Abstract
Artificial Intelligence occupies a major part in the end-to-end technology we use every day. In order to ensure and enhance security, Artificial Intelligence techniques are used in cyber security applications. Many of the cyber security applications including DDoS security, web firewall, antivirus, and antimalware are attacked everyday by various means by the attackers. As a result, we need an algorithm or a system that learns from the existing attacks and detect intrusions in the mere future of the same pattern. Artificial Intelligence helps to prevent breaches of a sensitive organization and customer data. Artificial Intelligence techniques help in early detection of threats. The use of Artificial Intelligence in cyber security applications helps analyze the traffic in a network, and fast incident response schemes can be applied in order to prevent the attack to happen. The main objective of this chapter is to analyze the uses of Artificial Intelligence techniques in various cyber security applications in order to achieve safe transactions between the users.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Sun, C.C., Hahn, A., Liu, C.C.: Cyber security of a power grid: state-of-the-art. Int. J. Electr. Power Energy Syst. 99, 45–56 (2018)
Tr uong, T.C., Zelinka, I., Plucar, J., ÄŒandÃk, M., Å ulc, V.: Artificial intelligence and cybersecurity: past, presence, and future. In: Artificial Intelligence and Evolutionary Computations in Engineering Systems, pp. 351–363. Springer, Singapore (2020)
Ongsulee, P.: Artificial intelligence, machine learning and deep learning. In: 15th International Conference on ICT and Knowledge Engineering (ICT&KE), pp. 1–6. IEEE (2017)
Mohammed, I.A.: Artificial intelligence for cybersecurity: a systematic mapping of literature. Int. J. Innovations Eng. Res. Technol. 7(9) (2020)
Anwar, S., Mohamad Zain, J., 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 39(2), 10 (2017)
Mohammadi, S., Mirvaziri, H., Ghazizadeh-Ahsaee, M., Karimipour, H.: Cyber intrusion detection by combined feature selection algorithm. J. Inf. Secur. Appl. 44, 80–88 (2019)
Tapiador, J.E., Orfla, A., Ribagorda, A., Ramos, B.: Key-recovery attacks on kids, a keyed anomaly detection system. IEEE Trans. Dependable Secur. Comput. 12(3), 312–325 (2013)
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)
Zheng, Y., Li, Z., Xu, X., Zhao, Q.: Dynamic defenses in cyber security: techniques, methods and challenges. Digit. Commun. Netw. 8(4), 422–435 (2022)
Kilincer, I.F., Ertam, F., Sengur, A.: Machine learning methods for cyber security intrusion detection: datasets and comparative study. Comput. Netw. 188, 107840 (2021)
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)
Shinan, K., Alsubhi, K., Alzahrani, A., Ashraf, M.U.: Machine learning-based botnet detection in software-defined network: a systematic review. Symmetry 13(5), 866 (2021)
Buchanan, B.G., Smith, R.G.: Fundamentals of expert systems. Annu. Rev. Comput. Sci. 3(1), 23–58 (1988)
Li, J.H.: Cyber security meets artificial intelligence: a survey. Frontiers Inf. Technol. Electronic Eng. 19(12), 1462–1474 (2018)
Rudenko, M., Zhivago, E., Rudenko, A.: Expert System for Modeling Threats and Protecting Premises from Information Leaks (2022)
Rani, C., Goel, S.: CSAAES: An expert system for cyber security attack awareness. In: International Conference on Computing, Communication and Automation, pp. 242–245. IEEE (2015)
Kivimaa, J., Ojamaa, A., Tyugu, E.: Graded security expert system. In: International Workshop on Critical Information Infrastructures Security, pp. 279–286. Springer, Berlin, Heidelberg (2008)
Malek, Z.S., Trivedi, B., Shah, A.: User behavior pattern-signature based intrusion detection. In: Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), pp. 549–552. IEEE (2020)
Alhayani, B., Mohammed, H.J., Chaloob, I.Z., Ahmed, J.S.: Effectiveness of artificial intelligence techniques against cyber security risks apply of IT industry. Mater. Today: Proc. (2021)
Anwar, A., Hassan, S.I.: Applying artificial intelligence techniques to prevent cyber assaults. Int. J. Comput. Intell. Res. 13(5), 883–889 (2017)
Kott, A.: Intelligent autonomous agents are key to cyber defense of the future army networks. Cyber Defense Rev. 3(3), 57–70 (2018)
Wang, P., Govindarasu, M.: Multi intelligent agent based cyber attack resilient system protection and emergency control. In: IEEE Power and Energy Society Innovative Smart Grid Technologies Conference (ISGT), pp. 1–5. IEEE (2016)
Ford, V., Siraj, A.: Applications of machine learning in cyber security. In: Proceedings of the 27th International Conference on Computer Applications in Industry and Engineering, vol. 118. IEEE Xplore, Kota Kinabalu, Malaysia (2014)
Salloum, S.A., Alshurideh, M., Elnagar, A., Shaalan, K.: Machine learning and deep learning techniques for cybersecurity: a review. In: The International Conference on Artificial Intelligence and Computer Vision, pp. 50–57. Springer, Cham (2020)
Panda, M., Patra, M.R.: Network intrusion detection using Naive Bayes. Int. J. Comput. Sci. Netw. Secur. 7(12), 258–263 (2007)
Amiri, F., Yousefi, M.R., Lucas, C., Shakery, A., Yazdani, N.: Mutual information-based feature selection for intrusion detection systems. J. Netw. Comput. Appl. 34(4), 1184–1199 (2011)
Kruegel, C., Toth, T.: Using decision trees to improve signature-based intrusion detection. In: International Workshop on Recent Advances in Intrusion Detection, pp. 173–191. Springer, Berlin, Heidelberg (2003)
Li, Z., Zhang, A., Lei, J., Wang, L.: Real-time correlation of network security alerts. In: IEEE International Conference on e-Business Engineering (ICEBE’07), pp. 73–80. IEEE (2007)
Sequeira, K., Zaki, M.: Admit: anomaly-based data mining for intrusions. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 386–395 (2002)
Banerjee, J., Maiti, S., Chakraborty, S., Dutta, S., Chakraborty, A., Banerjee, J.S.: Impact of machine learning in various network security applications. In: 3rd International Conference on Computing Methodologies and Communication (ICCMC), pp. 276–281. IEEE (2019)
Sjarif, N.N.A., Chuprat, S., Mahrin, M.N.R., Ahmad, N.A., Ariffin, A., Senan, F.M., et al.: Endpoint detection and response: why use machine learning? In: International Conference on Information and Communication Technology Convergence (ICTC), pp. 283–288. IEEE (2019)
MartÃn, A.G., Beltrán, M., Fernández-Isabel, A., de Diego, I.M.: An approach to detect user behaviour anomalies within identity federations. Comput. Secur. 108, 102356 (2021)
Ferrag, M.A., Maglaras, L., Moschoyiannis, S., Janicke, H.: Deep learning for cyber security intrusion detection: approaches, datasets, and comparative study. J. Inf. Secur. Appl. 50, 102419 (2020)
Roopak, M., Tian, G.Y., Chambers, J.: Deep learning models for cyber security in IoT networks. In: IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0452–0457. IEEE (2019)
Choi, Y.H., Liu, P., Shang, Z., Wang, H., Wang, Z., Zhang, L., et al.: Using deep learning to solve computer security challenges: a survey. Cybersecurity 3(1), 1–32 (2020)
Singh, G.A.P., Gupta, P.K.: Performance analysis of various machine learning-based approaches for detection and classification of lung cancer in humans. Neural Comput. Appl. 31(10), 6863–6877 (2019)
Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)
Zheng, A., Casari, A.: Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists. O’Reilly Media, Inc. (2018)
Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdisc. Rev.: Data Min. Knowl. Discov. 8(4), e1249 (2018)
Schneier, B.: Invited talk: The coming AI hackers. In: International Symposium on Cyber Security Cryptography and Machine Learning, pp. 336–360. Springer, Cham (2021)
Karuppiah, M., Saravanan, R.: A secure remote user mutual authentication scheme using smart cards. J Inf Secur. Appl. 19(4–5), 282–294 (2014)
Karuppiah, M., Saravanan, R.: A secure authentication scheme with user anonymity for roaming service in global mobility networks. Wireless Pers. Commun. 84(3), 2055–2078 (2015)
Karuppiah, M., Kumari, S., Li, X., Wu, F., Das, A.K., Khan, M.K., Basu, S.: A dynamic id-based generic framework for anonymous authentication scheme for roaming service in global mobility networks. Wireless Pers. Commun. 93(2), 383–407 (2017)
Kumari, S., Karuppiah, M., Li, X., Wu, F., Das, A.K., Odelu, V.: An enhanced and secure trust-extended authentication mechanism for vehicular ad-hoc networks. Secur. Commun. Netw. 9(17), 4255–4271 (2016)
Karuppiah, M., Kumari, S., Das, A.K., Li, X., Wu, F., Basu, S.: A secure lightweight authentication scheme with user anonymity for roaming service in ubiquitous networks. Secur. Commun. Netw. 9(17), 4192–4209 (2016)
Naeem, M., Chaudhry, S.A., Mahmood, K., Karuppiah, M., Kumari, S.: A scalable and secure RFID mutual authentication protocol using ECC for Internet of Things. Int. J. Commun. Syst. 33(13), e3906 (2020)
Karuppiah, M., Das, A.K., Li, X., Kumari, S., Wu, F., Chaudhry, S.A., Niranchana, R.: Secure remote user mutual authentication scheme with key agreement for cloud environment. Mob. Netw. Appl. 24(3), 1046–1062 (2019)
Maria, A., Pandi, V., Lazarus, J.D., Karuppiah, M., Christo, M.S.: BBAAS: blockchain-based anonymous authentication scheme for providing secure communication in VANETs. Secur. Commun. Netw. 2021 (2021)
Pradhan, A., Karuppiah, M., Niranchana, R., Jerlin, M.A., Rajkumar, S.: Design and analysis of smart card-based authentication scheme for secure transactions. Int. J. Internet Technol. Secured Trans. 8(4), 494–515 (2018)
Li, X., Niu, J., Bhuiyan, M.Z.A., Wu, F., Karuppiah, M., Kumari, S.: A robust ECC-based provable secure authentication protocol with privacy preserving for industrial internet of things. IEEE Trans. Industr. Inf. 14(8), 3599–3609 (2017)
Bhagat, R.C., Patil, S.S.: Enhanced SMOTE algorithm for classification of imbalanced big-data using random forest. In: IEEE International Advance Computing Conference (IACC), pp. 403–408. IEEE (2015)
Menardi, G., Torelli, N.: Training and assessing classification rules with imbalanced data. Data Min. Knowl. Disc. 28(1), 92–122 (2014)
Tyagi, S., Mittal, S.: Sampling approaches for imbalanced data classification problem in machine learning. In: Proceedings of ICRIC 2019, pp. 209–221. Springer, Cham (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Potula, S.R., Selvanambi, R., Karuppiah, M., Pelusi, D. (2023). Artificial Intelligence-Based Cyber Security Applications. In: Sarveshwaran, V., Chen, J.IZ., Pelusi, D. (eds) Artificial Intelligence and Cyber Security in Industry 4.0. Advanced Technologies and Societal Change. Springer, Singapore. https://doi.org/10.1007/978-981-99-2115-7_16
Download citation
DOI: https://doi.org/10.1007/978-981-99-2115-7_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-2114-0
Online ISBN: 978-981-99-2115-7
eBook Packages: Computer ScienceComputer Science (R0)