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Developing a multi-level intrusion detection system using hybrid-DBN

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Abstract

In this study, a hybrid deep belief network (DBN) cyber intrusion detection system was proposed to provide a secure network by controlling network traffic in Industrial control systems (ICS). The disadvantages of DBN have been analyzed and improved to create attack detectors in network traffic. The output is combined with Softmax Regression for effective intrusion detection and classification detection. Training and testing of the hybrid DBN model were carried out with the actual and original data set generated by ICS. DBNs are a much-preferred approach for detecting malicious attacks in network traffic. In instances where there is a lot of data, it is important to select the most appropriate structure for the DBN model. Therefore, in the model the hidden layers are updated by contrastive divergence (CD), and the output layer is combined with the Softmax classifier. The proposed model architecture has proved successful in many limitations, such as the complexity and size of training data. The proposed hybrid DBN model provided 99.72% accuracy in intrusion detection and classification. These results showed that the model achieved better performance than the existing intrusion detection system (IDS). It also provided approximately 5% more accuracy improvements with the hybrid model than with older DBN-based systems.

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References

  • Abraham A, Grosan C, Chen Y (2005) Cyber security and the evolution in intrusion detection systems. J Eng Tech Manage 1(1):74–82

  • Abusitta A, Bellaiche M, Dagenais M, Halabi T (2019) A deep learning approach for proactive multi-cloud cooperative intrusion detection system. Future Gener Comput Syst 98:308–318

    Article  Google Scholar 

  • Alom MZ, Bontupalli V, Taha TM (2015) Intrusion detection using deep belief networks. In: IEEE national aerospace and electronics conference (NAECON), pp 339–344

  • Balakrishnan N, Rajendran A, Pelusi D, Ponnusamy V (2019) Deep belief network enhanced intrusion detection system to prevent security breach in the Internet of Things. Internet Things. https://doi.org/10.1016/j.iot.2019.100112

  • Bengio Y, Lamblin P, Popovici D, Larochelle H (2007) Greedy layer-wise training of deep networks. Adv Neural Inf Process Syst 19(1):153

    Google Scholar 

  • Chen Y, Zhao X, Jia X (2015) Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE J Sel Top Appl Earth Obs Remote Sens 8(6):2381–2392

    Article  Google Scholar 

  • Dey D, Dinda A, Kundapur PP, Smitha R (2017) Warezmaster and Warezclient: an implementation of FTP based R2L attacks. In 2017 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT) pp 1–6. IEEE. https://doi.org/10.1016/j.future.2019.03.043

  • Dong B, Wang X (2016) Comparison deep learning method to traditional methods using for network intrusion detection. In: 2016 8th IEEE international conference on communication software and networks (ICCSN), pp 581–585

  • Erfani SM, Rajasegarar S, Karunasekera S, Leckie C (2016) High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning. Pattern Recognit 58:121–134

    Article  Google Scholar 

  • Farnaaz N, Jabbar MA (2016) Random forest modeling for network intrusion detection system. Procedia Comput Sci 89:213–217

    Article  Google Scholar 

  • Fiore U, Palmieri F, Castiglione A, De Santis A (2013) Network anomaly detection with the restricted Boltzmann machine. Neurocomputing 122:13–23

    Article  Google Scholar 

  • Gao N, Gao L, Gao Q, Wang H (2014) An intrusion detection model based on deep belief networks. In: 2014 second international conference on advanced cloud and big data, pp 247–252

  • Griffith S, Morris TH (2019) Using modeled cyber-physical systems for independent review of intrusion detection systems. In: National cyber summit, pp 116–125

  • Guoyin F (2018) Deep belief network based ensemble approach for cooling load forecasting of air-conditioning system. J Energy. https://doi.org/10.1016/j.energy.2018.01.180

    Article  Google Scholar 

  • Hee-su C, Choi SH (2014) Feature selection for efficient intrusion detection using attribute ratio. Int iktarJ Comput Commun 8:134–139

    Google Scholar 

  • Hinton GE (2002) Training products of experts by minimizing contrastive divergence. Neural Comput 14(8):1771–1800. https://doi.org/10.1162/089976602760128018

    Article  MATH  Google Scholar 

  • Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  MathSciNet  Google Scholar 

  • Huang Y, Tian K, Wu A, Zhang G (2019) Feature fusion methods research based on deep belief networks for speech emotion recognition under noise condition. J Ambient Intell Humaniz Comput 10(5):1787–1798

    Article  Google Scholar 

  • Javaid A, Niyaz Q, Sun W, Alam M (2016) A deep learning approach for network intrusion detection system. In: Proceedings of the 9th EAI international conference on bio-inspired information and communications technologies (formerly BIONETICS), pp 21–26

  • Jeya PG, Ravichandran M, Ravichandran CS (2012) Efficient classifier for R2L and U2R attacks. Int J Comput Appl 45(21):28–32

    Google Scholar 

  • Jiang M, Liang Y, Feng X, Fan X, Pei Z, Xue Y, Guan R (2018) Text classification based on deep belief network and softmax regression. Neural Comput Appl 29(1):61–70

    Article  Google Scholar 

  • Li Y, Ma R, Jiao R (2015) A hybrid malicious code detection method based on deep learning. Int J Secur Appl 9(5):205–216

    Google Scholar 

  • Li Y, Nie X, Huang R (2018) Web spam classification method based on deep belief networks. Expert Syst Appl 96:261–270

    Article  Google Scholar 

  • Li L, Qin L, Qu X, Zhang J, Wang Y, Ran B (2019a) Day-ahead traffic flow forecasting based on a deep belief network optimized by the multi-objective particle swarm algorithm. Knowl-Based Syst 172:1–14

    Article  Google Scholar 

  • Li Z, Cai X, Liu Y, Zhu B (2019b) A novel Gaussian-Bernoulli based convolutional deep belief networks for image feature extraction. Neural Process Lett 49(1):305–319

    Article  Google Scholar 

  • Liang D, Pan P (2019) Research on intrusion detection based on improved DBN-ELM. In: 2019 international conference on communications, information system and computer engineering (CISCE), pp 495–499

  • Ma M, Sun C, Chen X (2017) Discriminative deep belief networks with ant colony optimization for health status assessment of machine. IEEE Trans Instrum Meas 66(12):3115–3125

    Article  Google Scholar 

  • Mannepalli K, Sastry PN, Suman M (2017) Accent recognition system using deep belief networks for Telugu speech signals. In: Proceedings of the 5th international conference on frontiers in intelligent computing: theory and applications, pp 99–105

  • Mineraud J, Mazhelis O, Su X, Tarkoma S (2016) A gap analysis of internet-of-things platforms. Comput Commun 89:5–16

    Article  Google Scholar 

  • Morris TH, Gao W (2013) Industrial control system cyber attacks. In: Proceedings of the 1st international symposium on ICS & SCADA cyber security research, pp 22–29

  • Morris T, Gao W (2014) “Industrial control system network traffic data sets to facilitate intrusion detection system research. In: Sujeet Shenoi, Johnathan Butts (Eds) In Critical Infrastructure Protection VIII. ISBN: 978-3-662-45354-4

  • Movahedi F, Coyle JL, Sejdić E (2017) Deep belief networks for electroencephalography: a review of recent contributions and future outlooks. IEEE J Biomed Health Inf 22(3):642–652

    Article  Google Scholar 

  • Mukkamala S, Janoski G, Sung A (2002) Intrusion detection using neural networks and support vector machines. In: IEEE international joint conference on neural networks. IEEE Computer Society Press, pp 1702–1707

  • Naoum RS, Abid NA, Al-Sultani ZN (2012) An enhanced resilient backpropagation artificial neural network for intrusion detection system. Int J Comput Sci Netw Secur 12(3):11–16

    Google Scholar 

  • Othman SM, Alsohybe NT, Ba-Alwi FM, Zahary AT (2018) Survey on intrusion detection system types. Int J Cyber-Secur Digit Forensics 7(4):444–463

    Google Scholar 

  • Ouyang T, He Y, Li H, Sun Z, Baek S (2019) Modeling and forecasting short-term power load with copula model and deep belief network. IEEE Trans Emerg Top Comput Intell 3(2):127–136

    Article  Google Scholar 

  • Özekes S, Karakoç E (2019) Makine öğrenmesi yöntemleriyle anormal ağ trafiğinin tespit edilmesi. Düzce Üniv Bilim Teknol Derg 7(1):566–576. https://doi.org/10.29130/dubited.498358

    Article  Google Scholar 

  • Papamartzivanos D, Mármol FG, Kambourakis G (2019) Introducing deep learning self-adaptive misuse network intrusion detection systems. IEEE Access 7:13546–13560

    Article  Google Scholar 

  • Pitkänen A, Ndode-Ekane XE, Lapinlampi N, Puhakka N (2019) Epilepsy biomarkers–toward etiology and pathology specificity. Neurobiol Dis 123:42–58

    Article  Google Scholar 

  • Qu F, Zhang J, Shao Z, Qi S (2017) An intrusion detection model based on deep belief network. In: Proceedings of the 2017 VI international conference on network communication and computing, pp 97–101

  • Riley R, Jiang X, Xu D (2010) An architectural approach to preventing code injection attacks. IEEE Trans Dependable Secur Comput 7(4):351–365

    Article  Google Scholar 

  • Roy PP, Zhong G, Cheriet M (2017) Tandem hidden Markov models using deep belief networks for offline handwriting recognition. Front Inf Technol Electron Eng 18(7):978–988

    Article  Google Scholar 

  • Shone N, Ngoc TN, Phai VD, Shi Q (2018) A deep learning approach to network intrusion detection. IEEE Trans Emerg Top Comput Intell 2(1):41–50

    Article  Google Scholar 

  • Škrjanc I, Ozawa S, Ban T, Dovžan D (2018) Large-scale cyber-attacks monitoring using evolving cauchy possibilistic clustering. Appl Soft Comput 62:592–601

    Article  Google Scholar 

  • Su Z, Wassermann G (2006) The essence of command injection attacks in web applications. ACM Sigplan Not 41(1):372–382

  • Sultana N, Chilamkurti N, Peng W, Alhadad R (2019) Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Netw Appl 12(2):493–501. https://doi.org/10.1007/s12083-017-0630-0

    Article  Google Scholar 

  • Turner C, Jeremiah R, Richards D, Joseph A (2016) A rule status monitoring algorithm for rule-based intrusion detection and prevention systems. Procedia Comput Sci 95(1):361–368

    Article  Google Scholar 

  • Van NT, Thinh TN (2017) An anomaly-based network intrusion detection system using deep learning. In: 2017 international conference on system science and engineering (ICSSE), pp 210–214

  • Wei P, Li Y, Zhang Z, Hu T, Li Z, Liu D (2019) An optimization method for intrusion detection classification model based on deep belief network. IEEE Access 7:87593–87605

    Article  Google Scholar 

  • Yang Y, Zheng K, Wu C, Niu X, Yang Y (2019) Building an effective intrusion detection system using the modified density peak clustering algorithm and deep belief networks. Appl Sci 9(2):238–250

    Article  Google Scholar 

  • Yosinski J, Clune J, Bengio, Y, Lipson (2014) How transferable are features in deep neural networks? In: Advances in neural information processing systems, pp 3320–3328

  • Yu Y, Long J, Cai Z (2017) Network intrusion detection through stacking dilated convolutional autoencoders. Secur Commun Netw. https://doi.org/10.1155/2017/4184196

    Article  Google Scholar 

  • Zhang C, Tan KC, Li H, Hong GS (2018) A cost-sensitive deep belief network for imbalanced classification. IEEE Trans Neural Netw Learn Syst 30(1):109–122

    Article  Google Scholar 

  • Zhang J, Li F, Zhang H, Li R, Li Y (2019a) Intrusion detection system using deep learning for in-vehicle security. Ad Hoc Netw 95:101974. https://doi.org/10.1016/j.adhoc.2019.101974

    Article  Google Scholar 

  • Zhang Y, Li P, Wang X (2019b) Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access 7:31711–31722

    Article  Google Scholar 

  • Zhao G, Zhang C, Zheng L (2017) Intrusion detection using deep belief network and probabilistic neural network. In: 2017 IEEE international conference on computational science and engineering (CSE), pp 639–642

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Acknowledgements

I would like to thank the Cyber Security Application and Research Center of Isparta University of Applied Sciences for enabling the creation of the data set used in the study.

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  1. Cyber Security Application and Research Centre, Isparta University of Applied Sciences, 32050, Isparta, Turkey

    Ahmet Ali Süzen

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  1. Ahmet Ali Süzen
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Correspondence to Ahmet Ali Süzen.

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Süzen, A.A. Developing a multi-level intrusion detection system using hybrid-DBN. J Ambient Intell Human Comput 12, 1913–1923 (2021). https://doi.org/10.1007/s12652-020-02271-w

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