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An Exploration of Machine Learning Approaches in the Field of Cybersecurity

  • Conference paper
  • First Online:
Cryptology and Network Security with Machine Learning (ICCNSML 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 918))

  • 84 Accesses

Abstract

The extensive and growing utilization of the Internet and mobile apps has resulted in the enlargement of the online realm, rendering it more vulnerable to extended and automated cyber assaults. In response to this heightened vulnerability, cybersecurity techniques have been developed to strengthen security measures and improve the ability to detect and respond to cyberattacks. Due to the intelligence of cybercriminals in evading traditional security systems, the previously employed security measures have become inadequate. Conventional security systems struggle to effectively detect new and ever-changing security attacks that are previously unseen or have varying forms. ML methods are making substantial contributions to different aspects of cybersecurity, playing a pivotal role in numerous applications within the discipline. While ML systems have been successful so far, there are considerable obstacles in ensuring their trustworthiness. This paper’s main objective is to offer a thorough examination of the obstacles ML techniques encounter in safeguarding cyberspace from attacks. This is accomplished by examining the existing body of literature concerning ML techniques utilized in the field of cybersecurity. These techniques encompass areas such as intrusion detection, spam detection, and malware detection within computer and mobile networks. The document also provides succinct elucidations of each specific machine learning approach, indispensable machine learning tools, ML involvement in cybersecurity, and current state of ML for cybersecurity. Finally, the paper examines the barriers and challenges, as well as the anticipated path for the future of ML in the context of cybersecurity.

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References

  1. Zhou X et al (2022) Carbon-economic inequality in global ICT trade. Iscience 25(12)

    Google Scholar 

  2. Bhattacharyya DK, Kalita JK (2013) Network anomaly detection: a machine learning perspective. CRC Press

    Google Scholar 

  3. Thomas T, Vijayaraghavan AP, Emmanuel S (2020) Machine learning approaches in cyber security analytics. Springer, Singapore

    Book  Google Scholar 

  4. Al-Turjman F, Zahmatkesh H, Shahroze R (2022) An overview of security and privacy in smart cities’ IoT communications. Trans Emerg Telecommun Technol 33(3):e3677

    Article  Google Scholar 

  5. Firdausi I, Erwin A, Nugroho AS (2010) Analysis of machine learning techniques used in behavior-based malware detection. In: 2010 second international conference on advances in computing, control, and telecommunication technologies. IEEE

    Google Scholar 

  6. Manjramkar MA, Jondhale KC (2023) Cyber security using machine learning techniques. In: International conference on applications of machine intelligence and data analytics (ICAMIDA 2022). Atlantis Press

    Google Scholar 

  7. Kaspersky M (2020) What is cyber security?

    Google Scholar 

  8. Martínez Torres J, Comesaña CI, García-Nieto PJ (2019) Machine learning techniques applied to cybersecurity. Int J Mach Learn Cybern 10:2823–2836

    Google Scholar 

  9. Spafford EH (1994) Computer viruses as artificial life. Artif Life 1(3):249–265

    Article  Google Scholar 

  10. Ganapathi P (2020) A review of machine learning methods applied for handling zero-day attacks in the cloud environment. Handbook of research on machine and deep learning applications for cyber security, pp 364–387

    Google Scholar 

  11. Uma M, Padmavathi G (2013) A survey on various cyber-attacks and their classification. Int J Netw Secur 15(5):390–396

    Google Scholar 

  12. Dua S, Du X (2016) Data mining and machine learning in cybersecurity. CRC Press

    Google Scholar 

  13. Apruzzese G et al (2018) On the effectiveness of machine and deep learning for cyber security. In: 2018 10th international conference on cyber-Conflict (CyCon). IEEE

    Google Scholar 

  14. Fraley JB, Cannady J (2017) The promise of machine learning in cybersecurity. In: SoutheastCon 2017. IEEE

    Google Scholar 

  15. Kulkarni, AD, Brown III LL (2019) Phishing websites detection using machine learning

    Google Scholar 

  16. Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discovery 2(2):121–167

    Google Scholar 

  17. Witten IH et al (2017) Practical machine learning tools and techniques. Data mining, 4th edn, Elsevier Publishers

    Google Scholar 

  18. Srikant R, Agrawal R (1996) Mining sequential patterns: generalizations and performance improvements. In: International conference on extending database technology. Springer, Berlin, Heidelberg

    Google Scholar 

  19. Jain AK, Mao J, Moidin Mohiuddin K (1996) Artificial neural networks: a tutorial. Computer 29(3): 31–44

    Google Scholar 

  20. Sahu S, Mehtre BM (2015) Network intrusion detection system using J48 Decision Tree. In: 2015 international conference on advances in computing, communications and informatics (ICACCI). IEEE

    Google Scholar 

  21. Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Inc.

    Google Scholar 

  22. Selvaraj, Soundarya. Applying of machine learning for spam classification. Diss. Instytut Telekomunikacji, 2019.

    Google Scholar 

  23. Chandrasekar C, Priyatharsini P (2018) Classification techniques using spam filtering email. Int J Adv Res Comput Sci 9(2)

    Google Scholar 

  24. Lee SM et al (2010) Spam detection using feature selection and parameters optimization. In: 2010 international conference on complex, intelligent and software intensive systems. IEEE

    Google Scholar 

  25. Subramaniam T, Jalab HA, Taqa AY (2010) Overview of textual anti-spam filtering techniques. Int J Phys Sci 5(12):1869–1882

    Google Scholar 

  26. Kadir MFA et al (2022) Spam detection using machine learning based binary classifier. Indones J Electr Eng Comput Sci (IJEECS) 26(1):310–317

    Google Scholar 

  27. Sharma S, Arora A (2013) Adaptive approach for spam detection. Int J Comput Sci Iss (IJCSI) 10(4):23

    Google Scholar 

  28. Rathi M, Pareek V (2013) Spam mail detection through data mining—a comparative performance analysis. Int J Mod Educ Comput Sci 5(12)

    Google Scholar 

  29. Saab SA, Mitri N, Awad M (2014) Ham or spam? A comparative study for some content-based classification algorithms for email filtering. In: MELECON 2014–2014 17th IEEE Mediterranean electrotechnical conference. IEEE

    Google Scholar 

  30. Zhang Y et al (2014) Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowl-Based Syst 64:22–31

    Google Scholar 

  31. Subba B, Biswas S, Karmakar S (2016) Enhancing performance of anomaly-based intrusion detection systems through dimensionality reduction using principal component analysis. In: 2016 IEEE international conference on advanced networks and telecommunications systems (ANTS). IEEE

    Google Scholar 

  32. Tiwari VN, Rathore S, Patidar K (2016) Enhanced method for intrusion detection over KDD cup 99 dataset. Int J Curr Trends Eng Technol 2(02)

    Google Scholar 

  33. Kevric J, Jukic S, Subasi A (2017) An effective combining classifier approach using tree algorithms for network intrusion detection. Neural Comput Appl 28(Suppl 1):1051–1058

    Article  Google Scholar 

  34. Syarif AR, Gata W (2017) Intrusion detection system using hybrid binary PSO and K-nearest neighborhood algorithm. In: 2017 11th international conference on information & communication technology and system (ICTS). IEEE

    Google Scholar 

  35. Malik AJ, Khan FA (2018) A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection. Cluster Comput 21:667–680

    Google Scholar 

  36. Bouzida Y, Cuppens F (2006) Neural networks vs. decision trees for intrusion detection. In: IEEE/IST workshop on monitoring, attack detection and mitigation (MonAM), vol 28

    Google Scholar 

  37. Sarnovsky M, Paralic J (2020) Hierarchical intrusion detection using machine learning and knowledge model. Symmetry 12(2):203

    Article  Google Scholar 

  38. Anderson B et al (2011) Graph-based malware detection using dynamic analysis. J Comput Virol 7:247–258

    Google Scholar 

  39. Santos I et al (2013) Opcode sequences as representation of executables for data-mining-based unknown malware detection. Inf Sci 231:64–82

    Google Scholar 

  40. Salehi Z, Sami A, Ghiasi M (2014) Using feature generation from API calls for malware detection. Comput Fraud Secur 2014(9):9–18

    Article  Google Scholar 

  41. 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 

  42. Yan P, Yan Z (2018) A survey on dynamic mobile malware detection. Software Qual J 26(3):891–919

    Article  Google Scholar 

  43. Ma Z et al (2020) Droidetec: Android malware detection and malicious code localization through deep learning. arXiv preprint arXiv:2002.03594

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, India

    Brajesh Kumar Khare, Brajesh Kumar Khare & Imran Khan

Authors
  1. Brajesh Kumar Khare
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  2. Imran Khan
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Corresponding author

Correspondence to Brajesh Kumar Khare .

Editor information

Editors and Affiliations

  1. Department of Mathematics, Pranveer Singh Institute of Technology, Kanpur, India

    Atul Chaturvedi

  2. Department of Mathematics, Indian Institute of Technology Jammu, Jammu, India

    Sartaj Ul Hasan

  3. Applied Statistics Unit, Indian Statistical Institute, Kolkata, India

    Bimal Kumar Roy

  4. Department of Mathematics, Bar-Ilan University, Ramat-Gan, Israel

    Boaz Tsaban

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Khare, B.K., Khan, I. (2024). An Exploration of Machine Learning Approaches in the Field of Cybersecurity. In: Chaturvedi, A., Hasan, S.U., Roy, B.K., Tsaban, B. (eds) Cryptology and Network Security with Machine Learning. ICCNSML 2023. Lecture Notes in Networks and Systems, vol 918. Springer, Singapore. https://doi.org/10.1007/978-981-97-0641-9_24

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  • DOI: https://doi.org/10.1007/978-981-97-0641-9_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0640-2

  • Online ISBN: 978-981-97-0641-9

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