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.

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.

🚀 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.

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.

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.