Sandeep Dasari

Challenges in implementing machine learning (ML) include expanding data resources within the finance sector. Banking data with significant financial implications are highly confidential. Diverse breaches and privacy violations can result from a combination of user information from different institutions for banking purposes. To address these issues, federated learning (FL) using a flower framework is utilized to protect the privacy of individual organizations while still collaborating through separate models to create a unified global model. However, joint training on datasets with diverse distributions can lead to suboptimal learning and additional privacy concerns. To mitigate this, solutions using federated averaging (FedAvg), federated proximal (FedProx), and federated optimization methods have been proposed. These methods work with data locality during training at local clients without exposing data, while maintaining global convergence to enhance the privacy of local models within the framework. In this analysis, the UNSW-NB15 and credit datasets were employed, utilizing precision, recall, accuracy, F1-score, ROC, and AUC as performance indicators to demonstrate the effectiveness of the proposed strategy using FedAvg, FedProx, and FedOpt. The proposed algorithms were subjected to an empirical study, which revealed significant performance benefits when using the flower framework. Consequently experiments were conducted over 50 rounds using the UNSW-NB15 dataset, which achieved accuracies of 99.87% for both FedAvg and FedProx and 99.94% for FedOpt. Similarly, with the credit dataset under the same conditions, FedAvg and FedProx achieved accuracies of 99.95% and 99.94%, respectively. These results indicate that the proposed framework is highly effective and can be applied in real-world applications across various domains for secure and privacy-preserving collaborative machine learning.

Data privacy is essential in the financial sector to protect client’s sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It has become a challenging task due to the increase in usage of the internet and digital transactions. In this scenario, DDoS attack is one of the major attacks that makes clients’ privacy questionable. It requires effective and robust attack detection and prevention techniques. Machine Learning (ML) is the most effective approach for employing cyber attack detection systems. It paves the way for a new era where human and scientific communities will benefit. This paper presents a hierarchical ML-based hyperparameter-optimization approach for classifying intrusions in a network. CICIDS 2017 standard dataset was considered for this work. Initially, data was preprocessed with the min-max scaling and SMOTE methods. The LASSO approach was used for feature selection, given as input to the hierarchical ML algorithms: XGboost, LGBM, CatBoost, Random Forest (RF), and Decision Tree (DT). All these algorithms are pretrained with hyperparameters to enhance the effectiveness of algorithms. Models performance was assessed in terms of recall, precision, accuracy, and F1-score metrics. Evaluated approaches have shown that the LGBM algorithm gives a proven performance in classifying DDoS attacks with 99.77% of classification accuracy.

The increasing data pool in finance sectors forces machine learning (ML) to step into new complications. Banking data has significant financial implications and is confidential. Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages. As a result, this study employs federated learning (FL) using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model. However, diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy. To address this issue, the present paper proposes the implementation of Federated Averaging (FedAvg) and Federated Proximal (FedProx) methods in the flower framework, which take advantage of the data locality while training and guaranteeing global convergence. Resultantly improves the privacy of the local models. This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation (CICIDS) datasets. Precision, recall, and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx. The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55% and 83.72% for FedAvg and 99.57%, and 84.63% for FedProx.

In the current digital era, data is budding tremendously from various sources like banks, businesses, education, entertainment, etc. Due to its significant consequence, it became a prominent proceeding for numerous research areas like the semantic web, machine learning, computational intelligence, and data mining. For knowledge extraction, several corporate sectors depend on tweets, blogs, and social data to get adequate analysis. It helps them predict the customer’s tastes and preferences, optimize the usage of resources. In some cases, the same data creates complications that lead to a problem named as big data. To solve this, so many researchers have given various solutions. Based on literature analysis formulated 6-s simulation towards big data, detailed information about characteristics, a taxonomy of tools, and discussed various processing paradigms. No one tool can truly fit for all solutions, so this paper helps to make decisions smoothly by providing enough information and discussing major privacy issues and future directions.