Implementasi Row-Level Security pada Basis Data sebagai Strategi Mitigasi Insider Threat dengan Pendekatan Zero Trust

Analyzing Wazuh-Based File Integrity Monitoring for Layered Academic Server Security

The proposed system demonstrates the potential to serve as an effective and practical host-level security layer for strengthening cybersecurity resilience in academic server environments, although the evaluation was limited to three monitored servers and did not include advanced adversarial attack scenarios.

Privacy-Preserving Collaborative Vulner-ability Assessment using Secure Multi-Party Computation

Privileged Access Management for z/OS System Programmers: A Zero-Trust Control Framework for RACF, TSO, and SDSF Administrative Sessions

The PAM in z/OS systems needs additional measures in the realm of protecting sensitive data from misuse by insiders and credential abuse. Classic approaches using authentication through static mechanisms do not provide adequate protection of administrative sessions that use RACF, TSO, and SDSF, thus leaving systems open to risks associated with administrative activities. To combat these risks, this paper proposes implementing a new control approach for PAM known as the Zero-Trust Control Framework, based on behavioural analysis, graph theory, and entropy calculations. The new tool developed as part of this framework is called Behavioural Identity Drift Analyzer (BIDA). As such, the Privilege Interaction Graph Transformer (PIGT) leverages graph neural networks and transformers to generate privilege interaction graphs that detect the escalation routes, dependencies, and propagation vectors of insider threats in near real-time. Lastly, the Entropy Adaptive Zero-Trust Orchestrator (EAZTO) analyzes uncertainties in user actions, commands, and privileges in order to apply adaptive control measures such as multi-factor authentication, privilege reduction, or even session termination. In combination, these four modules provide layers of protection and achieve continuous PAM decision-making at 90 percent accuracy for the purpose of enhancing zero trust enforcement in z/OS administration.

TrustWorkday: A Zero-Trust Security and Compliance Architecture for Enterprise SaaS Platforms

Enterprise SaaS platforms have emerged as key components in today's digital transformation programs by improving efficiency, scalability, and distributed workforce support. Traditional perimeter-based security measures are inadequate in safeguarding critical data stored in enterprise environments due to their distributed nature. To overcome this problem, the present study develops a zero-trust approach called ZT-ACVRM or Zero-Trust Adaptive Compliance Verification and Risk Mitigation Framework. It provides a security architecture that is ideal for enterprise SaaS environments by combining various concepts including identity and continuous access monitoring, compliance checks, behavior analysis, threat intelligence, and adaptive authorization. Performance of the framework ZT-ACVRM was compared with WB-ZTAC and SAAM-ABA framework with the help of eight evaluation criteria. Performance of ZT-ACVRM was found to be superior and achieved accuracy of 98.4% access governance, 98.6% compliance adherence rate, 99.0% anomaly detection, 98.5% threat mitigation, 98.8% authorization decision, 98.7% trust evaluation, 98.3% risk assessment, and 99.0% overall security resilience. This clearly proves that the designed framework is indeed effective in improving the security of enterprise SaaS environment while remaining compliant and operationally efficient.

Comparative Effectiveness of OWASP WSTG and Top Ten in Web Security Audits

This study evaluates the comparative effectiveness of two widely adopted cybersecurity frameworks, the OWASP Top Ten (2021) and the OWASP Web Security Testing Guide (WSTG), in the context of web application security auditing. While the OWASP Top Ten is a standard for risk awareness, it lacks the technical granularity required for comprehensive testing, creating a gap between high-level risk identification and practical verification. To bridge this gap, this study proposes a structured integration through comparative mapping and empirical validation using real-world mitigation data. A procedural analysis combined with granularity evaluation was employed to map the ten OWASP risk categories to 102 technical verification units in the WSTG. The results reveal a 920% increase in testing granularity compared to the baseline Top Ten framework. Empirical validation conducted on a government subdomain (Instansi X) demonstrated that this integrated approach identified critical vulnerabilities, including Broken Access Control and Cryptographic Failures, which are often overlooked in high-level assessments. By implementing specific WSTG-based mitigation procedures, such as middleware authorization and secure communication protocols, identified risks were successfully remediated without disrupting production stability. This study contributes a validated framework that bridges the gap between conceptual risk and actionable technical verification. The findings indicate that while the OWASP Top Ten serves as a strategic reference, the WSTG is superior as a primary technical auditing framework. This integration enhances audit consistency, precision, and efficiency in evaluating modern web environments.

Slicing or Spoofing? Detecting and Containing Resource Hijacking in B5G Networks with Suspicious Traffic

Although 5G networks can virtually partition resources, the flexibility provided by slicing in B5G networks raises multi-tenant security concerns, such as coordinated hijacking of resources by spoofed identities. Traditional allocation models focus exclusively on performance and function in a purely reactive mode of post-anomaly detection. In this paper, we propose a proactive resource allocation model with a security-oriented approach, formulated as an integer linear programming (ILP) problem. The model incorporates easy-to-simulate suspicion variables into user-level functionalities, which narrow down multi-slice spoofing, reduce the need to throttle high-risk users, and enable the identification of coordinated attacks in real time, thanks to the conflict-detection variables. Experimental validation demonstrates that the framework proactively detects malicious actors, ensures resources are allocated fairly for legitimate uses, and maintains computational viability at the edge for orchestration.