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All Journal Journal of Computing Theories and Applications Jurnal INFOTEL
Musa, Muhammad Nazeer
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Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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An Empirical Analysis of Injection Attack Vectors and Mitigation Strategies in Redis NoSQL Database Musa, Muhammad Nazeer; Irhebhude, Martins Ekata
Journal of Computing Theories and Applications Vol. 2 No. 4 (2025): JCTA 2(4) 2025
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.12640

Abstract

The contemporary landscape of data management, marked by an unprecedented scale and velocity of data, has spurred the widespread adoption of NoSQL databases, prioritizing scalability and performance over traditional relational constraints. While offering significant flexibility, this paradigm shift introduces complex cybersecurity challenges, notably query injection vulnerabilities, which are consistently ranked among the top web application security risks. Redis, a leading in-memory key-value store powering critical infrastructure globally, presents a unique security profile due to its architectural design and features like Lua scripting. Despite its prevalence, a comprehensive academic evaluation of Redis injection attack vectors remains understudied. This study addresses this gap by systematically evaluating command and Lua script injection vulnerabilities in Redis version 7.4.1 across controlled configurations: default, password-protected, and ACL-secured environments. We quantify vulnerability risk and empirically validate mitigation strategies by employing a Dockerized testing framework, Python-driven exploit simulations, and CVSS v3.1 scoring. Our findings reveal critical weaknesses in default and permissively configured environments and demonstrate that restrictive Access Control Lists (ACLs), adhering to the principle of least privilege, provide complete mitigation against the specific injection vectors evaluated in our controlled experimental setup. We propose a Redis-specific threat taxonomy and provide empirically validated recommendations for securing Redis deployments, emphasizing layered security controls and proper ACL implementation. This research contributes the first systematic evaluation of modern Redis injection vulnerabilities and highlights the critical importance of security-conscious configurations to protect vital data infrastructure.
Klasifikasi Tumor Otak Berbasis MRI menggunakan ResNet-50 dan Regresi Softmax yang Dioptimalkan Musa, Muhammad Nazeer
JURNAL INFOTEL Vol 16 No 3 (2024): August 2024
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/infotel.v16i3.1175

Abstract

Accurate classification of brain tumors is crucial for effective treatment planning and patient management. This study presents a new hybrid deep learning classification method based on transfer learning by feature extraction to automate the categorization of MRI brain image datasets into four classes: meningioma, glioma, pituitary tumor, and no tumor. The proposed method combines a finely-tuned ResNet-50 model, a state-of-the-art convolutional neural network architecture, with optimized Softmax Regression (SR) for classification. The study explores the use of data augmentation techniques and evaluates the model's performance on both augmented and unaugmented images. The results demonstrate that the proposed method achieves an impressive accuracy of 98.4%, outperforming existing methods for automatic brain tumor detection. Furthermore, a detailed comparative analysis is presented to evaluate the proposed model's accuracy and efficiency against previous state-of-the-art hybrid models for brain tumor classification. The study suggests that the proposed methodology could be employed as a diagnostic tool to aid radiologists in identifying questionable brain regions, potentially improving the accuracy and efficiency of brain tumor diagnosis.
Co-Authors Irhebhude, Martins Ekata