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Hybrid Intrusion Detection Method Based on Constraints Optimized SAE and Grid Search Based SVM-RBF on Cloud
The present era is facing lot of Security, Privacy, and Integrity issues because of tremendous development in communication technology, data storage devices, and computing advancements leading to unavoidable losses. As a result of the aforementioned technological revolutions day by day, many of the organizations or institutions started migrating to cloud environment. Because of this, security issues have increased coupled with the advent of new ways of penetration into networks. Unauthorized users and many professionals with malicious intent started exploiting the legitimate users through cyber-crimes. So, there is a need to implement a proper Intrusion Detection System with optimization procedures. This paper proposes a hybrid Intrusion Detection approach with a combination of Constraints Optimized Stacked Autoencoder (COSAE) for dimension reduction and grid search based SVM-RBF classifier (GSVM-RBF). The COSAE+GSVM-RBF model enhanced the performance using a two-fold. i) The SAE is optimized through regularization techniques with the adoption of weight and dropout constraints, ii) To enhance the performance of the SVM classifier with RBF for tuning the hyperparameters using grid search. Various experiments are conducted to validate this model with four activation functions Scaled Exponential Linear Unit (SELU), Rectified Linear Unit, softplus, and Exponential Linear Unit (ELU) for dimension reduction using COSAE. The improvements carried out in this paper result in exploding gradients and vanishing gradients avoids overfitting in large datasets, intrusion detection rate, gain in computational time, and 100% F-Measure in classifying minor class labels. The proposed approach is validated on the CICIDS2017 dataset. Further, a comparative analysis of the proposed approach with state-of-the-art approaches has been conducted. Based on the experimental results it is observed that the proposed approach outperforms the prevailing approaches.
Keywords
Cloud Computing, Intrusion Detection, Stacked Autoencoder, Support Vector Machine, Regularization Constraints.
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- Shawahna, A., Abu-Amara, M., Mahmoud, A. S., & Osais, Y. (2018). “EDoS-ADS: an enhanced mitigation technique against economic denial of sustainability (EDoS) attacks”. IEEE Transactions on Cloud Computing, 8(3), 790-804.
- Tulasi Bhavani, T., Rao, M.K., Reddy, A.M. (2020). “Network intrusion detection system using random forest and decision tree machine learning techniques”. Advances in Intelligent Systems and Computing, Issue-1045. pp. 637-643.
- Krishna Anne, V.P., Rajasekhara Rao, K. (2017). “Standards and analysis of intrusion detection-based system: A comparative study”. ponte, Volume-73 Issue-2, pp. 87-97.
- Aamir, M., & Zaidi, S. M. A. (2019). “Clustering based semi-supervised machine learning for DDoS attack classification”. Journal of King Saud University-Computer and Information Sciences.
- Jadhav, A.D., Pellakuri, V. (2019). “Performance analysis of machine learning techniques for intrusion detection system”." International conf. on Computing, Communication Control and Automation, ICCUBEA 2019.
- Abdulhammed, R., Faezipour, M., Abuzneid, A., & AbuMallouh, A. (2018). “Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic”. IEEE sensors letters, 3(1), 1-4.
- Zhang, Y. (2018). “Deep generative model for multi-class imbalanced learning”. M.S. thesis,Dept. Elect., Comput., Biomed. Eng., Univ. Rhode Island, South Kingston, RI, USA,2018.
- Chawla, N. V. (2009). “Data mining for imbalanced datasets: An overview”. In Data mining and knowledge discovery handbook (pp. 875-886). Springer, Boston, MA.
- Karatas, G., Demir, O., & Sahingoz, O. K. (2020). “Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset”. IEEE Access, 8, 32150-32162.
- Mohamed, S., Ejbali, R., & Zaied, M. (2019). “Denoising Autoencoder with Dropout based Network Anomaly Detection”. ICSEA 2019, 110.
- Bhardwaj, A., Mangat, V., & Vig, R. (2020). “Hyperband Tuned Deep Neural Network with Well Posed Stacked Sparse AutoEncoder for Detection of DDoS Attacks in Cloud”. IEEE Access, 8, 181916-181929.
- Andalib, A., & Vakili, V. T. (2020).” A Novel Dimension Reduction Scheme for Intrusion Detection Systems in IoT Environments”. arXiv preprint arXiv:2007.05922.
- KASIM, Ö. (2020).” An efficient and robust deep learning based network anomaly detection against distributed denial of service attacks”. Computer Networks, 180, 107390.
- Musafer, H., Abuzneid, A., Faezipour, M., & Mahmood, A. (2020). “An Enhanced Design of Sparse Autoencoder for Latent Features Extraction Based on Trigonometric Simplexes for Network Intrusion Detection Systems”. Electronics, 9(2), 259.
- Xiaopeng, C., & Hongyan, Q. “Deep feature Extraction Via Sparse Autoencoder for Intrusion Detection System”,2020.
- Elkhadir, Z., Chougdali, K., & Benattou, M. (2016). “Intrusion detection system using pca and kernel pca methods”. In Proceedings of the Mediterranean Conf. on Information & Communication Technologies 2015 (pp. 489-497). Springer, Cham.
- Ieracitano, C., Adeel, A., Gogate, M., Dashtipour, K., Morabito, F. C., Larijani, H., ... & Hussain, A. (2018, July). “Statistical analysis driven optimized deep learning system for intrusion detection”. In International Conf. on Brain Inspired Cognitive Systems (pp. 759-769). Springer, Cham.
- Yeom, S., Choi, C., & Kim, K.” AutoEncoder Based Feature Extraction for Multi-Malicious Traffic Classification”.2020.
- Ustebay, S., Turgut, Z., & Aydin, M. A. (2019, June). “Cyber Attack Detection by Using Neural Network Approaches: Shallow Neural Network, Deep Neural Network and AutoEncoder”. In International Conf. on Computer Networks (pp. 144-155). Springer, Cham.
- Mighan, S. N., & Kahani, M. (2020). “A novel scalable intrusion detection system based on deep learning”. International Journal of Information Security, 1-17.
- Mighan, S. N., & Kahani, M. (2018, May). “Deep learning based latent feature extraction for intrusion detection”. In Electrical Engineering (ICEE), Iranian Conf. (pp. 1511-1516). IEEE.
- Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). “Dropout: a simple way to prevent neural networks from overfitting”. The journal of machine learning research, 15(1), 1929-1958.
- Narisetty, N., Kancherla, G. R., Bobba, B., & Swathi, K.” Performance of Various SVM Kernels for Intrusion Detection of Cloud Environment”. IJETER, volume 8, No.10, October 2020.
- Vorontsov, E., Trabelsi, C., Kadoury, S., & Pal, C. (2017, July). “On orthogonality and learning recurrent networks with long term dependencies”. In International Conf. on Machine Learning (pp. 3570-3578). PMLR.
- Courtenay, L. A., Huguet, R., Gonzalez-Aguilera, D., & Yravedra, J. (2020). “A hybrid geometric morphometric deep learning approach for cut and trampling mark classification”. Applied Sciences, 10(1), 150.
- Livieris, I. E., Iliadis, L., & Pintelas, P. (2020). “On ensemble techniques of weight-constrained neural networks”. Evolving Systems, 1-13.
- Sadaf, K., & Sultana, J. (2020). “Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing”. IEEE Access, 8, 167059-167068.
- Alexander Pauls & Josiah A Yoder (2018). “Determining Optimum Drop-out Rate for Neural Networks”. http://micsymposium.org/mics2018/proceedings/MICS_2018_paper_27.pdf.
- Narisetty, N., Kancherla, G. R., Bobba, B., & Swathi, K. “Investigative Study of the Effect of Various Activation Functions with Stacked Autoencoder for Dimension Reduction of NIDS using SVM”. IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 5, 2021.
- Budiman, F. (2019). “SVM-RBF parameters testing optimization using cross validation and grid search to improve multiclass classification”. Научная визуализация, 11(1), 80-90.
- Biggio, B., Fumera, G., & Roli, F. (2010, April). “Multiple classifier systems under attack”. In International workshop on multiple classifier systems (pp. 74-83). Springer, Berlin, Heidelberg.
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