Abstract
Network intrusion problem poses serious threat to all users of computer systems due to the increase in size of modern networks and the convolution of large network traffic data. The problem exceeds the weighty limits of conventional technique of intrusion prevention system. Therefore, new solutions for efficient and effective intrusion detection with low false alarm rate are required. This research proposes a new methodology for intrusion detection by combining wrapper feature selection approach based on a genetic algorithm with Synthetic Minority Over Sampling (SMOTE) and Resample techniques for the balancing of the class distribution. The two selected traffic datasets (KDDCUP99 and NSL-KDD) were subjected to hybrid preprocessing of filtering technique, where SMOTE and Resample were used to recognize the oversampling of the minority samples in a bid to constructively increase the prediction accuracy of the minority class under the assumption that the overall distribution is unchanged and the information loss of majority samples. Three different decision tree classifiers were used to compute the performance of the selected subset features. Remarkable and outstanding fair comparison with other state-of-the-art detection methods was achieved with performance accuracy of 99.9873% and 99.8457% on KDDCUP99 and NSL-KDD dataset respectively.
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References
Magán-Carrión, R., Urda, D., Díaz-Cano, I., & Dorronsoro, B. (2020). Towards a reliable comparison and evaluation of network intrusion detection systems based on machine learning approaches. Applied Sciences, 10(5), 1775.
Azeez, N. A., Bada, T. M., Misra, S., Adewumi, A., Van der Vyver, C., & Ahuja, R. (2020). Intrusion detection and prevention systems: An updated review. In Data management, analytics and innovation (pp. 685–696). Springer.
Hassan, A. A., Sheta, A. F., & Wahbi, T. M. (2017). Intrusion detection system using weka data mining tool. International Journal of Science and Research, 6, 2319–7064.
Durgesh, S., Rajeshwar, S., & Vikram, S. (2019). Performance evaluation of entropy based graph network intrusion detection. Journal of Advance Research in Dynamical and Control Systems, 11(02), 1–10.
Jabez, J., & Muthukumar, B. (2015). Intrusion detection system (IDS): Anomaly detection using outlier detection approach. Procedia Computer Science, 48, 338–346.
Kumar, G. T., & Ayyagari. (2020). Machine learning-based ensembles for intrusion detection systems—A review. The Journal of Supercomputing.
Kumar, G., Thakur, K., & Ayyagari, M. R. (2020). MLEsIDSs: Machine learning-based ensembles for intrusion detection systems—A review. The Journal of Supercomputing, 1–34.
Li, Y., Huang, G. Q., Wang, C. Z., & Li, Y. C. (2019). Analysis framework of network security situational awareness and comparison of implementation methods. EURASIP Journal on Wireless Communications and Networking, 2019(1), 205.
Aishwarya, C., Venkateswaran, N., Supriya, T., Sreekar, T., & Sreeja, V. (2020). Intrusion Detection System using KDD Cup 99 Dataset. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(4), 3169–3171.
Bashir, U., & Chachoo, M. (2017). Performance evaluation of j48 and bayes algorithms for intrusion detection system. International Journal of Network Security and Its Applications (IJNSA), 9(4).
Ganapathy, S., Kulothungan, K., Muthurajkumar, S., Vijayalakshmi, M., Yogesh, P., & Kannan, A. (2013). Intelligent feature selection and classification techniques for intrusion detection in networks: A survey. EURASIP Journal on Wireless Communications and Networking, 2013(1), 271.
Zhao, M., Kumar, A., Ristaniemi, T., & Chong, P. H. J. (2017). Machine-to-machine communication and research challenges: A survey. Wireless Personal Communications, 97(3), 3569–3585.
Zhou, L., Pan, S., Wang, J., & Vasilakos, A. V. (2017). Machine learning on big data: Opportunities and challenges. Neurocomputing, 237, 350–361.
Yu, S. (2016). Big privacy: Challenges and opportunities of privacy study in the age of big data. IEEE Access, 4, 2751–2763.
Olasehinde, O. O., Johnson, O. V., & Olayemi, O. C. (2020, March). Evaluation of selected meta learning algorithms for the prediction improvement of network intrusion detection system. In 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS) (pp. 1–7). IEEE.
Rajagopal, S., Kundapur, P. P., & Hareesha, K. S. (2020). A stacking ensemble for network intrusion detection using heterogeneous datasets. Security and Communication Networks.
Yuyang, Z., Guang, C., Shanqing, J., & Dai, M. (2019). An efficient intrusion detection system based feature selection and ensemble classifier. Journal of Latex Class Files, 14(8), 1–12. arXiv:1904.01352v2 [cs.CR] 19 Sep 2019.
Kabir, M. R., Onik, A. R., & Samad, T. (2017). A network intrusion detection framework based on Bayesian network using wrapper approach. International Journal of Computer Applications, 166(4), 13–17.
Wathq, A., & Ahmed, S. (2019). A comparative study for machine learning tools using WEKA and rapid miner with classifier algorithms random tree and random forest for network intrusion detection. International Journal of Innovative Science and Research Technology, 4(4), 749–752.
Ibrahim, L. M., Basheer, D. T., & Mahmod, M. S. (2013). A comparison study for intrusion database (KDD99, NSL-KDD) based on self-organization map (SOM) artificial neural network. Journal of Engineering Science and Technology, 8(1), 107–119.
Choudhary, S., & Kesswani, N. (2020). Analysis of KDD-Cup’99, NSL-KDD and UNSW-NB15 datasets using deep learning in IoT. Procedia Computer Science, 167, 1561–1573.
Dittman, D., Khoshgoftaar, T. M., Wald, R., & Napolitano, A. (2011, November). Random forest: A reliable tool for patient response prediction. In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW) (pp. 289–296). IEEE.
Kursa, M. B., & Rudnicki, W. R. (2011). The all relevant feature selection using random forest. arXiv preprint arXiv:1106.5112.
Cadenas, J. M., Garrido, M. C., & MartíNez, R. (2013). Feature subset selection filter–wrapper based on low quality data. Expert Systems with Applications, 40(16), 6241–6252.
Chahira, J. (2019). Model for improving performance of network intrusion detection based on machine learning techniques (Doctoral dissertation, Kabarak University).
Van Sang, H., Nam, N. H., & Nhan, N. D. (2016). A novel credit scoring prediction model based on Feature Selection approach and parallel random forest. Indian Journal of Science and Technology, 9(20), 1–6.
Venkatesh, B., & Anuradha, J. (2019). A hybrid feature selection approach for handling a high-dimensional data. In Innovations in Computer Science and Engineering (pp. 365–373). Springer.
Azeez, N. A., Ayemobola, T. J., Misra, S., Maskeliūnas, R., & Damaševičius, R. (2019). Network intrusion detection with a hashing based Apriori algorithm using Hadoop MapReduce. Computers, 8(4), 86.
Odusami, M., Misra, S., Adetiba, E., Abayomi-Alli, O., Damasevicius, R., & Ahuja, R. (2019, June). An improved model for alleviating layer seven distributed denial of service intrusion on webserver. Journal of Physics: Conference Series, 1235(1), 012020).
Sánchez-Hernández, F., Ballesteros-Herráez, J. C., Kraiem, M. S., Sánchez-Barba, M., & Moreno-García, M. N. (2019). Predictive Modeling of ICU healthcare-associated infections from imbalanced data. Using ensembles and a clustering-based undersampling approach. Applied Sciences, 9(24), 5287.
Neethu, B. (2012). Classification of intrusion detection dataset using machine learning approaches. International Journal of Electronics and Computer Science Engineering, 1(3), 1044–1051.
Saranya, T., Sridevi, S., Deisy, C., Chung, T. D., & Khan, M. A. (2020). Performance analysis of machine learning algorithms in intrusion detection system: A review. Procedia Computer Science, 171, 1251–1260.
Nguyen, G., Dlugolinsky, S., Bobák, M., Tran, V., García, Á. L., Heredia, I., et al. (2019). Machine learning and deep learning frameworks and libraries for large-scale data mining: A survey. Artificial Intelligence Review, 52(1), 77–124.
Pawlicki, M., Choraś, M., Kozik, R., & Hołubowicz, W. (2020, June). On the impact of network data balancing in cybersecurity applications. In International Conference on Computational Science (pp. 196–210). Springer.
Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20), 4396.
Yang, P., Liu, W., Zhou, B. B., Chawla, S., & Zomaya, A. Y. (2013, April). Ensemble-based wrapper methods for feature selection and class imbalance learning. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 544–555). Springer.
Acharya, N., & Singh, S. (2018). An IWD-based feature selection method for intrusion detection system. Soft Computing, 22(13), 4407–4416.
Karegowda, A. G., Jayaram, M. A., & Manjunath, A. S. (2010). Feature subset selection problem using wrapper approach in supervised learning. International Journal of Computer Applications, 1(7), 13–17.
Kubus, M. (2020). Evaluation of resampling methods in the class unbalance problem. Econometrics, 24(1), 39–50.
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
Gong, C., & Gu, L. (2016). A novel SMOTE-based classification approach to online data imbalance problem. Mathematical Problems in Engineering.
Khaldy, M. A., & Kambhampati, C. (2018). Resampling imbalanced class and the effectiveness of feature selection methods for heart failure dataset. International Robotics and Automation Journal, 4(1), 1–10.
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Awujoola, O.J., Ogwueleka, F.N., Irhebhude, M.E., Misra, S. (2021). Wrapper Based Approach for Network Intrusion Detection Model with Combination of Dual Filtering Technique of Resample and SMOTE. In: Misra, S., Kumar Tyagi, A. (eds) Artificial Intelligence for Cyber Security: Methods, Issues and Possible Horizons or Opportunities. Studies in Computational Intelligence, vol 972. Springer, Cham. https://doi.org/10.1007/978-3-030-72236-4_6
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