Official repo of "EndoBoost: a plug-and-play module for false positive suppression during computer-aided polyp detection in real-world colonoscopy (with dataset)"

ML-based SOC alert triage system using Random Forest to auto-classify alerts as True/False Positive — reducing analyst workload with real-time confidence scoring and live dashboard.

I-powered SIEM alert triage tool — MITRE ATT&CK mapping, IOC extraction, false positive reduction, and investigation playbooks using Claude AI

Heimdall-SecurityV2 is a DevSecOps security validation framework that combines Semgrep SAST, LLM-based exploitability reasoning, safe DAST validation, and CI/CD policy reporting to reduce false positives.

A Claude Skill that adjudicates sanctions / PEP / adverse-media screening hits deterministically. Token-efficient tiered design (parse → cheap FP rules → identifier corroboration → targeted multilingual research). Handles 9+ naming conventions, transliterations, and historical place names.

Synthetic SOC / Blue Team credential access detection lab with MITRE ATT&CK mapping, SIEM detection logic, alert triage notes, false-positive handling, detection tuning, and dashboard reporting.

🚀 SmartAlert AI - Production-ready adaptive ML system for intelligent incident prediction. Achieves 44% F1-Score on challenging datasets with 75% false alarm reduction. Features 118 sophisticated features and real-time adaptive learning.

GUARDIUM is an intelligent Wazuh rule optimization framework designed to reduce false positives, improve alert accuracy, and assist SOC teams in maintaining high-quality SIEM detections. GUARDIUM combines rule analysis, threat context, and Large Language Models (LLMs) to automatically evaluate, explain, and optimize Wazuh rules.

Python security gate with intelligent ML scoring that reduces false positives by 95%. Orchestrates Bandit, pip-audit, and Semgrep into a unified CI/CD pipeline. Includes baseline management, policy enforcement, and explainable predictions. Production-ready with comprehensive tests.

This repository contains the code for using the Negative Sigmoid loss, a deep learning loss function designed to penalize false positive segmentations in negative patches

Analytical portfolio demonstrating transaction monitoring, judgment-based alert review, and Excel-driven risk analysis across fraud, AML, and KYC workflows, with a focus on regulator-safe decisioning and operational consistency.

Lightweight network based host & service watchdog - very smart check definitions to cut down on false positives. Eight different nest-able, AND/OR-able health check types. Email, Signal and Webhook alerts. SOC/NOC wallboard, SLA tracking and more.

Memory-augmented SOC alert-triage research prototype with reproducible CIC-IDS2017 validation, leakage-aware controls, and publication-ready figures.

🔍 Analyze transaction data to identify fraud risks, streamline alert reviews, and ensure compliance in AML and KYC contexts using Excel-driven techniques.