Skip to main content
SpringerLink
Log in

Deep logarithmic neural network for Internet intrusion detection

  • Data analytics and machine learning
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Intrusion detection is an important means to deal with network attacks. The traditional neural network needs to preprocess the sample data in intrusion detection, which may cause the loss in data accuracy, thus affecting the performance of intrusion detection. In this paper, a logarithm neuron (LOGN) is designed, and each LOGN is responsible for processing a kind of data feature. Different from the traditional neuron operation rules, the logarithm operation is carried out in the LOGN, and the base number of logarithm operations in each LOGN can be determined by the neural network according to the actual task requirements. Because the increase and decrease of logarithm operation are different under different logarithmic base values, the sensitivity of LOGN to the change of sample data feature values is also different, which makes LOGN have higher capability in data feature extraction. Based on LOGN, the logarithmic neural network layer (LOGL) is designed. Logarithmic neural network (LOGNN) is constructed by combining LOGLs with traditional deep neural network. To prevent the gradient of LOGNN from disappearing, a loss function of anti-gradient vanishing (AGLF) is proposed. Experimental results show that LOGNN has obvious advantages over traditional machine learning and deep neural network methods in terms of performance indices on NSL_ KDD and UNSW_ NB_15 data sets.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Availability of data and material

All data generated or analyzed during this study are included in this article. The data in this article can be used freely after publication.

Code availability

Because the code content is not the main content of the article and, according to the theoretical description of the article, can easily reproduce the work of this article, so the code content is not included in this article. If necessary, we will release the code and other related content through relevant channels after the article is published.

References

  • Abdelkawy MA, Sabir Z, Guirao J et al (2020) Numerical investigations of a new singular second-order nonlinear coupled functional Lane-Emden model[J]. Open Phys 18(1):770–778

    Article  Google Scholar 

  • Adhi TB, Kyung-Hyune R (2017) HFSTE: hybrid feature selections and tree-based classifiers ensemble for intrusion detection system[J]. IEICE Trans Inf Syst 100(8):1729–1737

    Google Scholar 

  • Adil SH, Ali S, Raza K, et al (2019) An Improved Intrusion Detection Approach using Synthetic Minority Over-Sampling Technique and Deep Belief Network[J].

  • AI-Tashi Q, Abdulkadir SJ, Rais HM, Mirjalili S, Mlhussian H, Ragab MG, Alqushaibi A, (2020) Binary multi-objective grey wolf optimizer for feature selection in classification[J]. IEEE Access 8:106247–106263. https://doi.org/10.1109/ACCESS.2020.3000040

    Article  Google Scholar 

  • Bao-cai Y, Wen-tong W, Li-chun W (2015) Review of deep learning[J]. J Beijing Univ Technol 41(01):48–59

    Google Scholar 

  • Besharati E, Naderan M, Namjoo E (2019) LR-HIDS: logistic regression host-based intrusion detection system for cloud environments[J]. J Ambient Intell Human Comput. 10(9):3669–3692. https://doi.org/10.1007/s12652-018-1093-8

    Article  Google Scholar 

  • Beulah JR, Punithavathani DS (2017) A hybrid feature selection method for improved detection of wired/wireless network intrusions[J]. Wireless Pers Commun 98(2):1853–1869. https://doi.org/10.1007/s11277-017-4949-x

    Article  Google Scholar 

  • Blanco R, Malagón P, Briongos S et al (2019) Anomaly detection using Gaussian mixture probability model to implement intrusion detection system[J]. Springer, Cham

    Book  Google Scholar 

  • Canedo DRC, Romariz ARSR (2019) Intrusion detection system in Ad Hoc networks with artificial neural networks and algorithm K-means[J]. IEEE Lat Am Trans 17(7):1109–1115. https://doi.org/10.1109/TLA.2019.8931198

    Article  Google Scholar 

  • Chuan-Long Y, Yue-Fei Z, Jin-Long F et al (2017) A deep learning approach for intrusion detection using recurrent neural networks[J]. IEEE Access 5:21954–21961

    Article  Google Scholar 

  • D’Angelo G, Palmieri F (2021) Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial–temporal features extraction[J]. J Netw Comput Appl 173:102890

    Article  Google Scholar 

  • D’Angelo G, Palmieri F (2020) GGA: A modified Genetic Algorithm with Gradient-based Local Search for Solving Constrained Optimization Problems[J]. Inf Sci 547:136–162

    Article  MathSciNet  Google Scholar 

  • Deo RC (2015) Machine learning in medicine[J]. Circ 132(20):1920–1930

    Article  Google Scholar 

  • Duan LT, Han DZ, Tian QT (2019) Design of intrusion detection system based on improved ABC_elite and BP neural networks[J]. Comput Sci Inf Syst 16(3):773–795. https://doi.org/10.2298/CSIS181001026D

    Article  Google Scholar 

  • Ghamisi P, Benediktsson JA (2015) Feature selection based on hybridization of genetic algorithm and particle swarm optimization[J]. IEEE Geosci Remote Sens Lett 12(2):309–313. https://doi.org/10.1109/LGRS.2014.2337320

    Article  Google Scholar 

  • Hamed T, Dara R, Kremer SC (2018) network intrusion detection system based on recursive feature addition and bigram technique[J]. Comput Secur 73:137–155. https://doi.org/10.1016/j.cose.2017.10.011

    Article  Google Scholar 

  • Hassan MM, Gumaei A, Alsanad A et al (2019) A hybrid deep learning model for efficient intrusion detection in big data environment[J]. Inf Sci 513:386–396

    Article  Google Scholar 

  • Jaiswal S, Saxena K, Mishra A, Sahu SK (2016) A KNN-ACO approach for intrusion detection using KDDCUP'99 dataset. In: 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, pp 628–633

  • Jiang-xing WU, Jian-hua et al (2018) Security for cyberspace:challenges and opportunities[J]. Front Inf Tech Electron Eng 19(12):7–9

    Google Scholar 

  • Kim H, Hong H, Kim H-S, Kang S (2009) A memory-efficient parallel string matching for intrusion detection systems[J]. IEEE Commun Lett 13(12):1004–1006

    Article  Google Scholar 

  • Lam C, Siefkas A, Zelin NS , et al (2021) Using Machine Learning as a Precision Medicine Approach for Remdesivir and Corticosteroids as COVID-19 Pharmacotherapies[J]. Clinical Therapeutics, (5).

  • Langner R (2011) Stuxnet: dissecting a Cyberwarfare weapon[J]. IEEE Secur Priv Mag 9(3):49–51

    Article  Google Scholar 

  • Li-Wei H, Bi-Tao J, Shou-Ye L, Yan-Bo L, De-Yi L (2018) Survey on deep learning based recommender systems[J]. Ch J Comput 41(07):1619–1647

    MathSciNet  Google Scholar 

  • Moustafa N, Slay J (2015) UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set)[C]. In: Military Communications and Information Systems Conference (MilCIS). IEEE, 2015

  • Mulvey JM (2017) Machine Learning and Financial Planning. IEEE Potentials 36(6):8–13. https://doi.org/10.1109/MPOT.2017.2737200

    Article  Google Scholar 

  • Naren J, Ramalingam P, Raja RU, Vijayalakshmi P, Vithya G (2020) An intelligent system on computer-aided diagnosis for Parkinson’s Disease with MRI using machine learning. In: Dehuri S, Mishra B, Mallick P, Cho SB, Favorskaya M (eds) Biologically inspired techniques in many-criteria decision making. BITMDM 2019. Learning and Analytics in Intelligent Systems, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-39033-4_16

    Chapter  Google Scholar 

  • NSL-KDD dataset [Online], available: http://users.cis.fiu.edu/~1peng/Datasets.detail.html. Accessed 27 June 2019

  • Osada G, Omote K, Nishide T (2017) Network intrusion detection based on semi-supervised variational auto-encoder[C]. In: European Symposium on Research in Computer Security. Springer, Cham

  • Sabir Z et al (2020) Intelligence computing approach for solving second order system of Emden-Fowler Model. J Intell Fuzzy Syst 38(6):7391–7406

    Article  Google Scholar 

  • Sabir Z, Wahab HA, Umar M, Erdoğan F (2019) Stochastic numerical approach for solving second order nonlinear singular functional differential equation. Appl Math Comput 363:124605

    MathSciNet  MATH  Google Scholar 

  • Sabir Z, Guirao J, Saeed T (2021) Solving a novel designed second order nonlinear Lane-Emden delay differential model using the heuristic techniques[J]. Appl Soft Comput 102(3):107105

    Article  Google Scholar 

  • Sabir Z, Raja MAZ, Le DN et al (2021) A neuro-swarming intelligent heuristic for second-order nonlinear Lane–Emden multi-pantograph delay differential system[J]. Complex Intell Syst 1–14

  • Serpen G, Anghaei E (2018) Host-baesd misuse intrusion detection using PCA feature extraction and KNN classification Algorithms[J]. Intell Data Anal 22(5):1101–1114. https://doi.org/10.3233/IDA-173493

    Article  Google Scholar 

  • Shone N, Ngoc TN, Phai VD et al (2018a) A deep learning approach to network intrusion detection[J]. IEEE Trans Emerg Topics Comput Intell 2(1):41–50

    Article  Google Scholar 

  • Shone N, Ngoc TN, Phai VD, Shi Q (2018b) A deep learning approach to network intrusion detection[J]. IEEE Trans Emerg Topics Comput Intell 2(1):41–50. https://doi.org/10.1109/TETCI.2017.2772792

    Article  Google Scholar 

  • Szegedy C et al (2015) Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

  • Teng SH, Wu NQ, Zhu HB, Teng LY, Zhang W (2018a) SVM-DT-based adaptive and collaborative intrusion detection[J]. IEEE-CAA J Automatica Sinica 5(1):108–118. https://doi.org/10.1109/JAS.2017.7510730

    Article  Google Scholar 

  • Teng S, Mu N, Zhu H, Teng L, Zhang W (2018b) SVM-DT-based adaptive and collaborative intrusion detection[J]. IEEE/CAA J Automatica Sinica 5(1):108–118. https://doi.org/10.1109/JAS.2017.7510730

    Article  Google Scholar 

  • Usman AM, Yusof UK, Naim S (2020) Filter-based multi-objective feature selection using NSGA III and cuckoo optimization algorithm[J]. IEEE Access 8:76333–76356. https://doi.org/10.1109/ACCESS.2020.2987057

    Article  Google Scholar 

  • Van der Maaten L, Hinton G (2008) Visualizing data using t-SNE[J]. J Mach Learn Res 9(11):2579–2625. https://doi.org/10.1007/s10846-008-9235-4

    Article  MATH  Google Scholar 

  • Wang Z, Liu Y, Daojing HE et al (2021a) Intrusion detection methods based on integrated deep learning model[J]. Comput Secur 103:102177

    Article  Google Scholar 

  • Wang Z, Zeng Y, Liu Y et al (2021b) Deep belief network integrating improved Kernel-based extreme learning machine for network intrusion detection[J]. IEEE Access 9:16062

    Article  Google Scholar 

  • Wei B, Zhang W, Xia X, Zhang Y, Yu F, Zhu Z (2019) Efficient feature selection algorithm based on particle swarm optimization with learning memory[J]. IEEE Access 7:166066–166078. https://doi.org/10.1109/ACCESS.2019.2953298

    Article  Google Scholar 

  • Wikipedia. 2016 dyn cyberattack [EB/OL].[2017–05–09].https://en.wikipedia.Org/w/index.php?title=2016_Dyn_cyberattrack&oldid=763071700

  • Wu Z, Shen C, Hengel AVD (2019) Wider or deeper: revisiting the ResNet model for visual recognition[J]. Pattern Recogn 90:119–133. https://doi.org/10.1016/j.patcog.2019.01.006

    Article  Google Scholar 

  • Z. Ye, Y. Sun, S. Sun, S. Zhan, H. Yu, Q. Yao. (2019) Research on network intrusion detection based on support vector machine optimized with grasshopper optimization algorithm. In 2019 10th IEEE International Conference on Intelligent Data Acquistion and Advanced Computing Systems: Technology and Applications(IDAACS), Metz, France, pp 378–383. https://doi.org/10.1109/IDAACS.2019.8924234

  • Zarai R (2020) Recurrent neural networks and deep neural networks based on intrusion detection system[J]. Open Access Libr J 07(3):1–11

    MathSciNet  Google Scholar 

  • Zhang Y, Li P, Wang X (2019) Intrusion detection for IoT based on improved genetic algorithm and deep belief network. IEEE Access 7:31711–31722. https://doi.org/10.1109/ACCESS.2019.2903723

    Article  Google Scholar 

Download references

Funding

This work was supported by National Science Foundation of China and Natural Science Grant of Jiangxi Province. Grant numbers is (62062037, 61763017) and (20181BBE58018).

Author information

Authors and Affiliations

  1. School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou Jiangxi, China

    Zhendong Wang & Zhenyu Xu

  2. School of Software Engineering, East China Normal University, Shanghai, China

    Daojing He

  3. Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China

    Sammy Chan

Authors
  1. Zhendong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenyu Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daojing He
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sammy Chan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zhenyu Xu.

Ethics declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose. The authors have no conflicts of interest to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, Z., Xu, Z., He, D. et al. Deep logarithmic neural network for Internet intrusion detection. Soft Comput 25, 10129–10152 (2021). https://doi.org/10.1007/s00500-021-05987-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-021-05987-9

Keywords

Search

Navigation