Journal of Network Security and Data Mining
This is blind peer-reviewed journal that provides a platform which aspires to cover all the aspects of Network Security and Data Mining and its Integration into Recent Technologies in the field of Computer Engineering. Focus and Scope Wireless Networks and Protocols Mobile Computing Peer to Peer Networks ATM Networks Optical Networks Reliability and Fault-tolerance IP/ATM Networks Security Interconnection Networks Computer Networks Parallel/Distributed Architectures Communication Multimedia Systems
The Outcome Concerned with Process for Scholars Established On Distributed Data Mining
The student result-oriented learning process evaluation system is a vital tool for monitoring and improving the quality of education. This paper explores such a system through the lens of data analysis, focusing on the integration of distributed data mining and decision tree algorithms. Data mining has become a powerful method for uncovering patterns and trends within large datasets. The goal of this research is to develop a rule-discovery approach tailored to evaluating student learning outcomes. By applying this method in practice, the system aims to enhance the assessment of communication skills and better support the overall learning experience.
Blockchain – Based Secure Land Registration System
Land registration systems are critical for ensuring property rights, yet traditional methods are often centralized, slow, and vulnerable to fraud, resulting in ownership disputes and lack of transparency. This project presents a secure land registration system based on blockchain technology, designed to provide tamper-proof, transparent, and efficient property management. The system uses a decentralized ledger where every land record is stored as a block, verified by multiple nodes, and protectedby cryptographic techniques. Smart contracts automate verification, ownership transfer, and record management, eliminating the need for intermediaries and reducing human error. Citizens can register land, check ownership history, and transfer property seamlessly, while government authorities can verify records in real time. The system also integrates IPFS (Inter Planetary File System) to securely store large documents, ensuring data integrity. Compared to traditional approaches, this blockchain-based solution offers enhanced security, faster verification, transparency, and reduced fraud, making property transactions reliable and trustworthy. The implementation demonstrates a practical framework for modernizing land governance, which can be extended to include mobile applications, GIS integration, and AI-based dispute prediction, providing a scalable and robust solution for secure land management.
SecureDAD: Real-Time Intelligent IPv6 DAD Attack Detection & Mitigation
SecureDAD is an advanced IPv6 network security system which may prevent spoofing attacks before they disrupt communication services. This paper detects malicious Neighbor Discovery behavior at an early stage by examining live packet traffic captured from networks. We used artificial intelligence instead of conventional monitoring methods which are often slow and inaccurate. We used two highly effective methods Random Forest and anomaly scoring to understand suspicious patterns in IPv6 packets and classify whether traffic is normal or malicious. The dashboard allows simple Web based use of the system. It also provides pictorial graphs to depict performance and outcomes. The technique will enable administrators to detect attacks promptly and with high assurance in low resource environments quickly today always safely for users everywhere.
AI-Driven Disaster Aid Prioritization System for Efficient Resource Allocation and Emergency Response
Natural disasters such as floods, earthquakes, cyclones, and landslides often result in severe damage to human lives, infrastructure, and essential resources. One of the major challenges during such emergencies is ensuring that relief efforts are delivered quickly and efficiently, especially when resources are limited and conditions are constantly changing. Traditional disaster management approaches largely depend on manual assessment and human decision-making, which can lead to delays, inaccuracies, and unequal distribution of aid. This project presents a Disaster Aid Prioritization System that utilizes machine learning techniques to enhance the decision-making process. The system analyzes key factors such as population size, level of damage, presence of medical emergencies, and availability of critical resources like water, shelter, and food. Based on these parameters, it classifies affected regions into priority levels such as High, Medium, and Low urgency. By automating the prioritization process, the system reduces reliance on human judgment and improves response time. It provides a structured and data-driven approach for identifying the most critical areas, enabling authorities to allocate resources more effectively. The system is scalable, cost-efficient, and can be integrated with real-time data sources in the future, making it suitable for practical disaster response scenarios.
Polycystic Ovary Syndrome Risk Detection Using Machine Learning
Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder affecting women of reproductive age, causing issues such as irregular menstrual cycles, weight gain, and infertility, making early detection very important for proper treatment and management. Traditional diagnostic methods are often time-consuming and require multiple medical tests, which can delay diagnosis. The aim of this project is to develop an intelligent system using machine learning techniques to detect PCOS at an early stage with better accuracy. This system uses patient data such as age, body mass index (BMI), hormonal levels, and other clinical parameters for prediction. The objective of the project is to collect and preprocess the dataset, apply different machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest, and compare their performance based on evaluation metrics like accuracy, precision, and recall. The system analyzes the input data and predicts whether a patient is likely to have PCOS or not. By comparing multiple algorithms, the most efficient model is selected for better prediction results. This approach helps in reducing manual effort and supports healthcare professionals in making faster and more reliable decisions. The proposed system is cost-effective, user-friendly, and can be used as a supportive diagnostic tool. Overall, the project demonstrates that machine learning can play a significant role in improving early detection and management of PCOS.
A Machine Learning Framework for Credit Card Fraud Detection Using Random Forest and SMOTE
The rapid proliferation of online payment systems has made credit card fraud a critical financial security challenge. Detecting fraudulent transactions is inherently difficult due to severe class imbalance and continuously evolving fraud patterns. This paper presents a machine learning-based credit card fraud detection system integrated with a real-time web application. The system is built using a Python Flask backend and an HTML/JavaScript frontend. Uploaded transaction datasets are processed through missing value handling, outlier removal using the Interquartile Range (IQR) method, and class balancing via SMOTE. A Random Forest classifier is then dynamically trained on the processed data to classify transactions as fraudulent or legitimate. The application delivers rich visualizations including confusion matrix, precision-recall curve, fraud probability distribution, and transaction analysis graphs, along with a manual prediction module for real-time transaction testing. Experimental results demonstrate effective fraud detection with high accuracy, providing a practical, interpretable, and user-friendly fraud detection platform that combines machine learning and modern web technologies.