Comparative Study of Anomaly Detection Accuracy of Intrusion Detection Systems Based on Various Data Preprocessing Techniques


KIPS Transactions on Software and Data Engineering, Vol. 10, No. 11, pp. 449-456, Nov. 2021
https://doi.org/10.3745/KTSDE.2021.10.11.449,   PDF Download:
Keywords: Intrusion Detection, sliding window, Skip-gram, AAE, GRU
Abstract

An intrusion detection system is a technology that detects abnormal behaviors that violate security, and detects abnormal operations and prevents system attacks. Existing intrusion detection systems have been designed using statistical analysis or anomaly detection techniques for traffic patterns, but modern systems generate a variety of traffic different from existing systems due to rapidly growing technologies, so the existing methods have limitations. In order to overcome this limitation, study on intrusion detection methods applying various machine learning techniques is being actively conducted. In this study, a comparative study was conducted on data preprocessing techniques that can improve the accuracy of anomaly detection using NGIDS-DS (Next Generation IDS Database) generated by simulation equipment for traffic in various network environments. Padding and sliding window were used as data preprocessing, and an oversampling technique with Adversarial Auto-Encoder (AAE) was applied to solve the problem of imbalance between the normal data rate and the abnormal data rate. In addition, the performance improvement of detection accuracy was confirmed by using Skip-gram among the Word2Vec techniques that can extract feature vectors of preprocessed sequence data. PCA-SVM and GRU were used as models for comparative experiments, and the experimental results showed better performance when sliding window, skip-gram, AAE, and GRU were applied.


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Cite this article
[IEEE Style]
K. Park and K. Kim, "Comparative Study of Anomaly Detection Accuracy of Intrusion Detection Systems Based on Various Data Preprocessing Techniques," KIPS Transactions on Software and Data Engineering, vol. 10, no. 11, pp. 449-456, 2021. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.449.

[ACM Style]
Kyungseon Park and Kangseok Kim. 2021. Comparative Study of Anomaly Detection Accuracy of Intrusion Detection Systems Based on Various Data Preprocessing Techniques. KIPS Transactions on Software and Data Engineering, 10, 11, (2021), 449-456. DOI: https://doi.org/10.3745/KTSDE.2021.10.11.449.