[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content

Advertisement

Log in

DeNNeS: deep embedded neural network expert system for detecting cyber attacks

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

With the advances in computing powers and increasing volumes of data, deep learning’s emergence has helped revitalize artificial intelligence research. There is a growing trend of applying deep learning techniques to image processing, speech recognition, self-driving cars, and even health-care. Recently, several deep learning models have been employed to detect a cyber threat such as network attack, malware infiltration, or phishing website; nevertheless, they suffer from not being explainable to security experts. Security experts not only do need to detect the incoming threat but also need to know the incorporating features that cause that particular security incident. To address this issue, in this paper, we propose a deep embedded neural network expert system (DeNNeS) that extracts refined rules from a trained deep neural network (DNN) architecture to substitute the knowledge base of an expert system. The knowledge base later is used to classify an unseen security incident and inform the final user of the corresponding rule that made that inference. We consider different rule extraction scenarios, and to prove the robustness of DeNNeS, we evaluate it on two cybersecurity datasets including UCI phishing websites dataset and Android malware dataset comprising more than 4000 Android APKs from several sources. The comparison results of DeNNeS with standalone DNN, JRip, and common machine learning algorithms show that DeNNeS with the retraining uncovered samples scenario outperforms other algorithms on both datasets. Furthermore, the extracted rules approximately reproduce the accuracy of the neural network from which they are derived. DeNNeS achieves an outstanding accuracy of \(97.5\%\) and a negligible false positive rate of \(1.8\%\) about \(2.4\%\) higher and \(3.5\%\) lower than the rule learner JRip on the phishing dataset. Moreover, DeNNeS outperforms random forest (RF), which produces the highest results among decision tree (DT), support vector machine, k-nearest neighbor, and Gaussian naive Bayes. Despite smaller training data in the malware dataset, DeNNeS achieves an accuracy of \(95.8\%\) and \({F_{1}\,score}\) of \(91.1\%\), much higher than JRip and RF.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

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

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. (2018) Scikit-learn: machine learning in python. http://scikit-learn.org/stable/. Accessed Feb 2020

  2. (2018) Weka 3: data mining software in java. https://www.cs.waikato.ac.nz/ml/weka/. Accessed July 2019

  3. (2018) The onion ransomware (encryption trojan). https://www.kaspersky.co.in/resource-center/threats/onion-ransomware-virus-threat. Accessed Oct 2018

  4. Abadi M, Agarwal A, Barham P, KullBrevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M, et al. (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv:160304467

  5. Andrews R, Geva S (1995) Inserting and extracting knowledge from constrained error back propagation networks. In: Proceedings of the 6th Australian conference on neural networks, Sydney, NSW, Australia, pp 213–216

  6. Andrews R, Geva S (1995) Rule extraction from a constrained error back propagation mlp. In: Proceedings of the 5th Australian conference on neural networks, Brisbane, Queensland, Australia, pp 9–12

  7. Andrews R, Diederich J, Tickle AB (1995) Survey and critique of techniques for extracting rules from trained artificial neural networks. Knowl Based Syst 8(6):373–389. https://doi.org/10.1016/0950-7051(96)81920-4

    Article  MATH  Google Scholar 

  8. Augasta MG, Kathirvalavakumar T (2012) Reverse engineering the neural networks for rule extraction in classification problems. Neural Process Lett 35(2):131–150. https://doi.org/10.1007/s11063-011-9207-8

    Article  Google Scholar 

  9. Benítez JM, Castro JL, Requena I (1997) Are artificial neural networks black boxes? IEEE Trans Neural Netw 8(5):1156–1164. https://doi.org/10.1109/72.623216

    Article  Google Scholar 

  10. Biswas SK, Chakraborty M, Purkayastha B (2018) A rule generation algorithm from neural network using classified and misclassified data. Intl J Bio-Inspired Comput 11(1):60–70

    Article  Google Scholar 

  11. Borgolte K, Kruegel C, Vigna G (2015) Meerkat: detecting website defacements through image-based object recognition. In: Proceedings of the 2015 USENIX security symposium, pp 595–610

  12. Chakraborty M, Biswas SK, Purkayastha B (2018) Recursive rule extraction from nn using reverse engineering technique. New Gener Comput 36(2):119–142

    Article  Google Scholar 

  13. Chowdhury M, Rahman A, Islam R (2017) Malware analysis and detection using data mining and machine learning classification. In: Proceedings of the 2017 international conference on applications and techniques in cyber security and intelligence. Springer, pp 266–274. https://doi.org/10.1007/978-3-319-67071-3_33

  14. Cohen WW (1995) Fast effective rule induction. In: Machine learning proceedings 1995. Elsevier, pp 115–123

  15. Collobert R, Weston J (2008) A unified architecture for natural language processing: deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning. ACM, pp 160–167. https://doi.org/10.1145/1390156.1390177

  16. Craven MW, Shavlik JW (1994) Using sampling and queries to extract rules from trained neural networks. In: Proceedings of the 11th international conference on machine learning. Elsevier, pp 37–45. https://doi.org/10.1016/B978-1-55860-335-6.50013-1

  17. Dahl GE, Stokes JW, Deng L, Yu D (2013) Large-scale malware classification using random projections and neural networks. In: Proceedings of the 2013 international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 3422–3426. https://doi.org/10.1109/ICASSP.2013.6638293

  18. David OE, Netanyahu NS (2015) Deepsign: Deep learning for automatic malware signature generation and classification. In: Proceedings of the 2015 international joint conference on neural networks (IJCNN). IEEE, pp 1–8. https://doi.org/10.1109/IJCNN.2015.7280815

  19. De Paola A, Favaloro S, Gaglio S, Lo Re G, Morana M (2018) Malware detection through low-level features and stacked denoising autoencoders. In: Proceedings of the 2nd Italian conference on cyber security, ITASEC 2018, CEUR-WS, vol 2058

  20. Deng L (2014) A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Trans Signal Inf Process 3(2):1–29. https://doi.org/10.1017/atsip.2013.9

    Article  Google Scholar 

  21. Deng L, Yu D et al (2014) Deep learning: methods and applications. Found Trends Signal Process 7(3–4):197–387. https://doi.org/10.1561/2000000039

    Article  MathSciNet  MATH  Google Scholar 

  22. Ding Y, Chen S, Xu J (2016) Application of deep belief networks for opcode based malware detection. In: Proceedings of the 2016 international joint conference on neural networks (IJCNN). IEEE, pp 3901–3908. DOIurl10.1109/IJCNN.2016.7727705

  23. Duy PH, Diep NN (2017) Intrusion detection using deep neural network. Southeast Asian J Sci 5(2):111–125

    Google Scholar 

  24. Enck W, Ongtang M, McDaniel P (2009) Understanding android security. IEEE Secur Priv 1:50–57. https://doi.org/10.1109/MSP.2009.26

    Article  Google Scholar 

  25. Fu L (1991) Rule learning by searching on adapted nets. In: Proceedings of the 13th AAAI conference on artificial intelligence, vol 91, pp 590–595

  26. Fu L (1994) Rule generation from neural networks. IEEE Trans Syst Man Cybern 24(8):1114–1124. https://doi.org/10.1109/21.299696

    Article  Google Scholar 

  27. Gallant SI (1988) Connectionist expert systems. Commun ACM 31(2):152–169. https://doi.org/10.1109/ANNES.1993.323039

    Article  Google Scholar 

  28. Gallant SI (1988) Matrix controlled expert system producible from examples. US Patent 4,730,259

  29. Gallant SI (1995) Neural network learning and expert systems. A Bradford book, 3rd edn. MIT Press, Cambridge

    Google Scholar 

  30. Giles CL, Omlin CW (1993) Rule refinement with recurrent neural networks. In: Proceedings of the IEEE international conference on neural networks, pp 801–806. https://doi.org/10.1109/ICNN.1993.298658

  31. Guo W, Mu D, Xu J, Su P, Wang G, Xing X (2018) Lemna: explaining deep learning based security applications. In: Proceedings of the 2018 ACM SIGSAC conference on computer and communications security. ACM, pp 364–379. https://doi.org/10.1145/3243734.3243792

  32. Hayward R, Ho-Stuart C, Diederich J, Pop E (1996) RULENEG: extracting rules from a trained ann by stepwise negation. Technical report, Neurocomputing Research Centre, Queensland University Technology, Brisbane, Qld, Aust, QUT NRC

  33. Hinton G, Deng L, Yu D, Dahl GE, Ar M, Jaitly N, Senior A, Vanhoucke V, Nguyen P, Sainath TN et al (2012) Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 29(6):82–97. https://doi.org/10.1109/MSP.2012.2205597

    Article  Google Scholar 

  34. Hinton GE (2009) Deep belief networks. Scholarpedia 4(5):5947. https://doi.org/10.1145/1756006.1756025

    Article  Google Scholar 

  35. Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527

    Article  MathSciNet  MATH  Google Scholar 

  36. Hou S, Saas A, Chen L, Ye Y (2016) Deep4MalDroid: a deep learning framework for android malware detection based on Linux kernel system call graphs. In: Proceedings of the 2016 international conference on web intelligence workshops (WIW). IEEE, pp 104–111. https://doi.org/10.1109/WIW.2016.040

  37. Hou S, Saas A, Ye Y, Chen L (2016) Droiddelver: an android malware detection system using deep belief network based on API call blocks. In: Proceedings of the 2016 international conference on web-age information management. Springer, pp 54–66. https://doi.org/10.1007/978-3-319-47121-1_5

  38. Hsien-De Huang T, Kao HY (2018) R2-d2: color-inspired convolutional neural network (cnn)-based android malware detections. In: Proceedings of the 2018 IEEE international conference on big data (big data). IEEE, pp 2633–2642

  39. Huang W, Stokes JW (2016) MtNet: a multi-task neural network for dynamic malware classification. In: Detection of intrusions and malware, and vulnerability assessment. Springer, pp 399–418. https://doi.org/10.1007/978-3-319-40667-1_20

  40. Kadir AFA, Stakhanova N, Ghorbani AA (2015) Android botnets: what urls are telling us. In: International conference on network and system security. Springer, pp 78–91. https://doi.org/10.1007/978-3-319-25645-0_6

  41. Kadir AFA, Stakhanova N, Ghorbani AA (2016) An empirical analysis of android banking malware. In: Protecting mobile networks and devices: challenges and solutions, vol 209. CRC Press, Taylor & Francis

  42. Kahramanli H, Allahverdi N (2009) Rule extraction from trained adaptive neural networks using artificial immune systems. Expert Syst Appl 36(2):1513–1522. https://doi.org/10.1016/j.eswa.2007.11.024

    Article  Google Scholar 

  43. Karbab EB, Debbabi M, Derhab A, Mouheb D (2018) Maldozer: automatic framework for android malware detection using deep learning. Digit Invest 24:S48–S59. https://doi.org/10.1016/j.diin.2018.01.007

    Article  Google Scholar 

  44. Kim CH, Kabanga EK, Kang SJ (2018) Classifying malware using convolutional gated neural network. In: Proceedings of the 20th international conference on advanced communication technology (ICACT). IEEE, pp 40–44. https://doi.org/10.23919/ICACT.2018.8323639

  45. Kim J, Kim J, Thu HLT, Kim H (2016) Long short term memory recurrent neural network classifier for intrusion detection. In: Proceedings of the 2016 international conference on platform technology and service (PlatCon). IEEE, pp 1–5. https://doi.org/10.1109/PlatCon.2016.7456805

  46. Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv:14126980

  47. Kolosnjaji B, Zarras A, Webster G, Eckert C (2016) Deep learning for classification of malware system call sequences. In: Proceedings of the Australasian joint conference on artificial intelligence. Springer, pp 137–149. https://doi.org/10.1007/978-3-319-50127-7_11

  48. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the advances in neural information processing systems (NIPS), pp 1097–1105. https://doi.org/10.1145/3065386

  49. Kuang F, Xu W, Zhang S (2014) A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 18:178–184. https://doi.org/10.1016/j.asoc.2014.01.028

    Article  Google Scholar 

  50. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nat 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  51. Li Y, Shen T, Sun X, Pan X, Mao B (2015) Detection, classification and characterization of android malware using API data dependency. In: Proceedings of the 2015 international conference on security and privacy in communication systems. Springer, pp 23–40. https://doi.org/10.1007/978-3-319-28865-9_2

  52. Lin WC, Ke SW, Tsai CF (2015) CANN: an intrusion detection system based on combining cluster centers and nearest neighbors. Knowl Based Syst 78:13–21. https://doi.org/10.1016/j.knosys.2015.01.009

    Article  Google Scholar 

  53. Liu Y, Zhang X (2016) Intrusion detection based on IDBM. In: Proceedings of the 14th international conference on dependable, autonomic and secure computing, 14th international conference on pervasive intelligence and computing, 2nd international conference on big data intelligence and computing and cyber science and technology congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, pp 173–177. https://doi.org/10.1109/DASC-PICom-DataCom-CyberSciTec.2016.48

  54. Ma Z, Ge H, Liu Y, Zhao M, Ma J (2019) A combination method for android malware detection based on control flow graphs and machine learning algorithms. IEEE Access 7:21235–21245

    Article  Google Scholar 

  55. Mahdavifar S, Ghorbani AA (2019) Application of deep learning to cybersecurity: a survey. Neurocomputing 347:149–176

    Article  Google Scholar 

  56. Martín A, Fuentes-Hurtado F, Naranjo V, Camacho D (2017) Evolving deep neural networks architectures for android malware classification. In: Proceedings of the 2017 IEEE congress on evolutionary computation (CEC), IEEE, pp 1659–1666. https://doi.org/10.1109/CEC.2017.7969501

  57. McLaughlin N, Martinez del Rincon J, Kang B, Yerima S, Miller P, Sezer S, Safaei Y, Trickel E, Zhao Z, Doupé A, et al. (2017) Deep android malware detection. In: Proceedings of the seventh ACM on conference on data and application security and privacy. ACM, pp 301–308

  58. McMillan C, Mozer MC, Smolensky P (1991) The connectionist scientist game: rule extraction and refinement in a neural network. In: Proceedings of the 13th annual conference of the cognitive science society, pp 424–430

  59. Medsker L (1995) Expert systems and neural networks. In: Hybrid intelligent system. Springer US, chap 3. https://doi.org/10.1007/978-1-4615-2353-6_3

  60. Min S, Lee B, Yoon S (2017) Deep learning in bioinformatics. Brief Bioinform 18(5):851–869. https://doi.org/10.1093/bib/bbw068

    Article  Google Scholar 

  61. Mohammad RM, Thabtah F, McCluskey L (2012) An assessment of features related to phishing websites using an automated technique. In: 2012 international conference for internet technology and secured transactions. IEEE, pp 492–497

  62. Noda K, Yamaguchi Y, Nakadai K, Okuno HG, Ogata T (2015) Audio-visual speech recognition using deep learning. Appl Intell 42(4):722–737. https://doi.org/10.1007/s10489-014-0629-7

    Article  Google Scholar 

  63. Ota K, Dao MS, Mezaris V, De Natale FG (2017) Deep learning for mobile multimedia: a survey. ACM Trans Multimed Comput 13(3s):34:1–34:22. https://doi.org/10.1145/3092831

    Article  Google Scholar 

  64. Pascanu R, Stokes JW, Sanossian H, Marinescu M, Thomas A (2015) Malware classification with recurrent networks. In: Proceedings of the 2015 international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1916–1920. https://doi.org/10.1109/ICASSP.2015.7178304

  65. Rhode M, Burnap P, Jones K (2018) Early-stage malware prediction using recurrent neural networks. J Comput Secur 77:578–594. https://doi.org/10.1016/j.cose.2018.05.010

    Article  Google Scholar 

  66. Saito K, Nakano R (1988) Medical diagnostic expert system based on pdp model. Proc. IEEE Int. Conf. Neural Netw. 1:255–262. https://doi.org/10.1109/ICNN.1988.23855

    Article  Google Scholar 

  67. Sato M, Tsukimoto H (2001) Rule extraction from neural networks via decision tree induction. In: Proceedings of the 2001 international joint conference on neural networks. IEEE, vol 3, pp 1870–1875. https://doi.org/10.1109/IJCNN.2001.938448

  68. Segler MH, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural networks and symbolic ai. Nature 555(7698):604

    Article  Google Scholar 

  69. Sestito S (1991) The use of sub-symbolic methods for the automation of knowledge acquisition for expert systems. In: Proceedings of the 11th international conference on expert systems and their applications, 1991

  70. Sestito S (1992) Automated knowledge acquisition of rules with continuously valued attributes. In: Proceedings of the 12th international conference on expert systems and their applications, 1992

  71. Sethi KK, Mishra DK, Mishra B (2012) Kdruleex: a novel approach for enhancing user comprehensibility using rule extraction. In: Proceedings of the 3rd international conference on intelligent systems, modelling and simulation (ISMS). IEEE, pp 55–60. https://doi.org/10.1109/ISMS.2012.116

  72. Setiono R, Leow WK (2000) Fernn: an algorithm for fast extraction of rules from neural networks. Appl Intell 12(1–2):15–25. https://doi.org/10.1023/A:1008307919726

    Article  Google Scholar 

  73. Setiono R, Baesens B, Mues C (2008) Recursive neural network rule extraction for data with mixed attributes. IEEE Trans Neural Netw 19(2):299–307

    Article  Google Scholar 

  74. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556

  75. Socher R, Lin CC, Manning C, Ng AY (2011) Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 129–136

  76. Tam K, Khan SJ, Fattori A, Cavallaro L (2015) Copperdroid: automatic reconstruction of android malware behaviors. In: Network and distributed system security symposium (NDSS). https://doi.org/10.14722/ndss.2015.23145

  77. Thrun S (1993) Extracting provably correct rules from artificial neural networks. Citeseer

  78. Tickle AB, Orlowski M, Diederich J (1994) Dedec: decision detection by rule extraction from neural networks. QUT NRC

  79. Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA Neural Netw Mach Learn 4(2):26–31

    Google Scholar 

  80. Total V (2012) Virustotal-free online virus, malware and url scanner. https://www.virustotal.com/en

  81. Contagio Mobile (2016) Contagio mobile malware mini dump. http://contagiominidump.blogspot.com/

  82. Towell GG, Shavlik JW (1993) Extracting refined rules from knowledge-based neural networks. Mach Learn 13(1):71–101. https://doi.org/10.1023/A:1022683529158

    Article  Google Scholar 

  83. Tsukimoto H (2000) Extracting rules from trained neural networks. IEEE Trans Neural Netw 11(2):377–389. https://doi.org/10.1109/72.839008

    Article  Google Scholar 

  84. Wan J, Wang D, Hoi SCH, Wu P, Zhu J, Zhang Y, Li J (2014) Deep learning for content-based image retrieval: a comprehensive study. In: Proceedings of the 22nd ACM international conference on multimedia. ACM, pp 157–166. https://doi.org/10.1145/2647868.2654948

  85. Wang W, Zhao M, Wang J (2018) Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-018-0803-6

    Article  Google Scholar 

  86. Wang Y, Cai W, Pc W (2016) A deep learning approach for detecting malicious javascript code. Secur Commun Netw 9(11):1520–1534. https://doi.org/10.1002/sec.1441

    Article  Google Scholar 

  87. Wei F, Li Y, Roy S, Ou X, Zhou W (2017) Deep ground truth analysis of current android malware. In: International conference on detection of intrusions and malware, and vulnerability assessment (DIMVA’17). Springer, Bonn, pp 252–276. https://doi.org/10.1007/978-3-319-60876-1_12

  88. Weiss SM, Indurkhya N (1993) Optimized rule induction. IEEE Expert 8(6):61–69. https://doi.org/10.1109/64.248354

    Article  Google Scholar 

  89. Wu G, Lu W, Gao G, Zhao C, Liu J (2016) Regional deep learning model for visual tracking. Neurocomputing 175:310–323. https://doi.org/10.1016/j.neucom.2015.10.064

    Article  Google Scholar 

  90. Xu Z, Ray S, Subramanyan P, Malik S (2017) Malware detection using machine learning based analysis of virtual memory access patterns. In: Proceedings of the 2017 design, automation & test in europe conference & exhibition (DATE). IEEE, pp 169–174. https://doi.org/10.23919/DATE.2017.7926977

  91. Yen YS, Sun HM (2019) An android mutation malware detection based on deep learning using visualization of importance from codes. Microelectron Reliab 93:109–114

    Article  Google Scholar 

  92. Yin C, Zhu Y, Fei J, He X (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5:21954–21961. https://doi.org/10.1109/ACCESS.2017.2762418

    Article  Google Scholar 

  93. Yu D, Deng L (2011) Deep learning and its applications to signal and information processing [exploratory dsp]. IEEE Signal Process Mag 28(1):145–154. https://doi.org/10.1109/MSP.2010.939038

    Article  Google Scholar 

  94. Zilke JR, Mencía EL, Janssen F (2016) Deepred–rule extraction from deep neural networks. In: International conference on discovery science. Springer, pp 457–473. https://doi.org/10.1007/978-3-319-46307-0_29

  95. Zulkifli A, Hamid IRA, Shah WM, Abdullah Z (2018) Android malware detection based on network traffic using decision tree algorithm. In: Proceedings of the 2018 international conference on soft computing and data mining. Springer, pp 485–494. https://doi.org/10.1007/978-3-319-72550-5_46

Download references

Acknowledgements

The authors acknowledge the generous funding from the Atlantic Canada Opportunities Agency (ACOA) (Grant No: #201212) through the Atlantic Innovation Fund (AIF) and through a Grant from the Natural Sciences and Engineering Research Council of Canada (NSERC) (Grant No: #232074) to Dr. Ghorbani.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Samaneh Mahdavifar.

Ethics declarations

Conflict of interest

Author B has received research grants from Atlantic Canada Opportunities Agency (ACOA) (Grant No: 201212) through the Atlantic Innovation Fund (AIF) and from the Natural Sciences and Engineering Research Council of Canada (NSERC) (Grant No: 232074).

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

Mahdavifar, S., Ghorbani, A.A. DeNNeS: deep embedded neural network expert system for detecting cyber attacks. Neural Comput & Applic 32, 14753–14780 (2020). https://doi.org/10.1007/s00521-020-04830-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-04830-w

Keywords

Navigation