Results for “detection”

Mosaic Augmentation for Detection and Segmentation

Automates detection engineering workflows in Google SecOps by extracting threat intelligence, generating detection opportunities, simulating attacker behavior with synthetic events, evaluating rule coverage, and creating new YARA-L 2.0 rules to close gaps.

Implements face detection and recognition with privacy-preserving features and bias mitigation

Build effective detection rules using Splunk Search Processing Language (SPL) correlation searches to identify security threats in SOC environments.

Deploys and configures host-based intrusion detection systems (Wazuh, OSSEC, AIDE) to monitor file integrity, system calls, and configuration changes across endpoints. Includes FIM policies, rootkit detection, custom alert rules, active response, and SIEM integration.

Design, implement, test, and maintain SIEM detection rules mapped to MITRE ATT&CK across Splunk, Elastic, and Sentinel platforms.

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

Detects weak reverse engineering and tampering protections in mobile apps (Android/iOS). Trigger on: root detection bypass, jailbreak detection bypass, Frida detection, debugger detection, anti-debugging, ptrace, sysctl, emulator detection, code obfuscation absent, debug symbols present, get-task-allow, ProGuard disabled, R8 disabled, string encryption, integrity check, file tampering, repackaging, dynamic instrumentation, runtime hook, Magisk hide, Magisk, frida-server, objection bypass, signing verification, apk resign. Covers MASVS-RESILIENCE-1/2/3/4.

NVIDIA's runtime safety framework for LLM applications. Features jailbreak detection, input/output validation, fact-checking, hallucination detection, PII filtering, toxicity detection. Uses Colang 2.0 DSL for programmable rails. Production-ready, runs on T4 GPU.

Deploy, debug, and operate the RTVI-CV 2D detection/tracking microservice and call its REST API for stream management, health checks, and metrics.

Develop precise YARA rules for malware detection by identifying unique byte patterns, strings, and behavioral indicators in executable files while minimizing false positives.

Trains, evaluates, exports, prunes, quantizes, retrains, and runs inference for OCDNet scene text detection models using TAO, detecting arbitrary-oriented text regions in natural images.

Analyze text with Azure AI Language service for sentiment, entities, key phrases, language detection, PII redaction, and healthcare NLP using the Python SDK.

Proactively hunt for threats by analyzing IOCs, detecting behavioral anomalies in telemetry, and prioritizing signals mapped to MITRE ATT&CK.

Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.

Detects the test platform (VSTest vs Microsoft.Testing.Platform) and test framework (MSTest, xUnit, NUnit, TUnit) from .NET project files.

Trains and fine-tunes vision models for object detection, image classification, and segmentation using Hugging Face Transformers on cloud GPUs, with automatic dataset validation and Hub persistence.

Train, evaluate, distill, quantize, export, and run inference for RT-DETR object detection models using NVIDIA TAO.

Detect C2 beaconing patterns in network traffic using frequency analysis, jitter detection, and domain reputation to identify compromised endpoints communicating with adversary infrastructure.

Hunt for Mimikatz execution using command-line patterns, LSASS access signatures, binary indicators, and in-memory detection of known modules.

Detect sophisticated cyber-physical attacks that modify PLC logic while spoofing sensor readings, covering PLC integrity monitoring, process anomaly detection, and multi-stage attack chain detection.

Skill for the Observability area of paddock. 105 symbols across 26 files.

Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.

Maps observed adversary behaviors, security alerts, and detection rules to MITRE ATT&CK techniques and sub-techniques to quantify detection coverage and guide control prioritization.

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Deploys deception technology including honeypots, honeytokens, and decoy systems to detect attackers who have bypassed perimeter defenses, providing high-fidelity alerts with near-zero false positive rates.

Detects lateral movement techniques including Pass-the-Hash, PsExec, WMI execution, RDP pivoting, and SMB-based spreading using SIEM correlation of Windows event logs, network flow data, and endpoint telemetry mapped to MITRE ATT&CK Lateral Movement (TA0008) techniques.

Analyzes network traffic captures and flow data to identify adversary activity during security incidents, including command-and-control communications, lateral movement, data exfiltration, and exploitation attempts.

Detect and triage infrastructure drift by comparing declared Terraform state against live cloud resources using scheduled pipelines and audit logs.

Detect container escape attempts using runtime security tools like Falco, Sysdig, and custom seccomp/audit rules.