Ishan P.

Key Skills

Software

No software listed

Top Subject Matter

Cybersecurity intrusion detection and adversarial attack evaluation

Security operations alert triage

threat classification

Top Data Types

Text

Computer Code Programming

Top Task Types

Classification

Red Teaming

Freelancer Overview

Graduate Security Researcher (Cyber-Physical and Power System Lab) — detection workflow engineering and evaluation for a. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Security Onion, Suricata, and Wazuh. Education includes Master of Computer Science and Engineering, Texas A&M University Kingsville (2026) and Bachelor of Computer Science and Engineering, G D Goenka University (2021). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including Evaluation, Rati

Labeling Experience

Graduate Security Researcher - Cyber-Physical and Power System Lab

ClassificationRed Teaming

Graduate security researcher bridging cyber-physical power systems testing with SOC-style detection engineering to reduce operational noise and improve response speed. Built automated alert correlation and enrichment pipelines to support large-scale event processing and faster analyst triage. Designed real-time detection workflows and machine-learning-driven classification for smart grid and DER threat scenarios. • Engineered automated alert correlation/enrichment pipelines in Python and Bash to reduce false positives by 38% across 300,000+ events. • Architected sub-10-second MTTD detection workflows using Security Onion, Suricata, and custom signatures. • Integrated OPAL-RT hardware simulation with ML models for automated threat classification and alerting across seven attack types. • Coordinated red-team validation against adversarial patterns using MITRE ATT&CK mapping and researcher collaboration.

2024 - Present

Graduate Security Researcher (Cyber-Physical and Power System Lab) — detection workflow engineering and evaluation for attack classification

Text

Worked on building and validating detection workflows that required classifying and evaluating intrusion/attack events across multiple environments. Labeled outcomes implicitly as detection/false-positive vs correctly detected intrusions for tuning and performance tracking using adversarial and DER/smart grid scenarios. The effort focused on producing high-accuracy, low-latency detection signals that act as training/evaluation targets for ML-driven security classification pipelines. • Reduced false positive rate from 15% to 9.3% by engineering automated alert correlation and enrichment pipelines. • Targeted sub-10-second mean time to detect (MTTD) for network intrusions using Security Onion and Suricata. • Supported 7 attack classifications with 99.01% accuracy via ML-integrated threat classification and alerting. • Coordinated validation against 12+ adversarial patterns using MITRE ATT&CK mapping during red-team exercises.

2024 - Present

Security Engineer - Safeaeon Inc.

Classification

Security engineer for managed client environments, focusing on incident detection, triage automation, and threat hunting. Implemented alert correlation workflows and built repeatable processes to reduce response times while improving classification accuracy. Produced forensic investigation deliverables using threat-intelligence frameworks and log/telemetry analysis. • Decreased mean time to respond by 40% by implementing automated alert correlation workflows in Wazuh and QRadar with custom Python triage logic. • Triaged and investigated 500+ weekly alerts, using Suricata, CrowdStrike Falcon, and packet capture analysis for behavioral anomaly detection. • Delivered 100+ forensic investigation reports through deep-dive hunting and IOC/indicator of compromise analysis across platforms. • Engineered custom detection rules by analyzing 10M+ events to reduce false positives and tune correlation logic across multiple log sources.

2021 - 2023

Security Engineer — SOC alert triage and classification workflow improvements with automated correlation

TextClassification

Performed triage and investigation of security alerts to classify threats and support detection engineering that informs monitoring and training datasets. Used behavioral anomaly cues and indicator-of-compromise style investigation outputs to label alert outcomes and refine detection logic across many client environments. Produced forensic investigation findings aligned to MITRE ATT&CK techniques to support accurate threat classification and downstream risk decisions. • Achieved 99.2% accurate threat classification while triaging 500+ weekly security alerts using Suricata, CrowdStrike Falcon, and packet capture analysis. • Decreased mean time to respond by 40% by implementing automated alert correlation workflows in Wazuh and QRadar with Python triage/escalation logic. • Delivered 100+ forensic investigation reports by conducting deep-dive threat hunting and indicator analysis across event management and telemetry using MITRE ATT&CK. • Engineered custom detection rules that reduced false positives by 35% after analyzing 10M+ security events to tune correlation logic.

2021 - 2023

Education

Work History