I. J. of Electrical & Electronics Research
Research Article | 
Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application
Author(s) : B. Narsimha1, Ch V Raghavendran2, Pannangi Rajyalakshmi3, G Kasi Reddy4, M. Bhargavi5 and P. Naresh6
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 2 , Special Issue on IEEE-SD
Publisher : FOREX Publication
Published : 13 May 2022
e-ISSN : 2347-470X
Page(s) : 87-92
Abstract
Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms. Therefore, AI with machine learning techniques has been set up with cyber security to build intelligent models for malware categorization & intelligently sensing the fraught with danger. This paper introduces the cyber security defense mechanism by using artificial intelligence (AI), machine learning (ML)) techniques with the current Feedzai security model to identifying fraudulent banking transaction. We have given a preface to the popular ML & AI model with random forest algorithm and Feedzai’s Open ML fraud detection software tool, which provides automatic fraud-recognition to the current intelligent framework for solving Financial Fraud Detection.
Keywords: Digital Space, Machine Learning, Deep Learning, Malware Detection Intelligence Sensing Feedzai
B. Narsimha, Asso. Prof. & Head, Department of CSE, Holymary Institute of Technology and Science, Hyderabad, India; Email: prof.narsimha@gmail.com
Ch V Raghavendran, Prof., Department of CSE, Aditya College of Engineering and Technology, Surampalem, India; Email: raghuchv@yahoo.com
Pannangi Rajyalakshmi, Asst. Prof., Department of CSE, Guru Nanak Institute of Technology, Hyderabad, India; Email: pannangiraji@gmail.com
G Kasi Reddy, Asst. Prof., Department of CSE, Guru Nanak Institute of Technology, Hyderabad, India; Email: kasireddyg.csegnit@gniindia.org
M. Bhargavi, Asst. Prof., Department of CSE, CMR Engineering College, Hyderabad, India; Email: maddibhargavireddy@gmail.com
P. Naresh, Asst. Prof., Department of IT, Guru Nanak Institutions Technical Campus, Hyderabad, India; Email: pannanginaresh@gmail.com
[1] Abeshu A, Chilamkurti N, 2018. Deep learning: the frontier for distributed attack detection in fog-to-things computing. IEEE Commun Mag, 56(2):169-175, https://doi.org/10.1109/MCOM.2018.1700332 [Cross Ref]
[2] Akhtar N, Mian A, 2018. Threat of adversarial attacks on deep learning in computer vision: a survey. IEEE Access, 6:14410-14430.[Cross Ref]
[3] B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Srndic, P. Laskov, G. Giacinto, and F. Roli, ‘‘Evasion attacks against machine learning at test time,’’ 2017, arXiv:1708.06131. [Online]. Available: http://arxiv. org/abs/1708.06131[Cross Ref]
[4] Quamar Niyaz, Weiqing Sun, Ahmad Y Javaid, and Mansoor Alam, “A Deep Learning Approach for Network Intrusion Detection System,” IEEE Transactions on Emerging Topics in Computational Intelligence, 2018.[Cross Ref]
[5] G. Apruzzese, M. Colajanni, L. Ferretti, and M. Marchetti, ‘‘Addressing adversarial attacks against security systems based on machine learning,’’ in Proc. 11th Int. Conf. Cyber Conflict (CyCon), May 2019, pp. 1–18.[Cross Ref]
[6] Wookhyun Jung, Sangwon Kim,, Sangyong Choi, “Deep Learning for Zero-day Flash Malware Detection,” IEEE security, 2017.
[7] J. Gardiner and S. Nagaraja, ‘‘on the security of machine learning in malware c&c detection: A survey,’’ ACM Comput. Surv., vol. 49, no. 3, pp. 59:1–59:39, 2016.[Cross Ref]
[8] G. Li, P. Zhu, J. Li, Z. Yang, N. Cao, and Z. Chen, ‘‘Security matters: A survey on adversarial machine learning,’’ 2018, arXiv:1810.07339. [Online]. Available: http://arxiv.org/abs/1810.07339.
[9] Soleymanzadeh, Raha, Mustafa Aljasim, Muhammad W. Qadeer, and Rasha Kashef. 2022. "Cyberattack and Fraud Detection Using Ensemble Stacking" AI 3, no. 1: 22-36. https://doi.org/10.3390/ai3010002.[Cross Ref]
[10] N. Martins, J. M. Cruz, T. Cruz and P. Henriques Abreu, "Adversarial Machine Learning Applied to Intrusion and Malware Scenarios: A Systematic Review," in IEEE Access, vol. 8, pp. 35403-35419, 2020, doi: 10.1109/ACCESS.2020.2974752.[Cross Ref]
[11] H. Wang et al., "Deep Learning-Based Interval State Estimation of AC Smart Grids Against Sparse Cyber Attacks," in IEEE Transactions on Industrial Informatics, vol. 14, no. 11, pp. 4766-4778, Nov. 2018, doi: 10.1109/TII.2018.2804669.[Cross Ref]
[12] W. Xue, H. Wang, T. Wu, J. Peng and Y. Liu, "An Ensembled ELMs Based Defense Mechanism Against Cyber Attack on Power Systems," 2019 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 2019, pp. 1-5, doi: 10.1109/APPEEC45492.2019.8994549.[Cross Ref]
[13] X. Yuan, P. He, Q. Zhu, and X. Li, ‘‘Adversarial examples: Attacks and defenses for deep learning,’’ IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 9, pp. 2805–2824, Sep. 2019.[Cross Ref]
[14] H. Amrouch, P. Krishnamurthy, N. Patel, J. Henkel, R. Karri and F. Khorrami, "Special session: emerging (Un-)reliability based security threats and mitigations for embedded systems," 2017 International Conference on Compilers, Architectures and Synthesis For Embedded Systems (CASES), 2017, pp. 1-10, doi: 10.1145/3125501.3125529.[Cross Ref]
[15] C. Whyte, "Problems of Poison: New Paradigms and "Agreed” Competition in the Era of AI-Enabled Cyber Operations," 2020 12th International Conference on Cyber Conflict (CyCon), 2020, pp. 215-232, doi: 10.23919/CyCon49761.2020.9131717.[Cross Ref]
B. Narsimha, Ch V Raghavendran, Pannangi Rajyalakshmi, G Kasi Reddy, M. Bhargavi and P. Naresh (2022), Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application. IJEER 10(2), 87-92. DOI: 10.37391/IJEER.100206.