arXiv subjects

Dikshant

Publications and source records attributed to Dikshant.

3 recordsLinked to original sources

arXiv2026

RDEx-CASK: Cauchy Mutation, Archive, and Stagnation Kick for RDEx-CSOP

We extend RDEx-CSOP with 3 changes that target stagnation & late-stage variance, plus minor parameter tuning. The second scale factor in the standard branch is sampled independently from a truncated Cauchy. A small feasible-only JADE-style archive (|A|_max = 50) is added & sampled with probability |A|/(|A|+|P|). Per-individual stagnation counter triggers, after 180 no-improvement generations, three local overrides on standard branch: pull toward the global best, lift the archive sampling floor to 0.65, & saturate CR to 0.95 when population success rate is below 0.10. The exploitation biased branch & every other RDEx component are left untouched. On CEC CSOP suite (D=30, 25 runs), RDEx-CASK is competitive with RDEx, UDE-III, & CL-SRDE in feasibility-aware quality & improves time-to-target on most problems.

cs.NE↗

arXiv2025

Precision over Noise: Tailoring S3 Public Access Detection to Reduce False Positives in Cloud Security Platforms

Excessive and spurious alert generation by cloud security solutions is a root cause of analyst fatigue and operational inefficiencies. In this study, the long-standing issue of false positives from publicly accessible alerts in Amazon S3, as generated by a licensed cloud-native security solution, is examined. In a simulated production test environment, which consisted of over 1,000 Amazon S3 buckets with diverse access configurations, it was discovered that over 80\% of the alerts generated by default rules were classified as false positives, thus demonstrating the severity of the detection issue. This severely impacted detection accuracy and generated a heavier workload for analysts due to redundant manual triage efforts. For addressing this problem, custom detection logic was created as an exercise of the native rule customization capabilities of the solution. A unified titled ``S3 Public Access Validation and Data Exposure'' was created in an effort to consolidate different forms of alerts into one, context-aware logic that systematically scans ACL configurations, bucket policies, indicators of public exposure, and the presence of sensitive data, and then marks only those S3 buckets that indeed denote security risk and are publicly exposed on the internet with no authentication. The results demonstrate a significant reduction in false positives, more precise alert fidelity, and significant time saving for security analysts, thus demonstrating an actionable and reproducible solution to enhance the accuracy of security alerting in compliance-focused cloud environments.

cs.CR↗

arXiv2025

Reducing False Positives with Active Behavioral Analysis for Cloud Security

Rule-based cloud security posture management (CSPM) solutions are known to produce a lot of false positives based on the limited contextual understanding and dependence on static heuristics testing. This paper introduces a validation-driven methodology that integrates active behavioral testing in cloud security posture management solution(s) to evaluate the exploitability of policy violations in real time. The proposed system employs lightweight and automated probes, built from open-source tools, validation scripts, and penetration testing test cases, to simulate adversarial attacks on misconfigured or vulnerable cloud assets without any impact to the cloud services or environment. For instance, cloud services may be flagged as publicly exposed and vulnerable despite being protected by access control layers, or secure policies, resulting in non-actionable alerts that consumes analysts time during manual validation. Through controlled experimentation in a reproducible AWS setup, we evaluated the reduction in false positive rates across various misconfiguration and vulnerable alerts. Our findings indicate an average reduction of 93\% in false positives. Furthermore, the framework demonstrates low latency performance. These results demonstrate a scalable method to improve detection accuracy and analyst productivity in large cloud environments. While our evaluation focuses on AWS, the architecture is modular and extensible to multi-cloud setups.

cs.CR↗