Leveraging Deep Autoencoders for Security in Big Data Framework: An Unsupervised Cloud Computing Approach
anomaly detection; big data; cloud computing; deep autoencoder; encryption; security
Abnormalities recognition in bank transaction big data is the number one issue for stability of financial security system. Due to the rate digital transactions are increasing it is vital to have effective ways. Encryption with deep autoencoder model should be explored as it involves trained neural networks that learn such patterns from the complex transaction data. The following paper demonstrates application of anomaly detection using deep autoencoders in the banking big data transactions. It focuses on the theoretical bases, network design, preparedness and the testing measures for deep autoencoders. On the other hand, it solves problems such as high dimensionality and imbalanced dataset. This research paper shows deep autoencoders effectiveness in deep learning and how the network identifies different fraudulent big data transactions, money laundry and unauthorized access. It also encompasses recent developments of cloud environments and future methods using deep autoencoders including the fact that constant search for new possible solutions is a must. The insights delivered contribute to the discourse in financial security community, which incorporates researchers, practitioners, and policymakers involved in anomaly detection in cloud. 2024 IEEE.
Roy S.; Nijaguna G.S.; Shankar S.S.; Adnan M.M.; Umaeswari P.
International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024
Institute of Electrical and Electronics Engineers Inc.
Roy S., Christ (Deemed to Be University), Department of Electronics and Communication Engineering, Bangalore, India; Nijaguna G.S., S.E.A. College of Engineering and Technology, Department of Information Science and Engineering, Bangalore, India; Shankar S.S., Nitte Meenakshi Institute of Technology, Dept of Ece, Bengaluru, India; Adnan M.M., The Islamic University, Najaf, Iraq; Umaeswari P., R.M.K. Engineering College, Department of Computer Science and Business Systems, Tamil Nadu, Kavaraipettai, India