks to validate the proposed IDS framework. Based on the evaluation, the hybrid-intrusion detection approach is shown to have a superior detection capability compared to conventional single model methods by providing increased adaptability, lower computational complexity, and improved security from rapidly changing networks. The proposed IDS framework will address the need for intelligent, scalable, and real-time solutions for securing modern communication infrastructures.

Introduction

This study presents a Hybrid Machine Learning-Based Intrusion Detection System (IDS) designed to improve cybersecurity in modern digital environments. With the rapid growth of technologies such as cloud computing, IoT, artificial intelligence, big data, and smart communication networks, cyber threats have become more sophisticated and difficult to detect using traditional security measures like firewalls and access controls. As a result, intelligent and adaptive intrusion detection systems are needed to identify malicious activities in real time.

Traditional IDS approaches are categorized into:

• Signature-based detection, which accurately identifies known attacks but cannot detect new or unknown threats.
• Anomaly-based detection, which can identify previously unseen attacks but often suffers from high false positive rates and greater computational complexity.

Machine learning (ML) and deep learning techniques have improved intrusion detection by automatically learning network traffic patterns and classifying cyberattacks. However, individual ML models face challenges such as data imbalance, feature redundancy, overfitting, scalability issues, and limited adaptability in diverse network environments. To address these limitations, the study proposes a hybrid machine learning framework that combines multiple intelligent techniques for more reliable intrusion detection.

Literature Review

Previous research demonstrates the effectiveness of machine learning and deep learning algorithms, including:

• Decision Trees
• Random Forests
• Support Vector Machines (SVM)
• Naïve Bayes
• Artificial Neural Networks (ANN)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
• Long Short-Term Memory (LSTM)

Studies show that ensemble and hybrid models generally outperform single-model approaches. Researchers have also highlighted challenges such as class imbalance, high-dimensional data processing, computational cost, and the detection of unknown or advanced attacks.

Proposed Framework

The proposed IDS framework consists of several stages:

1. Network Traffic Acquisition and Preprocessing
   • Collects traffic data from routers, servers, cloud systems, and connected devices.
   • Removes duplicate, corrupted, and noisy data.
   • Handles missing values and normalizes features.
   • Converts categorical attributes into numerical formats.
   • Balances datasets using oversampling and undersampling techniques.
2. Feature Extraction and Optimization
   • Extracts statistical, temporal, and behavioral traffic features.
   • Calculates metrics such as packet size, transmission rate, flow duration, and communication patterns.
   • Uses optimization methods like:
     • Principal Component Analysis (PCA)
     • Correlation-based filtering
     • Recursive Feature Elimination (RFE)
   • Reduces redundancy and computational complexity while improving detection accuracy.
3. Hybrid Machine Learning-Based Intrusion Detection
   • Combines supervised machine learning and deep learning methods.
   • Utilizes classifiers such as:
     • Random Forest (RF)
     • Support Vector Machine (SVM)
     • Gradient Boosting
   • Employs ensemble learning to improve prediction stability and classification performance.
   • Enhances detection of both known and unknown cyber threats.

Benefits of the Proposed System

• Higher detection accuracy.
• Lower false positive rates.
• Improved scalability and adaptability.
• Better handling of complex and high-dimensional network traffic.
• Real-time intrusion detection and threat prediction.
• Enhanced protection for cloud systems, IoT networks, smart cities, autonomous systems, and next-generation communication infrastructures.

Conclusion

The proposed framework integrated advanced preprocessing techniques, optimized feature extraction mechanisms, ensemble machine learning algorithms, and deep learning architectures including CNN and LSTM networks to achieve efficient and reliable intrusion detection. Experimental evaluation using benchmark datasets such as NSL-KDD, CICIDS2017, and UNSW-NB15 demonstrated that the proposed hybrid model achieved superior performance compared to traditional machine learning and standalone deep learning approaches. The framework obtained an overall detection accuracy of 97.3%, precision of 96.8%, recall of 96.1%, and F1-score of 96.4%, outperforming SVM, Random Forest, CNN, and LSTM-based intrusion detection models. The proposed framework also showed strong attack-wise performance, achieving F1-scores of 96.8% for DoS/DDoS attacks, 96.1% for probe attacks, 96.4% for brute-force attacks, 95.3% for R2L intrusions, and 94.9% for U2R attacks. Additionally, the computational efficiency analysis confirmed that the proposed model reduced inference time to 48 ms and maintained a compact model size of 28.6 MB, making it suitable for real-time cybersecurity applications. The robustness evaluation further demonstrated that the framework maintained stable performance under noisy traffic conditions, packet loss scenarios, and heterogeneous network environments. Therefore, the proposed hybrid intrusion detection framework provides an intelligent, scalable, and computationally efficient cybersecurity solution for modern communication infrastructures. In future work, the proposed framework can be extended by integrating federated learning, explainable artificial intelligence, blockchain-assisted threat intelligence, and quantum-resistant security mechanisms to improve privacy preservation, attack interpretability, and resilience against emerging cyber threats.

References

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Copyright

Copyright © 2026 Anup Raj, Sharad Kumar, Jagdeep Singh, Manoj Kumar, Sachin Kumar, Vikas Sharma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.