An Enhanced Intrusion Detection System Using Attention-Based Stacked Sparse Autoencoder Feature Extraction

Venkata Ramani Varanasi; Razia Shaik

doi:10.48084/etasr.11034

Authors

Venkata Ramani Varanasi Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India
Razia Shaik Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India

Volume: 15 | Issue: 4 | Pages: 24436-24441 | August 2025 | https://doi.org/10.48084/etasr.11034

Received: 19 March 2025 | Revised: 7 April 2025 | Accepted: 12 April 2025 | Online: 26 June 2025

Corresponding author: Venkata Ramani Varanasi

Abstract

Attention-based stacked sparse autoencoders (AB-SSAEs) are an innovative method for improving Intrusion Detection Systems (IDSs) through the extraction of important features in high-dimensional and heterogeneous data. The proposed AB-SSAE presents an innovative approach to optimizing feature extraction processes using attention mechanisms and a hierarchy of focused sparse autoencoders. The AB-SSAE architecture employs several layers of sparse autoencoders, which transform features through attention mechanisms at every level, improving precision for feature extraction. AB-SSAE employs adaptive denoising with median filtering as a preprocessing step. From the mined data, normal and intrusion attempts are efficiently classified using a Bidirectional Long-Short-Term Memory (Bi-LSTM) network. The proposed technique was compared with several existing approaches, and the results showed that it can differentiate between malicious and benign network traffic with an accuracy of over 0.98.

Keywords:

intrusion detection systems, attention-based stacked sparse autoencoder, median filtering, LSTM

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References

C. Yue, L. Wang, D. Wang, R. Duo, and X. Nie, "An Ensemble Intrusion Detection Method for Train Ethernet Consist Network Based on CNN and RNN," IEEE Access, vol. 9, pp. 59527–59539, 2021.

J. Ghasemi, J. Esmaily, and R. Moradinezhad, "Intrusion detection system using an optimized kernel extreme learning machine and efficient features," Sādhanā, vol. 45, no. 1, Dec. 2019, Art. no. 2.

M. A. Khan and J. Kim, "Toward Developing Efficient Conv-AE-Based Intrusion Detection System Using Heterogeneous Dataset," Electronics, vol. 9, no. 11, Nov. 2020, Art. no. 1771.

P. Rajesh Kanna and P. Santhi, "Unified Deep Learning approach for Efficient Intrusion Detection System using Integrated Spatial–Temporal Features," Knowledge-Based Systems, vol. 226, Aug. 2021, Art. no. 107132.

J. Yang, T. Li, G. Liang, W. He, and Y. Zhao, "A Simple Recurrent Unit Model Based Intrusion Detection System With DCGAN," IEEE Access, vol. 7, pp. 83286–83296, 2019.

T. T. H. Le, H. Kim, H. Kang, and H. Kim, "Classification and Explanation for Intrusion Detection System Based on Ensemble Trees and SHAP Method," Sensors, vol. 22, no. 3, Jan. 2022, Art. no. 1154.

K. H. Le, M. H. Nguyen, T. D. Tran, and N. D. Tran, "IMIDS: An Intelligent Intrusion Detection System against Cyber Threats in IoT," Electronics, vol. 11, no. 4, Jan. 2022, Art. no. 524.

V. Gowdhaman and R. Dhanapal, "An intrusion detection system for wireless sensor networks using deep neural network," Soft Computing, vol. 26, no. 23, pp. 13059–13067, Dec. 2022.

A. M. Aleesa, A. A. Mohammed, and N. M. Sahar, "Deep-intrusion detection system with enhanced UNSW-NB15 dataset based on deep learning techniques," Journal of Engineering Science and Technology, vol. 16, no. 1, pp. 711–727, 2021.

J. Gu and S. Lu, "An effective intrusion detection approach using SVM with naïve Bayes feature embedding," Computers & Security, vol. 103, Apr. 2021, Art. no. 102158.

N. V. Sharma and N. S. Yadav, "An optimal intrusion detection system using recursive feature elimination and ensemble of classifiers," Microprocessors and Microsystems, vol. 85, Sep. 2021, Art. no. 104293.

R. Panigrahi et al., "A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets," Mathematics, vol. 9, no. 7, Jan. 2021, Art. no. 751.

A. Al-Bakaa and B. Al-Musawi, "Improving the Performance of Intrusion Detection System through Finding the Most Effective Features," in 2021 International Congress of Advanced Technology and Engineering (ICOTEN), Taiz, Yemen, Jul. 2021, pp. 1–9.

S. Rawat, A. Srinivasan, V. Ravi, and U. Ghosh, "Intrusion detection systems using classical machine learning techniques vs integrated unsupervised feature learning and deep neural network," Internet Technology Letters, vol. 5, no. 1, 2022, Art. no. e232.

M. Ramaiah, V. Chandrasekaran, V. Ravi, and N. Kumar, "An intrusion detection system using optimized deep neural network architecture," Transactions on Emerging Telecommunications Technologies, vol. 32, no. 4, 2021, Art. no. e4221.

S. N. Mighan and M. Kahani, "A novel scalable intrusion detection system based on deep learning," International Journal of Information Security, vol. 20, no. 3, pp. 387–403, Jun. 2021.

F. Medjek, D. Tandjaoui, N. Djedjig, and I. Romdhani, "Fault-tolerant AI-driven Intrusion Detection System for the Internet of Things," International Journal of Critical Infrastructure Protection, vol. 34, Sep. 2021, Art. no. 100436.

K. N. Rao, K. V. Rao, and P. V. G. D. Prasad Reddy, "A hybrid Intrusion Detection System based on Sparse autoencoder and Deep Neural Network," Computer Communications, vol. 180, pp. 77–88, Dec. 2021.

Y. N. Kunang, S. Nurmaini, D. Stiawan, and B. Y. Suprapto, "Attack classification of an intrusion detection system using deep learning and hyperparameter optimization," Journal of Information Security and Applications, vol. 58, May 2021, Art. no. 102804.

S. K. Sahu, D. P. Mohapatra, J. K. Rout, K. S. Sahoo, Q. V. Pham, and N. N. Dao, "A LSTM-FCNN based multi-class intrusion detection using scalable framework," Computers and Electrical Engineering, vol. 99, Apr. 2022, Art. no. 107720.

A. U. H. Qureshi, H. Larijani, M. Yousefi, A. Adeel, and N. Mtetwa, "An Adversarial Approach for Intrusion Detection Systems Using Jacobian Saliency Map Attacks (JSMA) Algorithm," Computers, vol. 9, no. 3, Sep. 2020, Art. no. 58.

Y. Yang, K. Zheng, C. Wu, and Y. Yang, "Improving the Classification Effectiveness of Intrusion Detection by Using Improved Conditional Variational AutoEncoder and Deep Neural Network," Sensors, vol. 19, no. 11, Jan. 2019, Art. no. 2528.

S. Thakur, A. Chakraborty, R. De, N. Kumar, and R. Sarkar, "Intrusion detection in cyber-physical systems using a generic and domain specific deep autoencoder model," Computers & Electrical Engineering, vol. 91, May 2021, Art. no. 107044.

A. Basati and M. M. Faghih, "APAE: an IoT intrusion detection system using asymmetric parallel auto-encoder," Neural Computing and Applications, vol. 35, no. 7, pp. 4813–4833, Mar. 2023.

F. Laghrissi, S. Douzi, K. Douzi, and B. Hssina, "Intrusion detection systems using long short-term memory (LSTM)," Journal of Big Data, vol. 8, no. 1, May 2021, Art. no. 65.

"IDS 2018 Intrusion CSVs (CSE-CIC-IDS2018)." [Online]. Available: https://www.kaggle.com/datasets/solarmainframe/ids-intrusion-csv.

"UNSW_NB15." [Online]. Available: https://www.kaggle.com/datasets/mrwellsdavid/unsw-nb15.

"elifnurkarakoc/CICIDS2017." [Online]. Available: https://github.com/elifnurkarakoc/CICIDS2017.