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All Journal International Journal of Education, Science, Technology, and Engineering (IJESTE) Journal of Computing Theories and Applications
More, Aditya
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Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering

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Automated Defense Against Application-Layer Attacks on Windows Systems Using Wazuh and Shuffle Thakker, Aastha; More, Aditya; Kumar, Kapil
International Journal of Education, Science, Technology, and Engineering (IJESTE) Vol 8 No 1: June 2025
Publisher : Lamintang Education and Training Centre, in collaboration with the International Association of Educators, Scientists, Technologists, and Engineers (IA-ESTE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36079/lamintang.ijeste-0801.842

Abstract

Application-layer attacks targeting Windows systems remain a significant threat due to their ability to bypass traditional perimeter defenses. These attacks often exploit vulnerabilities listed in the OWASP Top 10 for desktop applications, demanding proactive defense mechanisms. This paper proposes a unified approach that combines SIEM and SOAR capabilities to detect and respond to Windows-based application-layer threats with increased efficiency and automation. The framework integrates the open-source SIEM platform Wazuh with the SOAR engine Shuffle to automate threat detection and incident response. A layered defense strategy is implemented, involving log correlation, rule-based policy enforcement, and playbook-driven response automation. The integration reduces manual triage overhead and enhances response time compared to traditional SOC patterns. This framework demonstrates a scalable, open-source-based solution for defending Windows environments at the application layer. It sets the groundwork for future integration of AI-driven analytics, multi-OS support, and tamper-proof event lo event logging using blockchain technologies.
Integrating Fully Homomorphic Encryption and Zero-Knowledge Proofs for Efficient Verifiable Computation Qureshi, UmmeAmmara; Doshi, Bhumika; More, Aditya; Joshi, Kashyap; Kumar, Kapil
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14181

Abstract

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information, although they do not support encrypted computation independently. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL for homomorphic encryption and PySNARK for Zero-Knowledge Proof verification. Experiments are conducted on standard consumer-grade hardware (Intel i5, 8 GB RAM, Ubuntu 22.04) using datasets ranging from 100 MB to 1 GB. The evaluation focuses on encryption and decryption time, homomorphic computation latency, memory usage, and proof generation overhead. Experimental results show that integrating ZKPs introduces a moderate and stable runtime overhead of approximately 15–20%, as analyzed in Section 4, while enabling verification without plaintext disclosure. Ciphertext expansion remains a notable limitation, with observed growth of approximately 30–40× relative to plaintext size, consistent with prior FHE implementations. Despite these overheads, the system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware. Overall, the results indicate that the integrated FHE+ZKP approach provides a practical balance between confidentiality, verifiability, and performance, supporting its applicability to privacy-preserving scenarios such as secure cloud computation, encrypted data analytics, and confidential data processing under realistic resource constraints.
Co-Authors Doshi, Bhumika Joshi, Kashyap Kumar, Kapil Qureshi, UmmeAmmara Thakker, Aastha