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Chandra Shekhar Gautam

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Review Open access Jul 2026

A Review on Supervised Machine Learning Techniques for Enhancing Cyber Threat Prediction Accuracy

The rapid expansion of digital infrastructures has increased the magnitude and sophistication of cyber threats, making timely and accurate threat prediction a foundational requirement for modern cyber-security systems. We use multiple algorithms—including Random Forest, Gradient Boosting Machines, and Deep Neural Networks—on benchmark intrusion-detection datasets and real-world enterprise log samples. Experimental results demonstrate that an ensemble-optimized model achieves improved predictive accuracy, reduced false positives, and enhanced generalization to unseen attack patterns. The study highlights important feature engineering techniques, model-optimization strategies, and deployment considerations for practical cyber-security environments. This research focuses on developing a supervised machine learning model to improve the accuracy of cyber threat prediction by leveraging historical and labeled cyber-security data. Experimental analysis on a benchmark transaction dataset demonstrates that unsupervised models can achieve over 90% recall in detecting abnormal activities, providing a scalable and adaptive defense against evolving cyber threats in online banking.

Bandana Gupta, Chandra Shekhar Gautam · 0 citations
Review Open access Aug 2026

A Survey on Privacy-Preserving Techniques for Cloud Data Processing Using Homomorphic Encryption and Federated Learning

Cloud computing offers organizations scalable storage and computation, but outsourcing data processing to third-party infrastructure introduces serious privacy and confidentiality risks. Two complementary paradigms have emerged to address this challenge: homomorphic encryption (HE), which allows computation directly on encrypted data, and federated learning (FL), which enables collaborative model training without centralizing raw data. This paper presents a structured review of privacy-preserving data processing techniques for cloud environments built on HE and FL, individually and in hybrid combination. We propose a taxonomy of existing approaches, synthesize representative literature in a comparative table, illustrate a generic hybrid HE-FL architecture, and evaluate the two paradigms against criteria including data exposure, computational overhead, communication cost, resistance to inference attacks, and cloud deployment readiness. We further identify open challenges — including computational latency, key management, non-IID data distributions, and standardization gaps — and outline promising directions for future research, such as hardware-accelerated HE, adaptive encryption granularity, and standardized hybrid privacy frameworks for cloud-native machine learning.

Shivendra Shukla, Chandra Shekhar Gautam, Divyansh Tiwari · 0 citations