Past Issues

Cryptographic Methods in Cybersecurity – Analyzing Mathematical Foundations of Encryption, Blockchain, and Post-Quantum Cryptography

Abstract

With the rapid expansion of digital communication and data storage, cybersecurity has become a critical concern for organizations and individuals. Cryptographic methods play a vital role in ensuring data confidentiality, integrity, and authentication. This study explores the mathematical foundations of encryption, blockchain security, and post-quantum cryptography. Traditional encryption methods such as symmetric and asymmetric encryption rely on number theory and complex mathematical problems like integer factorization and discrete logarithms. Blockchain security is reinforced by cryptographic hashing and digital signatures, ensuring tamper-proof transactions. However, the advent of quantum computing poses a significant threat to existing cryptographic protocols, necessitating the development of post-quantum cryptographic methods. This research provides an in-depth analysis of current cryptographic techniques, evaluates their effectiveness, and discusses future advancements in quantum-resistant cryptography.

Limnological Characterization and Ecological Assessment of Selected Freshwater Wetlands in Fatehabad District, Haryana, India

Abstract

Freshwater wetlands are unique and fragile ecosystems where the physico-chemical properties of water govern its biodiversity and ecological status. The present investigation was carried out in three freshwater wetlands Daulatpuria Pond (DP), Chilli Lake (CL) and Bhodia Khera Temple Pond (BP) of Fatehabad district, Haryana, India. The study was conducted through two sampling campaigns: first consisted of monthly sampling from December 2023 to November 2024, while the second comprised of quarterly sampling from December 2024 to November 2025. Physicochemical parameters viz. temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), total dissolved salts (TDS), Electrical Conductivity (EC), total hardness and alkalinity in water of the selected wetlands were analyzed. Water quality was also assessed by using the Weighted Arithmetic Water Quality Index (WQI). A significant spatial and temporal variation was recorded in physicochemical parameters among the study sites. The water of the wetlands was slightly alkaline in nature and its DO was found to be lower during summer season and was significantly correlated with BOD level. The TDS and EC values were comparatively higher at CL depicting the higher amount of dissolved ions present in this wetland. WQI was ranged from 191.86 to 195.53, 193.29-194.72, 231.21-236.67 in DP, BP and CL, respectively, which implied that the water of the studied wetlands was highly polluted and unfit for domestic and drinking purposes, of which the CL was more polluted in comparison to the rest two. The results highlight the degree of anthropogenic activities taking place in these wetlands and the necessity of their regular monitoring.

An Exploration-Driven Hybrid Machine Learning Framework for Advanced Mathematical Problem Solving

Abstract

Solving advanced mathematical problems requires a combination of logical reasoning, pattern recognition, and efficient exploration of complex solution spaces. Traditional rule-based systems often struggle with high-dimensional and non-linear problems, while standalone machine learning (ML) approaches are limited by their dependence on existing data and lack of systematic exploration capabilities. This paper proposes a novel hybrid framework that integrates the Theory of Exploration with machine learning to enhance mathematical problem-solving efficiency and accuracy. The proposed model incorporates three key components: an exploration engine for generating diverse hypotheses, a machine learning module for evaluating and optimizing candidate solutions, and a reasoning layer to ensure mathematical correctness and validation. A feedback-driven iterative mechanism enables continuous refinement by dynamically balancing exploration and exploitation. Experimental evaluation demonstrates that the integrated approach significantly outperforms conventional and ML-only methods in terms of accuracy, convergence speed, and error reduction across various problem types, including algebraic, optimization, and pattern recognition tasks. The framework not only improves computational efficiency but also promotes adaptive learning and discovery of novel solution pathways. This research highlights the importance of combining exploratory intelligence with data-driven learning and presents a scalable approach applicable to optimization, theorem discovery, and intelligent educational systems. The proposed methodology paves the way for more adaptive, interpretable, and cognitively inspired mathematical problem-solving systems

Development of Intelligent Frameworks for Brute Force Intrusion Detection using Large Language Models

Abstract

As people utilize more digital devices and networks, cyber dangers like brute force attacks that take advantage of poor authentication techniques are becoming more common. Signature-based or classical machine learning algorithms are often not very good at uncovering new and unexpected attack patterns with low false positives. This paper suggests creating a smart hybrid system that uses ML and LLMs to find brute-force attacks. The method starts with speedy initial detection through data preprocessing, feature engineering, and categorization based on machine learning. Then it moves on to semantic analysis led by LLMs, which makes it even easier to understand the context and make decisions. Experimental results reveal that the suggested hybrid model works better than solo models since it lowers false alarms and raises accuracy, precision, recall, and F1-score. Adding LLMs can help you comprehend and evaluate complicated attack behaviors more easily.