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Artificial Intelligence for Cybersecurity: Recent Advancements, Challenges and Opportunities

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Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1030))

Abstract

With the advancement of technology, there exists a wide variety of cybersecurity attacks like ID theft, cracking the captcha, data breaches. These attacks are affecting individuals as well as well-established organizations. To handle these attacks, a robust and intelligent system is required. Artificial intelligence (AI) is one of the most emerging areas used in cyber security to protect internet-connected systems from eavesdropping, attacks, unauthorized access, and threats. AI is a technique that enables machines to tackle every situation intelligently. AI has been used almost in every area, from health care to robotics. The concept of AI in cyber-security makes machines more intelligent and actionable compared to traditional approaches. In this article, we focus and deliberate concisely on the use of AI in cyber-security, its application, various challenges, and opportunities. Authors have also highlighted future perspectives of AI in cybersecurity.

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References

  1. Abbas, N.N., Ahmed, T., Shah, S.H.U., Omar, M., Park, H.W.: Investigating the applications of artificial intelligence in cyber security. Scientometrics 121(2), 1189–1211 (2019). https://doi.org/10.1007/s11192-019-03222-9

    Article  Google Scholar 

  2. Adekunle, Y.A., Adebayo, A.O.: Holistic exploration of gaps vis-à-vis artificial intelligence in automated teller machine and internet banking 12(19), 4–8 (2019)

    Google Scholar 

  3. Ahmed, M., Naser, M.A., Hu, J.: A survey of network anomaly detection techniques. J. Netw. Comput. Appl. 60, 19–31 (2016). https://doi.org/10.1016/j.jnca.2015.11.016

    Article  Google Scholar 

  4. Ali, R., Ali, A., Aleem, S.: A systematic review of artificial intelligence and machine learning techniques for cyber security a systematic review of artificial intelligence and machine learning techniques for cyber security (2020). https://doi.org/10.1007/978-981-15-7530-3

  5. Aljamal, I., Tekeoglu, A., Bekiroglu, K., Sengupta, S.: Hybrid intrusion detection system using machine learning techniques in cloud computing environments. In: Proceedings 2019 IEEE/ACIS 17th International Conference on Software Engineering Research, Management and Application, SERA 2019, pp. 84–89 (2019). https://doi.org/10.1109/SERA.2019.8886794

  6. Anwar, S., Zain, J.M., Zolkipli, M.F., Inayat, Z., Khan, S., Anthony, B., Chang, V.: From intrusion detection to an intrusion response system: fundamentals, requirements, and future directions. Algorithms 10(2) (2017). https://doi.org/10.3390/a10020039

  7. Bhatele, K.R., Shrivastava, H., Kumari, N.: The role of artificial intelligence in cyber security. August 2020, 170–192 (2019). https://doi.org/10.4018/978-1-5225-8241-0.ch009

  8. Brundage, M., Avin, S., Clark, J., Toner, H., Eckersley, P., Garfinkel, B., Dafoe, A., et al.: The malicious use of artificial intelligence: forecasting, prevention, and mitigation (2018). https://doi.org/10.17863/CAM.22520. https://www.researchgate.net/publication/323302750_The_Malicious_Use_of_Artificial_Intelligence_Forecasting_Prevention_and_Mon

  9. Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1176 (2016). https://doi.org/10.1109/COMST.2015.2494502

    Article  Google Scholar 

  10. Chaudhary, H., Detroja, A., Prajapati, P., Shah, P.: A review of various challenges in cybersecurity using artificial intelligence. In: Proceedings of the 3rd International Conference on Intelligent Sustainable Systems, ICISS 2020, 829–836 (2020). https://doi.org/10.1109/ICISS49785.2020.9316003

  11. Chowdhury, M., Rahman, A., Islam, R.: Malware analysis and detection using data mining and machine learning classification. In: Abawajy, J., Choo, K.-K.R., Islam, R. (eds.) International Conference on Applications and Techniques in Cyber Security and Intelligence–Applications and Techniques in Cyber Security and Intelligence. Advances in Intelligent Systems and Computing, vol. 580, pp. 266–274. Springer-Verlag London Ltd. (2018). https://doi.org/10.1007/978-3-319-67071-3_33

  12. Demertzis, K., Iliadis, L.: A bio-inspired hybrid artificial intelligence framework for cyber security. In: Daras, N., Rassias, M. (eds.) Computation, Cryptography, and Network Security. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18275-9_7

  13. Dongmei, Z., Jinxing, L.: Study on network security situation awareness based on particle swarm optimization algorithm. Comput. Ind. Eng. 125, 764–775 (2018). https://doi.org/10.1016/j.cie.2018.01.006

  14. Eian, I.C., Yong, L.K., Li, M., Qi, Y.H., Fatima, Z.: Cyber attacks in the era of COVID-19 and possible solution domains (2020). https://doi.org/10.20944/preprints202009.0630.v1

  15. El-latif, A.A., Abd-El-Atty, B., Mehmood, I., Muhammad, K., Venegas-Andraca, S.E., Peng, J.: Quantum-inspired blockchain-based cybersecurity: securing smart edge utilities in IoT-based smart cities. Inf. Process. Manag. 58, 102549 (2021)

    Google Scholar 

  16. Geetha, R., Thilagam, T.: A review on the effectiveness of machine learning and deep learning algorithms for cyber security. Arch. Comput. Meth. Eng. 28(4), 2861–2879 (2021). https://doi.org/10.1007/s11831-020-09478-2

    Article  MathSciNet  Google Scholar 

  17. Gupta, B.B., Prajapati, V., Nedjah, N., et al.: Machine learning and smart card based two-factor authentication scheme for preserving anonymity in telecare medical information system (TMIS). Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-021-06152-x

    Article  Google Scholar 

  18. Korkmaz, Y.: Kullanıcı Giriş Sistemlerinde Yapay Sinir Ağları Kullan ı larak Ş ifre Güvenlik Sisteminin Geli ş tirilmesi Developing Password Security System By Using Artificial Neural Networks In User Log In Systems Bilgisayar Mühendisli ğ i , Fatih Sultan Mehm, pp. 1–4 (2016)

    Google Scholar 

  19. Kumar, M., Singh, N., Kumar, R., Goel, S., Kumar, K.: Gait recognition based on vision systems: a systematic survey. J. Visual Comm. Image Represent. 75(August 2020), 103052 (2021). https://doi.org/10.1016/j.jvcir.2021.103052

  20. Karthik Narayan, L., Sonu, G., Soukhya, S. M.: Fingerprint recognition and its advanced features. Int. J. Eng. Res. Technol. 9(04), 424–428 (2020). https://doi.org/10.17577/ijertv9is040393

  21. Kaye, J., Whitley, E., Lund, D., et al.: Dynamic consent: a patient interface for twenty-first century research networks. Eur. J. Hum. Genet. 23, 141–146 (2015). https://doi.org/10.1038/ejhg.2014.71

  22. Li, H.U.A.: Cyber security meets artificial intelligence: a survey. Front. Inform. Technol. Electr. Eng. 19(12), 1462–1474 (2018). https://doi.org/10.1631/FITEE.1800573

  23. Li, Z., Zheng, L.: The impact of artificial intelligence on accounting. Haisa, 158–169. https://doi.org/10.2991/icsshe-18.2018.203

  24. Malatji, M., Marnewick, A., Solms, S.V.: The impact of artificial intelligence on the human aspects of information and cybersecurity. In: Proceedings of the Twelfth International Symposium on Human Aspects of Information Security & Assurance, HAISA 2018 (2018)

    Google Scholar 

  25. Marir, N., Wang, H., Feng, G., Li, B., Jia, M.: Distributed abnormal behavior detection approach based on deep belief network and ensemble SVM using Spark. IEEE Access 6, 59657–59671 (2018). https://doi.org/10.1109/access.2018.2875045

  26. Naderpour, M., Lu, J., Zhang, G.: A situation risk awareness approach for process systems safety. Safety Sci. 64, 173–189 (2014). https://doi.org/10.1016/j.ssci.2013.12.005

  27. Naik, B., Mehta, A., Yagnik, H., Shah, M.: The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review. Complex Intell. Syst. 0123456789. https://doi.org/10.1007/s40747-021-00494-8

  28. Radanliev, P., De Roure, D., Page, K., Nurse, J.R.C., Mantilla Montalvo, R., Santos, O., Maddox L.T., Burnap, P.: Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains. Cybersecurity 3(1) (2020). https://doi.org/10.1186/s42400-020-00052-8

  29. Salhi, D.E., Tari, A., Kechadi, M.T.: Email classification for forensic analysis by information gain technique. Int. J. Softw. Sci. Comput. Intell. 13(4), 40–53 (2021). https://doi.org/10.4018/ijssci.2021100103

    Article  Google Scholar 

  30. Salloum, S.A., Alshurideh, M.: Machine learning and deep learning techniques for cybersecurity : a review, vol. 2. Springer International Publishing (2020). https://doi.org/10.1007/978-3-030-44289-7

  31. Saravanan, A., Bama, S.S.: A review on cyber security and the fifth generation cyberattacks. Oriental J. Comput. Sci. Technol. 12(2), 50–56 (2019). https://doi.org/10.13005/ojcst12.02.04

  32. Sarker, I.H., Kayes, A.S.M., Badsha, S., et al.: Cybersecurity data science: an overview from machine learning perspective. J. Big Data 7, 41 (2020). https://doi.org/10.1186/s40537-020-00318-5

  33. Sarker, I.H., Furhad, M.H., Nowrozy, R.: AI-driven cybersecurity: an overview, security intelligence modeling and research directions. SN Comput. Sci. 2(3), 1–18 (2021). https://doi.org/10.1007/s42979-021-00557-0

    Article  Google Scholar 

  34. Sedik, A.,Tawalbeh, L.O.A.I., Hammad, M.,Latif, A., EI-Banby, G.M., Khalaf, A.F., Samie, F., LLiyasu, A.M.: Deep learning modalities for biometric alteration detection in 5G networks-based secure smart cities. In: IEEE Access, vol. 9, 94780–94788 (2021). https://doi.org/10.1109/ACCESS.2021.3088341

  35. Sharma, G.D., Yadav, A., Chopra, R.: Artificial intelligence and effective governance: a review, critique and research agenda. Sustain. Futures 2(December 2019), 100004 (2020). https://doi.org/10.1016/j.sftr.2019.100004

  36. Shidaganti, G.I., Inamdar, A.S., Rai, S.V., Rajeev, A.M.: SCEF: a model for prevention of DDoS attacks from the cloud. Int. J. Cloud Appl. Comput. 10(3), 67–80 (2020). https://doi.org/10.4018/ijcac.2020070104

    Article  Google Scholar 

  37. Shoufan, A., Taha, B.: Machine learning-based drone detection and classification: state-of-the-art in research. IEEE Access 7, 138669–138682 (2019). https://doi.org/10.1109/access.2019.2942944

  38. Tao, F., Akhtar, M., Jiayuan, Z.: The future of artificial intelligence in cybersecurity: a comprehensive survey. EAI Endorsed Trans. Creative Technol. 8(28), 170285. https://doi.org/10.4108/eai.7-7-2021.170285

  39. Truong, T.C., Diep, Q.B., Zelinka, I.: Artificial intelligence in the cyber domain: offense and defense. Symmetry MDPI AG 12(3), 410 (2020). https://doi.org/10.3390/sym12030410

  40. Ubale, T., Jain, A.K.: Survey on DDoS attack techniques and solutions in software-defined network. In: Gupta, B., Perez, G., Agrawal, D., Gupta, D. (eds.) Handbook of Computer Networks and Cyber Security. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-22277-2_15

  41. Ullah, F., Babar, M.A.: Architectural tactics for big data cybersecurity analytics systems: a review. J. Syst. Softw. 151, 81–118 (2019)

    Article  Google Scholar 

  42. Vähäkainu, P., Lehto, M.: Artificial intelligence in the cyber security environment artificial intelligence in the cyber security environment (2019)

    Google Scholar 

  43. Wiafe, I., Koranteng, F.N., Obeng, E.N., Assyne, N., Wiafe, A., Gulliver, S.R.: Artificial intelligence for cybersecurity: a systematic mapping of literature. IEEE Access 8, 146598–146612 (2020). https://doi.org/10.1109/ACCESS.2020.3013145

    Article  Google Scholar 

  44. Xu, M., Peng, J., Gupta, B.B., Kang, J., Xiong, Z., Li, Z., EI-Latif, A.A.: Multi-agent federated reinforcement learning for secure incentive mechanism in intelligent cyber-physical systems. In: IEEE Internet of Things Journal (2021). https://doi.org/10.1109/JIOT.2021.3081626

  45. Xu, Z., Ray, S., Subramanyan, P., Malik, S.: Malware detection using machine learning based analysis of virtual memory access patterns. In: Proceedings of the Conference on Design, Automation & Test in Europe, Lausanne, Switzerland, 27–31, pp. 169–174 (2017)

    Google Scholar 

  46. Ye, Y., Chen, L., Hou, S., et al.: DeepAM: a heterogeneous deep learning framework for intelligent malware detection. Knowl. Inf. Syst. 54, 265–285 (2018). https://doi.org/10.1007/s10115-017-1058-9

  47. Zeng, Y., Gu, H., Wei, W., Guo, Y.: Deep-full-range: a deep learning based network encrypted traffic classification and intrusion detection framework. IEEE Access, 1–1 (2019)

    Google Scholar 

  48. Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., Zhang, F., Choo, K.K.R.: Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artif. Intell. Rev. 0123456789 (2021). https://doi.org/10.1007/s10462-021-09976-0

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Authors and Affiliations

  1. Department of Computational Sciences, Maharaja Ranjit Singh Punjab Technical University, Bathinda, Punjab, India

    Veenu Rani & Munish Kumar

  2. University Institute of Engineering and Technology, Panjab University, Chandigarh, India

    Ajay Mittal & Krishan Kumar

Authors
  1. Veenu Rani
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  2. Munish Kumar
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  3. Ajay Mittal
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  4. Krishan Kumar
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Corresponding author

Correspondence to Ajay Mittal .

Editor information

Editors and Affiliations

  1. State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Nadia Nedjah

  2. Mathematics and Computer Science Department, Menoufia University, Faculty of Science, Menofia, Egypt

    Ahmed A. Abd El-Latif

  3. Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan

    Brij B. Gupta

  4. State University of Rio de Janeiro, Rio de Janeiro, Brazil

    Luiza M. Mourelle

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Rani, V., Kumar, M., Mittal, A., Kumar, K. (2022). Artificial Intelligence for Cybersecurity: Recent Advancements, Challenges and Opportunities. In: Nedjah, N., Abd El-Latif, A.A., Gupta, B.B., Mourelle, L.M. (eds) Robotics and AI for Cybersecurity and Critical Infrastructure in Smart Cities. Studies in Computational Intelligence, vol 1030. Springer, Cham. https://doi.org/10.1007/978-3-030-96737-6_4

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