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Cyber Security with AI—Part I

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The "Essence" of Network Security: An End-to-End Panorama

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 163))

Abstract

As a result of continuous and extreme inclusion of the Internet, computer networks, and social life, there has been a complete transformation of how people learn and work. With the expansion of the Internet and its application to our lives, it opens an abysmal for cyber security attacks. The continuous increase in cyberattacks has given rise to Artificial Intelligence (AI) and Machine Learning (ML)-based techniques that have a vital measurement in detecting security risks, security breaches and alerts, progress triage events, and malware detection to defense issues. {ML, AI} is the set of statistical and mathematical forms to clarify higher non-linearity troubles of dissimilar themes such as data organization, prediction, and classification. Moreover, it is an undeniable fact that information is an attractive reasonable presence for each corporation and big business. For that reason, protecting security models driven by the real data sets logically turns out to be important. Hence, this chapter presents the role of ML and AI in cyber security, describes a variety of active ML techniques, how and where to add ML and AI models for network security, cyber security threats classification. This chapter presents commonly used ML techniques and network data sets. Finally, challenges and future works are discussed.

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References

  1. Torres M, Comesaña JI, Carla G-N, Paulino J (2019) Review: machine learning techniques applied to cybersecurity. Int J Mach Learn Cybern 10(10):2823–2836

    Google Scholar 

  2. Handa AS, Shukla A, Sandeep K (2019) Machine learning in cybersecurity: a review. Wiley Interdiscip Rev Data Mining Knowl Discov 9(4):1–7

    Article  Google Scholar 

  3. Das RM, Thomas H (2018) Machine learning and cyber security. In: 2017 international conference on computer, electrical and communication engineering, ICCECE 2017, pp 1–7

    Google Scholar 

  4. Fraley JB, Cannady J (2017) The promise of machine learning in cybersecurity. In: Conference proceedings—IEEE southeastcon

    Google Scholar 

  5. Xin Y, Kong L, Liu Z, Chen Y (2018) Machine learning and deep learning methods for cybersecurity. IEEE Access 35365–35381

    Google Scholar 

  6. Vljqlilfdqw S, Iru S, Frpsdqlhv DOO (2017) Cyber security data sets. In: 2017 IEEE international conference on big data (BIGDATA)

    Google Scholar 

  7. Liu W, Wang Z, Liu X (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234(10):11–26

    Google Scholar 

  8. Kwon D, Kim H. A survey of deep learning-based network anomaly detection. Clust Comput 6(22):949–961

    Google Scholar 

  9. Vapnik V (1982) Estimation of dependences based on empirical data. Springer, Berlin

    Google Scholar 

  10. Drucker H, Burges C, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. MIT Press, Cambridge

    Google Scholar 

  11. Osuna E, Freund R, Girosi F (1997) An improved training algorithm for support vector machines, In: Proceedings of the 1997 IEEE signal processing society workshop, Amelia Island, Florida, USA, pp 1–10

    Google Scholar 

  12. Chandrasekhar AM, Raghuveer K (2014) Confederation of FCM clustering, ANN and SVM techniques to implement hybrid NIDS using corrected KDD cup 99 dataset. In: International conference on communications and signal processing, pp 672–676

    Google Scholar 

  13. Yan M, Liu Z (2017) A new method of transductive SVM-based network intrusion detection, in computer and computing technologies in agriculture IV. In: IFIPTC 12 conference, CCTA 2010, Nanchang, China, October 22–25, 2010, Selected Papers, pp 87–95

    Google Scholar 

  14. Kokila RT, Selvi ST, Govindarajan K (2015) DDoS detection and analysis in SDN-based environment using support vector machine classifier. In: Sixth international conference on advanced computing, pp 205–210

    Google Scholar 

  15. Peng XU, Jiang F (2014) Network intrusion detection model based on particle swarm optimization and k-nearest neighbor. Comput Eng Appl

    Google Scholar 

  16. Rai K, Syamala M, Devi, Guleria A (2016) Decision tree based algorithm for intrusion detection. 07(4):2828–2834

    Google Scholar 

  17. Azad C, Jha VK (2015) Genetic algorithm to solve the problem of small disjunction the decision tree based intrusion detection system. 7(8):56–71

    Google Scholar 

  18. Puthran S, Shah K (2016) Intrusion detection using improved decision tree algorithm with binary and quad split. In: International symposium on security in computing and communication, pp 427–438

    Google Scholar 

  19. Vishwakarma S, Sharma V, Tiwari A (2017) An intrusion detection system using KNN-ACO algorithm. Int J Comput Appl 171(10):18–23

    Google Scholar 

  20. Ingre B, Yadav A, Soni AK (2017) Decision tree based intrusion detection system for NSL-KDD dataset. In: International conference on information and communication technology for intelligent systems, pp 207–218

    Google Scholar 

  21. Malik AJ, Khan FA (2017) A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection. Clust Comput 3:1–14

    Google Scholar 

  22. Relan NG, Patil DR (2015) Implementation of network intrusion detection system using variant of decision tree algorithm. In: International conference on nascent technologies in the engineering field, pp 1–8

    Google Scholar 

  23. Abraham A, Grosan C, Martin-Vide C (2007) Evolutionary design of intrusion detection programs. Int J Netw Secur 4(3):328–339

    Google Scholar 

  24. Joshi SS, Phoha VV (2005) Investigating hidden Markov models capabilities in anomaly detection. In: Proceedings of the 43rd annual southeast regional conference, vol 1. ACM, pp 98–103

    Google Scholar 

  25. Fan W, Miller M, Stolfo S, Lee W, Chan P (2004) Using artificial anomalies to detect unknown and known network intrusions. Knowl Inf Syst 6(5):507–527

    Google Scholar 

  26. Staudemeyer RC (2015) Applying long short-term memory recurrent neural networks to intrusion detection. 56(1):136–154

    Google Scholar 

  27. Kim G, Yi H, Lee J, Paek Y, Yoon Y (2016) LSTM-based system-call language modeling and robust ensemble method for designing host-based intrusion detection systems. arXiv:1611.01726

  28. Le TTH, Kim J, Kim H (2017) An effective intrusion detection classifier using long short-term memory with gradient descent optimization. In: International conference on platform technology and service, pp 1–6

    Google Scholar 

  29. Zhao G, Zhang C, Zheng L (2017) Intrusion detection using deep belief network and probabilistic neural network. In: IEEE international conference on computational science and engineering, vol 1, pp 639–642

    Google Scholar 

  30. Alrawashdeh K, Purdy C (2017) Toward an online anomaly intrusion detection system based on deep learning. In: IEEE international conference on machine learning and applications, pp 195–200

    Google Scholar 

  31. Tan Q, Huang W, Li Q (2016) An intrusion detection method based on DBN in ad hoc networks. In: International conference on wireless communication and sensor network, pp 477–485

    Google Scholar 

  32. Yin Q, Zhu YF, Fei JL, He XZ (2017) A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5:21954–21961

    Google Scholar 

  33. Kolosnjaji B, Zarras A, Webster G, Eckert C (2016) Deep learning for classification of malware system call sequences. In: AI 2016: Advances in artificial intelligence, pp 137–149

    Google Scholar 

  34. Saxe J, Berlin K (2017) eXpose: a character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys. arXiv:1702.08568

  35. Wang W, Zhu M, Wang J, Zeng X, Yang Z (2017) End-to-end encrypted traffic classification with one-dimensional convolution neural networks. In: 2017 IEEE international conference on intelligence and security informatics (ISI), pp 43–48

    Google Scholar 

  36. Wang W, Zhu M, Zeng X, Ye X, Sheng Y (2017) Malware traffic classification using convolutional neural network for representation learning. In: International conference on information networking, pp 712–717

    Google Scholar 

  37. Sortomme E, Venkata S, Mitra J (2010) Microgrid protection using communication-assisted digital relays. IEEE Trans Power Deliv 25(4):2789–2796

    Article  Google Scholar 

  38. Bernabeu EE, Thorp JS, Centeno V (2012) Methodology for a security/dependability adaptive protection scheme based on data mining. IEEE Trans Power Deliv 27(1):104–111

    Article  Google Scholar 

  39. Wang ZJ, Liu Y, Wang ZJ: E-mail filtration and classification based on variable weights of the Bayesian algorithm. Appl Mech Mater 513–517

    Google Scholar 

  40. Almeida J, Almeida T, Yamakami A (2011) Spam filtering: how the dimensionality reduction affects the accuracy of Naive Bayes classifiers. J Internet Serv Appl 1(3):183–200

    Article  Google Scholar 

  41. Biggio B, Fumera G, Pillai I, Roli F (2011) A survey and experi- mental evaluation of image spam filtering techniques. Pattern Recognit Lett 32(10):1436–1446

    Article  Google Scholar 

  42. Zhang T, Zhu Q (2018) Distributed privacy-preserving collaborative intrusion detection systems for vanets. IEEE Trans Signal Inf Process Netw 4(1):148–161

    MathSciNet  Google Scholar 

  43. Kumar V, Srivastava J, Lazarevic A (2006) Managing cyber threats: issues, approaches, and challenges, vol 5. Springer Science & Business Media, New York, NY

    Google Scholar 

  44. Boyd S (2011) Alternating direction method of multipliers. In: Talk at nips workshop on optimization and machine learning. Now Publishers, Boston

    Google Scholar 

  45. Ranveer S, Hiray S (2015) Comparative analysis of feature extraction methods of malware detection. Int J Comput Appl 120(5):1–7

    Google Scholar 

  46. Wihersaari K (2015) Intelligence acquisition methods in cyber domain: examining the circumstantial applicability of cyber intelligence acquisition methods using a hierarchical model

    Google Scholar 

  47. Farwell JP, Rohozinski R (2011) Stuxnet and the future of cyber war. Survival 53(1):23–40

    Google Scholar 

  48. Figueiredo J, da Costa JS (2012) A SCADA system for energy management in intelligent buildings. Energy Build 49:85–98

    Article  Google Scholar 

  49. Huang L, Joseph AD, Nelson B, Rubinstein BI, Tygar J (2011) Adversarial machine learning. In Proceedings of the 4th ACM workshop on security and artificial intelligence. ACM, New York, NY, pp 43–58

    Google Scholar 

  50. Cruz T, Barrigas J, Proenca J, Graziano A, Panzieri S, Lev L, Simões P (2015) Improving network security monitoring for industrial control systems. In: IFIP/IEEE international symposium on integrated network management (IM) IM2015, pp 878–881

    Google Scholar 

  51. Maglaras LA, Jiang J (2014) Intrusion detection in SCADA systems using machine learning techniques. In: Science and information conference (SAI), pp 626–631

    Google Scholar 

  52. Fette I, Sadeh N, Tomasic A (2007) Learning to detect phishing emails. In: Proceedings of the 16th international conference on World Wide Web (WWW ’07). ACM, New York (US), pp 649–656

    Google Scholar 

  53. Santhana Lakshmi V, Vijaya MS (2012) Efficient prediction of phishing websites using supervised learning algorithms. Procedia Eng 30:798–805

    Article  Google Scholar 

  54. Almomani A, Gupta BB, Atawneh S, Meulenberg A, Almomani E (2013) A survey of phishing email filtering techniques. IEEE Commun Surv Tutor 15(4):2070–2090

    Article  Google Scholar 

  55. Gu X, Wang H, Ni T (2013) An efficient approach to detecting phishing web. J Comput Inf Syst 9(14):5553–5560

    Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering, Pondicherry University, Pondicherry, 609605, India

    Bhanu Chander & Gopalakrishnan Kumaravelan

Authors
  1. Bhanu Chander
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  2. Gopalakrishnan Kumaravelan
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Corresponding author

Correspondence to Bhanu Chander .

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Editors and Affiliations

  1. Department of Information Technology, Institute of Engineering & Management, Kolkata, West Bengal, India

    Mohuya Chakraborty

  2. Department of Information Technology, Institute of Engineering & Management, Kolkata, West Bengal, India

    Moutushi Singh

  3. Department of Automatics & Applied Software, Aurel Vlaicu University of Arad, Arad, Arad, Romania

    Valentina E. Balas

  4. Department of Information Technology, Institute of Engineering & Management, Kolkata, West Bengal, India

    Indraneel Mukhopadhyay

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Chander, B., Kumaravelan, G. (2021). Cyber Security with AI—Part I. In: Chakraborty, M., Singh, M., Balas, V.E., Mukhopadhyay, I. (eds) The "Essence" of Network Security: An End-to-End Panorama. Lecture Notes in Networks and Systems, vol 163. Springer, Singapore. https://doi.org/10.1007/978-981-15-9317-8_6

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  • DOI: https://doi.org/10.1007/978-981-15-9317-8_6

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-9316-1

  • Online ISBN: 978-981-15-9317-8

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