A NEW APPROACH TO IMPROVE CNN PERFORMANCE IN ANOMALY DETECTION FOR IOT NETWORKS BASED ON THE ALGORITHM ADABOOST

Details

Hits: 959

Volume 5 (2), December 2022, Pages 212-235

Zahra Jahangiri¹, Nasser Modiri¹ and Zahra Tayyebi Qasabeh²

¹Islamic Azad University, Zanjan, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

²Payame Noor University of Guilan, Guilan, Iran, This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

Since the increase in internet attacks brings much damage, it is essential to take care of the security of network activities. networks must use different security systems, such as intrusion detection systems, to deal with attacks. This research proposes a reliable approach for intrusion detection systems based on anomaly networks. The network traffic data sets are large and unbalanced, affecting intrusion detection systems' performance. The imbalance has caused the minority class to be incorrectly identified by conventional data mining algorithms. By ignoring the example of this class, we tried to increase the overall accuracy, while the correct example of the minority class protocols is also essential. In the proposed method, network penetration detection based on the combination of multi-dimensional features and homogeneous cumulative set learning was proposed, which has three stages: the first stage, based on the characteristics of the data, several original datasets of raw data or datasets criteria are extracted. Then, the original feature datasets are combined to form multiple comprehensive feature datasets. Finally, the same basic algorithm is used to train different comprehensive feature datasets for the multi-dimensional subspace of features.

An initial classifier is trained, and the predicted probabilities of all the basic classifiers are entered into a meta-module. In this research, an AdaBoost meta-algorithm has been used for unbalanced data according to a suitable design. Also, various single CNN models and multi-CNN fusion models have been proposed, implemented, and trained. This evaluation is done with the NSL-KDD dataset to solve some of the inherent problems of the KDD'99 dataset. Simulations were performed to evaluate the performance of the proposed model on the mentioned data sets. This proposed method's accuracy and detection rate obtained better results than other methods.

Keywords:

Intrusion Detection (anomaly), Internet of Things, CNN, and Adaboost Algorithm.

DOI: https://doi.org/10.32010/26166127.2022.5.2.212.235

Reference 

Aljumah, A. (2021). IoT-based intrusion detection system using convolution neural networks. PeerJ Computer Science, 7, e721.

Al-Qatf, M., Lasheng, Y., Al-Habib, M., & Al-Sabahi, K. (2018). Deep learning approach combining sparse autoencoder with SVM for network intrusion detection. Ieee Access, 6, 52843-52856.

Ashfaq, R. A. R., Wang, X. Z., et al. (2017). Fuzziness based semi-supervised learning approach for intrusion detection system. Information sciences, 378, 484-497.

Atul, D. J., Kamalraj, R., et al. (2021). A machine learning based IoT for providing an intrusion detection system for security. Microprocessors and Microsystems, 82, 103741.

Costa, K. A., Papa, J. P., et al. (2019). Internet of Things: A survey on machine learning-based intrusion detection approaches. Computer Networks, 151, 147-157.

Hajiheidari, S., Wakil, K., Badri, M., & Navimipour, N. J. (2019). Intrusion detection systems in the Internet of things: A comprehensive investigation. Computer Networks, 160, 165-191.

Ingre, B., & Yadav, A. (2015, January). Performance analysis of NSL-KDD dataset using ANN. In 2015 international conference on signal processing and communication engineering systems (pp. 92-96). IEEE.

Khraisat, A., & Alazab, A. (2021). A critical review of intrusion detection systems in the internet of things: techniques, deployment strategy, validation strategy, attacks, public datasets and challenges. Cybersecurity, 4(1), 1-27.

Kodyš, M., Lu, Z., Fok, K. W., & Thing, V. L. (2021, December). Intrusion Detection in Internet of Things using Convolutional Neural Networks. In 2021 18th International Conference on Privacy, Security and Trust (PST) (pp. 1-10). IEEE.

Li, Y., Xu, Y., et al. (2020). Robust detection for network intrusion of industrial IoT based on multi-CNN fusion. Measurement, 154, 107450.

Naseer, S., Saleem, Y., et al. (2018). Enhanced network anomaly detection based on deep neural networks. IEEE access, 6, 48231-48246.

Nimbalkar, P., & Kshirsagar, D. (2021). Feature selection for intrusion detection system in Internet-of-Things (IoT). ICT Express, 7(2), 177-181.

Rincy N, T., & Gupta, R. (2021). Design and development of an efficient network intrusion detection system using machine learning techniques. Wireless Communications and Mobile Computing, 2021.

Sicari, S., Rizzardi, A., Grieco, L. A., & Coen-Porisini, A. (2015). Security, privacy and trust in Internet of Things: The road ahead. Computer networks, 76, 146-164.

Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009, July). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE symposium on computational intelligence for security and defense applications (pp. 1-6). Ieee.

Yin, C., Zhu, Y., Fei, J., & He, X. (2017). A deep learning approach for intrusion detection using recurrent neural networks. Ieee Access, 5, 21954-21961.

Yousefi-Azar, M., Varadharajan, V., Hamey, L., & Tupakula, U. (2017, May). Autoencoder-based feature learning for cyber security applications. In 2017 International joint conference on neural networks (IJCNN) (pp. 3854-3861). IEEE.

Zarpelão, B. B., Miani, R. S., Kawakani, C. T., & de Alvarenga, S. C. (2017). A survey of intrusion detection in Internet of Things. Journal of Network and Computer Applications, 84, 25-37.