Abstract
Cyberattacks may occur in any device with an Internet connection. The majority of businesses either advise preventative measures or creating gadgets with integrated cyber threat protection mechanisms. However, the availability of tools and methods needs to go beyond standard preventative measures which make the process more difficult to identify cyber threats. One important tool for combating these intrusions is an intrusion detection system based on deep learning. To analyze intrusion detection systems, this study suggests random forest-based ensemble methods. Using random forest, tests were carried out in the first phase. In the subsequent stage, random forest is utilized due to their recent notable advancements in supervised learning performance. Deep learning methods like long short-term memory (LSTM) and autoencoder (AE) networks are used in the experiment. The work is optimized using Harris hawks optimization (HHO). For experimental purposes, the Kaggle dataset is utilized. Using this dataset, the results demonstrate that IDS have greatly improved, surpassing the state of the art. The applicability model in IDS is strengthened by this enhancement.
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References
Alrawashdeh X, Purdy C (2016) Toward an online anomaly intrusion detection system based on deep learning. In: Proceedings of 15th IEEE International Conference on Machine Learning and Applications, Anaheim, CA, USA, pp 195–200
Ashfaq RA, Wang XZ, Huang JZ, Abbas H, He YL (2017) Fuzziness based semi-supervised learning approach for intrusion detection system. Inf Sci 1(378):484–497
Beigh, Peer MA (2014) Performance evaluation of different intrusion detection system: an empirical approach. In: International Conference on Computer Communication and Informatics, pp 1–7
Buczak AL, Guven E (2016) A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun Surv Tutor 18(2):1153–1176
Cao J, Wu Z, Mao B, Zhang Y (2013) Shilling attack detection utilizing semi-supervised learning method for attack detection utilizing semi-supervised learning method for collaborative recommender system. World Wide Web J 16(5–6):729–748
Chang D, Li W, Yang Z (2017) Network intrusion detection based on random forest and support vector machine. In: Proceedings IEEE International Conference on Computational Science and Engineering/IEEE IEEE international Conference on Embedded and Ubiquitous Computing, pp 635–638
Farnaaz N, Jabbar MA (2016) Random forest modelling for network intrusion detection system. Proced Comput Sci 89:213–217
Gouveia A, Correia M (2017) A systematic approach for the application of restricted Boltzmann machines in network intrusion detection. IWANN 10305:05
Hodo A, Bellekens XJA, Hamilton A, Tachtatzis C, Atkinson RC (2017) Shallow and deep networks intrusion detection system: a taxonomy and survey, submitted to ACM survey. http://arxiv.org/abs/1701.02145
Ingre B, Yadav A (2015) Performance analysis of NSL-KDD dataset using ANN. In: 2015 International Conference on Signal Processing and Communication Engineering Systems, IEEE, pp 92–96
Javaid A, Niyaz Q, Sun W, Alam M (2015) A deep learning approach for network intrusion detection system. Proc Ninth EAI Int Conf Bio-Inspired Inf Commun Technol 35:2126
Khan JA, Jain N (2016) A survey on intrusion detection systems and classification techniques. Int J Sci Res Sci Eng Technol 2(5):202–208
Kim G, Yi H, Lee J, Paek Y, Yoon S (2016) Lstm-based system-call language modelling and robust ensemble method for designing host-based intrusion detection systems. arXiv preprint arXiv:1611.01726
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Li Y, Wang Y, Zhang J, Yang Y (2016) A deep learning-based RNNs model for automatic security audit of short messages. In: Proceedings of 16th International Symposium on Information and Communication Technology, Qingdao, China, pp 225–229
Moradi P, Ahmadian S (2015) A reliability-based recommendation method to improve trust-aware recommender systems. Expert Syst Appl 42:7386–7389
Otoum S, Burak K, Hussein TM (2018) Adaptively supervised and intrusion-aware data aggregation for wireless sensor clusters in critical infrastructures. In: 2018 IEEE International Conference on Communications (ICC), pp 1–6
Potluri, Diedrich C (2016) Accelerated deep neural networks for an enhanced intrusion detection system. In: Proceedings of IEEE 21st International Conference on Emergency Technology Factory Automation, Berlin, Germany, pp 1–8
Reddy RR, Ramadevi Y, Sunitha KN (2016) Effective discriminant function for intrusion detection using SVM. In: 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, pp 1148–1153
Tang C, Mhamdi D, McLernon D, Zaidi SAR, Ghogho M (2016) Deep learning approach for network intrusion detection in software-defined networking. In: Proceedings of International Conference on Wireless Network Mobile Communications (WINCOM), pp 258–263
Turk A, Bilge A (2019) Robustness analysis of multi-criteria collaborative filtering algorithms against shilling attacks. Expert Syst Appl 115:386–402
Vincent C, Larochelle H, Lajoie I, Bengio Y, Manzagol P-A (2010) Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J Mach Learn Res 11:3371–3408
Yang Z, Cai Z (2017) Detecting abnormal profiles in collaborative filtering recommender systems. J Intell Inf Syst 48(3):499–518
Yu H, Gao R, Wang K, Zhang F (2017) A novel robust recommendation method based on kernel matrix factorization. J Intell Fuzzy Syst 32(3):2101–2109
Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 15(115):213–237
Zhou W, Wen J, Koh Y, Xiong Q, Gao M, Dobbie G, Alam S (2015) Shilling attacks detection in recommender systems based on target item analysis. PLoS ONE 10(7):e0130968
Zhou W, Wen J, Qu Q, Zeng J, Cheng T (2018) Shilling attack detection for recommender systems based on credibility of group users and rating time series. PLoS ONE 13(5):e0196533
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Harita, U., Mohammed, M. Analyzing threat flow over network using ensemble-based dense network model. Soft Comput 28, 4171–4184 (2024). https://doi.org/10.1007/s00500-024-09645-8
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DOI: https://doi.org/10.1007/s00500-024-09645-8