A Comprehensive Review: Performance Analysis of Machine and Deep Learning Techniques for Intrusion Detection Using Synthetic Data
A Comprehensive Review: Performance Analysis of Machine and Deep Learning Techniques for Intrusion Detection Using Synthetic Data |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-4 |
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| Year of Publication : 2025 | ||
| Author : Neha, Abhishek Kajal |
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| DOI : 10.14445/22315381/IJETT-V73I4P128 | ||
How to Cite?
Neha, Abhishek Kajal, "A Comprehensive Review: Performance Analysis of Machine and Deep Learning Techniques for Intrusion Detection Using Synthetic Data," International Journal of Engineering Trends and Technology, vol. 73, no. 4, pp.341-367, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I4P128
Abstract
In current times, the security of information for critical infrastructures has become extremely crucial. The most common threat faced by CIIs is in the form of frequent network intrusions. This article provides a comprehensive analysis of various intrusion detection techniques and cybersecurity measures for critical infrastructures. The main aim is to present a comparative analysis of network performance in the presence of DDoS, DoS, malware, APT and Ransomware attacks while also analyzing solutions to mitigate these challenges in different sectors of critical infrastructures like healthcare, government, defense, energy, and other online platforms. The study evaluates the effectiveness of emerging ML and DL approaches such as DT, RF, SVM, CNN, LSTM, GRU, RNN, etc. The most widely used datasets, such as KDD-Cup99, NSL-KDD, CICIDS2017-18-19, BOT-IoT, and TON-IoT, were also analyzed for evaluating the efficiency proposed by researchers for safeguarding CIIs. The dataset analysis investigates the performance dependence against the features used in feature engineering, followed by feature selection and feature extraction techniques. This review study also provides an overview of NIDS, Anomaly, behavior-based detection and IPS. This article analyzes the recent papers and highlights the significance of thorough testing on large datasets and the need for real-time situation comparisons to understand the effectiveness of these methods in protecting IDS for CIIs. Most of the information in the article is extracted from reputed database depositories and research articles retrieved from 2005 to 2024. In the end, the various challenges and recommendations will be outlined to be helpful in future research directions. Moreover, these ML and DL techniques were implemented on a synthetic dataset against three cyber-attack types: DDoS, SQL Injection and ransomware. We observed that the accuracy of DL-based techniques improved with the increase in the number of data samples, getting 98.8 accuracy for CNN against a sample of 10 lakh instances, including 20 attributes.
Keywords
Machine Learning, Deep Learning, Critical Information Infrastructures, Publicly available datasets, Syntactic dataset, IDS.
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