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Integration of Machine Learning with Cybersecurity: Applications and Challenges

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Artificial Intelligence in Cyber Security: Theories and Applications

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 240))

Abstract

Machine learning (ML) has advanced from the lab to the forefront of operational systems in recent years. Machine learning is used by Facebook, Amazon, and Google every day to improve consumer experiences and purchases. Machine learning enables personal interactions and helps people connect socially with the use of new applications. The significant capability of machine learning is also present in cybersecurity. ML has become necessary in wide range of fields, also there are several cybersecurity implementations of ML. Some of them are malware analysis, particularly for zero-day “malware detection”, “threat analysis”, “anomaly-based intrusion detection” of typical attacks on sensitive infrastructures, and a variety of other applications.

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References

  1. T. Saba, Recent advancement in cancer detection using machine learning: systematic survey of decades, comparisons and challenges. J. Infect. Public Health 13(9), 1274–1289 (2020). https://doi.org/10.1016/j.jiph.2020.06.033

    Article  Google Scholar 

  2. K. Bhanot, S.K. Peddoju, T. Bhardwaj, A model to find optimal percentage of training and testing data for efficient ECG analysis using neural network. Int. J. Syst. Assur. Eng. Manag. 9(1), 12–17 (2018). https://doi.org/10.1007/s13198-015-0398-7

    Article  Google Scholar 

  3. K. Kadarla, S. C. Sharma, T. Bhardwaj, A. Chaudhary, A simulation study of response times in cloud environment for IoT-based healthcare workloads, in Proceedings of the 14th IEEE International Conference on Mobile Ad Hoc Sensor Systems MASS 2017, (2017), pp. 678–683. https://doi.org/10.1109/MASS.2017.65

  4. D. Gangwani, P. Gangwani, Applications of machine learning and artificial intelligence in intelligent transportation system: a review, in Lecture Notes in Electrical Engineering (Springer, 2021), pp. 203–216

    Google Scholar 

  5. Symantec, Internet security threat report. Netw. Secur. 21(2), 1–3 (2016)

    Google Scholar 

  6. T. Bhardwaj, R. Mittal, H. Upadhyay, L. Lagos, Applications of swarm intelligent and deep learning algorithms for image-based cancer recognition, in Artificial Intelligence in Healthcare (Springer, Singapore, 2022), pp. 133–150

    Google Scholar 

  7. P. Gangwani, J. Soni, H. Upadhyay, S. Joshi, A deep learning approach for modeling of geothermal energy prediction. Int. J. Comput. Sci. Inf. Secur. 18(1), 62–65 (2020)

    Google Scholar 

  8. T. Bhardwaj, H. Upadhyay, L. Lagos, Deep learning-based cyber security solutions for smart-city: application and review,” in Artificial Intelligence in Industrial Applications, vol. 25, ed by T. Sharma, S. Fernandes (Springer, Cham, 2022)

    Google Scholar 

  9. T. Bhardwaj, T. K. Sharma, M. R. Pandit, Social engineering prevention by detecting malicious URLs using artificial bee colony algorithm. Adv. Intell. Syst. Comput. 258, 355–363 (2014). https://doi.org/10.1007/978-81-322-1771-8_31

  10. T. Bhardwaj, End-to-End Data Security for Multi-Tenant Cloud Environment (2016)

    Google Scholar 

  11. M.M. Anjum, S. Iqbal, B. Hamelin, Analyzing the usefulness of the DARPA OpTC dataset in cyber threat detection research, in Proceedings of the 26th ACM Symposium on Access Control Models and Technologies (2021), pp. 27–32. https://doi.org/10.1145/3450569.3463573

  12. T. Bhardwaj, C. Reyes, H. Upadhyay, S.C. Sharma, L. Lagos, Cloudlet-enabled wireless body area networks (WBANs): a systematic review, architecture, and research directions for QoS improvement. Int. J. Syst. Assur. Eng. Manag. (2021). https://doi.org/10.1007/s13198-021-01508-x

    Article  Google Scholar 

  13. T. Bhardwaj, S.C. Sharma, Fuzzy logic-based elasticity controller for autonomic resource provisioning in parallel scientific applications: A cloud computing perspective. Comput. Electr. Eng. 70, 1049–1073 (2018). https://doi.org/10.1016/j.compeleceng.2018.02.050

    Article  Google Scholar 

  14. T. Bhardwaj, S.C. Sharma, Cloud-WBAN: an experimental framework for cloud-enabled wireless body area network with efficient virtual resource utilization. Sustain. Comput. Informatics Syst. 20, 14–33 (2018). https://doi.org/10.1016/j.suscom.2018.08.008

    Article  Google Scholar 

  15. B. Ingre, A. Yadav, Performance analysis of NSL-KDD dataset using ANN, in 2015 International Conference on Signal Processing and Communication Engineering Systems (2015), pp. 92–96. https://doi.org/10.1109/SPACES.2015.7058223

  16. M. Tavallaee, E. Bagheri, W. Lu, A. A. Ghorbani, A detailed analysis of the KDD CUP 99 data set, in 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (2009), pp. 1–6. https://doi.org/10.1109/CISDA.2009.5356528

  17. Y. Zhou, X. Jiang, Dissecting android malware: characterization and evolution, in 2012 IEEE Symposium on Security and Privacy (2012), pp. 95–109. https://doi.org/10.1109/SP.2012.16

  18. A.-D. Schmidt, J.H. Clausen, A. Camtepe, S. Albayrak, Detecting Symbian OS malware through static function call analysis, in 2009 4th International Conference on Malicious and Unwanted Software (MALWARE) (2009), pp. 15–22. https://doi.org/10.1109/MALWARE.2009.5403024

  19. Y. Hao, H. Liang, D. Zhang, Q. Zhao, B. Cui, JavaScript malicious codes analysis based on naive bayes classification, in 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (2014), pp. 513–519. https://doi.org/10.1109/3PGCIC.2014.147

  20. Y. Lu, P. Zulie, L. Jingju, S. Yi, Android malware detection technology based on improved Bayesian classification, in 2013 Third International Conference on Instrumentation, Measurement, Computer, Communication and Control (2013), pp. 1338–1341. https://doi.org/10.1109/IMCCC.2013.297

  21. F. Shang, Y. Li, X. Deng, D. He, Android malware detection method based on naive Bayes and permission correlation algorithm. Cluster Comput. 21(1), 955–966 (2018). https://doi.org/10.1007/s10586-017-0981-6

    Article  Google Scholar 

  22. B. Biggio et al., Security evaluation of support vector machines in adversarial environments. Support Vector Mach. Appl. 9783319023007, 105–153 (2014). https://doi.org/10.1007/978-3-319-02300-7_4

    Article  Google Scholar 

  23. H. Haes Alhelou, M. Hamedani-Golshan, T. Njenda, P. Siano, A survey on power system blackout and cascading events: research motivations and challenges. Energies 12(4), 682 (2019). https://doi.org/10.3390/en12040682

  24. M. Kezunovic et al., Design, implementation and validation of a real-time digital simulator for protection relay testing. IEEE Trans. Power Deliv. 11(1), 158–164 (1996). https://doi.org/10.1109/61.484012

    Article  Google Scholar 

  25. Z. Ramzan, C. Wüest, Phishing attacks: analyzing trends in 2006, in 4th Conference on Email Anti-Spam, CEAS 2007 (2007)

    Google Scholar 

  26. S.O. Uwagbole, W.J. Buchanan, L. Fan, Applied machine learning predictive analytics to SQL injection attack detection and prevention, in 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) (2017), pp. 1087–1090. https://doi.org/10.23919/INM.2017.7987433

  27. A. Altaher, Phishing websites classification using hybrid SVM and KNN approach. Int. J. Adv. Comput. Sci. Appl. 8(6) (2017). https://doi.org/10.14569/ijacsa.2017.080611

  28. M. Zouina, B. Outtaj, A novel lightweight URL phishing detection system using SVM and similarity index. Human-centric Comput. Inf. Sci. 7(1), 17 (2017). https://doi.org/10.1186/s13673-017-0098-1

    Article  Google Scholar 

  29. P. Gangwani, A. Perez-Pons, T. Bhardwaj, H. Upadhyay, S. Joshi, L. Lagos, Securing environmental IoT data using masked authentication messaging protocol in a DAG-based blockchain: IOTA tangle. Futur. Internet 13(12), 312 (2021). https://doi.org/10.3390/fi13120312

    Article  Google Scholar 

  30. D. Moore, V. Paxson, S. Savage, C. Shannon, S. Staniford, N. Weaver, Inside the slammer worm. IEEE Secur. Priv. 1(4), 33–39 (2003). https://doi.org/10.1109/MSECP.2003.1219056

    Article  Google Scholar 

  31. D. Gangwani, Q. Liang, S. Wang, X. Zhu, An empirical study of deep learning frameworks for melanoma cancer detection using transfer learning and data augmentation, in 2021 IEEE International Conference on Big Knowledge (ICBK) (2021), pp. 38–45. https://doi.org/10.1109/ICKG52313.2021.00015

  32. W. Gao, T. Morris, B. Reaves, D. Richey, On SCADA control system command and response injection and intrusion detection, in 2010 eCrime Researchers Summit (2010), pp. 1–9. https://doi.org/10.1109/ecrime.2010.5706699

  33. L.A. Maglaras, J. Jiang, OCSVM model combined with K-means recursive clustering for intrusion detection in SCADA systems, in 10th International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness (2014), pp. 133–134. https://doi.org/10.1109/QSHINE.2014.6928673

  34. T. Bhardwaj, S.C. Sharma, An autonomic resource provisioning framework for efficient data collection in cloudlet-enabled wireless body area networks: a fuzzy-based proactive approach. Soft Comput. 23(20), 10361–10383 (2019). https://doi.org/10.1007/s00500-018-3587-x

    Article  Google Scholar 

  35. R. Panwar, M. Supriya, Autonomic resource allocation frameworks for service-based cloud applications: a survey, in Proceedings of the 2019 International Conference on Computing, Communication and Intelligent Systems ICCCIS 2019, vol. 2019 (2019), pp. 214–219. https://doi.org/10.1109/ICCCIS48478.2019.8974463

  36. L.A. Maglaras, J. Jiang, T.J. Cruz, Combining ensemble methods and social network metrics for improving accuracy of OCSVM on intrusion detection in SCADA systems. J. Inf. Secur. Appl. 30, 15–26 (2016). https://doi.org/10.1016/j.jisa.2016.04.002

    Article  Google Scholar 

  37. S. Shaw, S. Kadam, S. Joshi, D. Hadsul, Advanced Virtual Apparel Try Using Augmented Reality (AVATAR), vol. 1154 (2020)

    Google Scholar 

  38. Y. Tang, N. Cheng, W. Wu, M. Wang, Y. Dai, X. Shen, Delay-minimization routing for heterogeneous VANETs with machine learning based mobility prediction. IEEE Trans. Veh. Technol. 68(4), 3967–3979 (2019). https://doi.org/10.1109/TVT.2019.2899627

    Article  Google Scholar 

  39. T. Zhang, Q. Zhu, Distributed privacy-preserving collaborative intrusion detection systems for VANETs. SIEEE Trans. Signal Inf. Process. over Networks 4(1), 148–161 (2018). https://doi.org/10.1109/TSIPN.2018.2801622

    Article  MathSciNet  Google Scholar 

  40. K. Shaukat et al., Performance comparison and current challenges of using machine learning techniques in cybersecurity. Energies 13(10), 2509 (2020). https://doi.org/10.3390/en13102509

    Article  Google Scholar 

  41. M. Pawlicki, M. Choraś, R. Kozik, W. Hołubowicz, On the impact of network data balancing in cybersecurity applications, in Lecture Notes in Computer Science (2020), pp. 196–210

    Google Scholar 

  42. S. Singhal, U. Chawla, R. Shorey, Machine learning & concept drift based approach for malicious website detection, in 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS) (2020), pp. 582–585. https://doi.org/10.1109/COMSNETS48256.2020.9027485

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Author information

Authors and Affiliations

  1. Department of Mechanical and Materials Engineering, Florida International University, Miami, USA

    Suprabha Das

  2. Department of Electrical & Computer Engineering, Florida International University, Miami, USA

    Pranav Gangwani

  3. Applied Research Center, Florida International University, Miami, USA

    Himanshu Upadhyay

Authors
  1. Suprabha Das
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  2. Pranav Gangwani
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  3. Himanshu Upadhyay
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Corresponding author

Correspondence to Suprabha Das .

Editor information

Editors and Affiliations

  1. Applied Research Center, Florida International University, Miami Fl, FL, USA

    Tushar Bhardwaj

  2. Applied Research Center, Florida International University, Miami Fl, FL, USA

    Himanshu Upadhyay

  3. Shobhit University, Gangoh, India

    Tarun Kumar Sharma

  4. Department of Computer Science, Creighton University, Omaha, NE, USA

    Steven Lawrence Fernandes

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Das, S., Gangwani, P., Upadhyay, H. (2023). Integration of Machine Learning with Cybersecurity: Applications and Challenges. In: Bhardwaj, T., Upadhyay, H., Sharma, T.K., Fernandes, S.L. (eds) Artificial Intelligence in Cyber Security: Theories and Applications. Intelligent Systems Reference Library, vol 240. Springer, Cham. https://doi.org/10.1007/978-3-031-28581-3_7

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