Engineering Intelligence to Defend Critical Digital Infrastructure

Cyber Threat Intelligence • Machine Learning Security • Cloud & Infrastructure Defense

Mission & Expertise

I design intelligent security systems that proactively detect, predict, and neutralize cyber threats targeting cloud platforms, energy systems, and data-driven infrastructure.

My work focuses on applying machine learning and large-scale data engineering to reduce systemic cyber risk, strengthen national resilience, and protect critical digital assets.

Proactive Detection

Early threat identification before damage occurs

Infrastructure Defense

Hardening critical cloud and data systems

Automated Response

Intelligent systems that act on threats in real-time

Systemic Risk Reduction

Reducing cyber risk across entire infrastructure

2018

Foundation in Data Engineering

Built enterprise-scale ETL pipelines and analytics systems

2020

Cloud & Infrastructure Focus

Specialized in cloud security architecture and zero-trust systems

2022

Machine Learning Security

Developed ML-driven threat detection and predictive models

2024

Cyber Threat Intelligence

Leading research in proactive ransomware and breach prevention

Protected Feature

Cyber Threat Intelligence Lab

Real-time threat monitoring system demonstrating MITRE ATT&CK mapping, severity analysis, and infrastructure-specific threat intelligence

Authentication Required

The Threat Intelligence Lab is a protected feature. Please log in to access real-time threat data, MITRE ATT&CK mappings, and advanced filtering capabilities.

Machine Learning for Cybersecurity

Applying advanced machine learning techniques to detect, predict, and prevent cyber threats at scale across complex enterprise environments

Behavioral Anomaly Detection

Identify deviations from baseline user and system behavior patterns to detect insider threats and account compromise

Unsupervised learningTime-series analysisUEBA algorithms

Predictive Threat Modeling

Forecast potential attack vectors and vulnerabilities before exploitation using historical threat data and risk patterns

Ensemble methodsBayesian networksRisk scoring

Feature Engineering for SIEM

Transform raw security logs into actionable features that improve detection accuracy and reduce alert noise

Log parsingStatistical featuresTemporal patterns

False Positive Reduction

Apply machine learning to distinguish genuine threats from benign anomalies at scale across enterprise environments

Classification modelsPrecision tuningFeedback loops

ML Security Pipeline Architecture

📊

Data Collection

SIEM logs, network traffic, endpoint telemetry

🔍

Feature Extraction

Behavioral patterns, statistical metrics

🧠

Model Training

Supervised & unsupervised algorithms

⚠️

Threat Detection

Real-time anomaly scoring

🚨

Alert Generation

Prioritized, actionable intelligence

⚡

Response Automation

Orchestrated incident response

Pseudocode Example: Anomaly Detection

# Behavioral Anomaly Detection Pipeline
function detectAnomalies(user_logs):
    # Extract behavioral features
    features = extractFeatures(user_logs)
    baseline = calculateBaseline(historical_data)
    
    # Calculate anomaly score
    for activity in features:
        score = computeDeviation(activity, baseline)
        risk_level = classifyRisk(score)
        
        if risk_level > THRESHOLD:
            alert = generateAlert(activity, score)
            prioritize(alert, context)
            triggerResponse(alert)
    
    return threat_intelligence

95% Detection Rate

High accuracy in identifying genuine threats while minimizing false positives

60% Faster Response

Automated threat detection and prioritization reduces mean time to respond

Scale to Millions

Process and analyze millions of security events per day across enterprise infrastructure

Cloud & Infrastructure Security

Architecting secure, resilient cloud infrastructure across AWS, Azure, and GCP with defense-in-depth strategies and zero-trust principles

Secure ETL Pipelines

End-to-end encryption, secure data transformation, and compliance-ready data workflows

Azure Data FactoryAWS GlueApache Spark

Zero-Trust Architecture

Identity-based access controls, continuous verification, and micro-segmentation

IAM PoliciesNetwork PoliciesService Mesh

IAM Hardening

Least privilege enforcement, role-based access control, and credential management

AWS IAMAzure ADGCP IAM

Automated Response

Real-time threat mitigation, automated remediation, and orchestrated incident response

LambdaEventBridgeLogic Apps

Data Loss Prevention

Encryption at rest and in transit, access logging, and anomalous data exfiltration detection

KMSCloudTrailAzure Monitor

Security Posture: Before & After

Vulnerabilities

✗Overly permissive IAM policies with wildcard access
✗Unencrypted data in transit and at rest
✗No centralized logging or monitoring
✗Public S3 buckets with sensitive data
✗No automated incident response

Risk Level

Data ExposureCritical

Unauthorized AccessHigh

Breach DetectionPoor

Zero-Trust Architecture Example

# Zero-Trust IAM Policy Structure
{
  "Version": "2012-10-17",
  "Statement": [{
    "Effect": "Allow",
    "Action": ["s3:GetObject"],
    "Resource": "arn:aws:s3:::secure-bucket/data/*",
    "Condition": {
      "IpAddress": {"aws:SourceIp": "10.0.0.0/16"},
      "StringEquals": {"aws:PrincipalOrgID": "o-xxxxx"},
      "Bool": {"aws:SecureTransport": "true"}
    }
  }]
}

Research & Publications

Original research advancing the state of cybersecurity through machine learning, threat intelligence, and infrastructure defense methodologies

Machine Learning-Based Early Detection of Ransomware in Cloud Environments

In Progress2024

Abstract

This research investigates the application of supervised and unsupervised learning algorithms to identify ransomware behavior patterns before encryption occurs. By analyzing file system access patterns, network traffic anomalies, and process execution sequences, we develop a multi-layered detection system capable of identifying ransomware signatures with 95% accuracy while maintaining low false positive rates in production cloud environments.

Keywords

Ransomware DetectionMachine LearningCloud SecurityThreat Intelligence

Behavioral Anomaly Detection in Critical Infrastructure SCADA Systems

Under Review2024

Abstract

Energy sector SCADA systems represent high-value targets for nation-state adversaries and ransomware operators. This paper presents a novel anomaly detection framework using time-series analysis and behavioral modeling to identify unauthorized access and malicious control commands in industrial control systems. The approach leverages domain-specific knowledge of SCADA protocols and operational technology to reduce alert fatigue while improving threat visibility.

Keywords

SCADA SecurityCritical InfrastructureAnomaly DetectionOT Security

Zero-Trust Architecture Patterns for Multi-Cloud Data Engineering Pipelines

Published2023

Abstract

As organizations adopt multi-cloud strategies, securing data pipelines across AWS, Azure, and GCP presents significant challenges. This research examines zero-trust security patterns for ETL workflows, including identity-based access control, data encryption at rest and in transit, and continuous authentication. We present implementation frameworks that reduce attack surface while maintaining operational efficiency for large-scale data engineering operations.

Keywords

Zero-TrustData EngineeringMulti-CloudETL Security

Predictive Threat Intelligence: Forecasting Cyber Attack Vectors Using Historical Data

In Progress2024

Abstract

Traditional threat intelligence is reactive, analyzing attacks after they occur. This research develops predictive models that forecast emerging attack vectors by analyzing historical breach data, vulnerability disclosures, and adversary tactics. Using ensemble learning techniques and threat actor profiling, we demonstrate the ability to anticipate attack patterns 30-60 days before widespread exploitation, enabling proactive defensive measures.

Keywords

Threat IntelligencePredictive AnalyticsCyber ForecastingMITRE ATT&CK

Research focused on advancing practical cybersecurity solutions for critical infrastructure and national security applications

National Interest

National & Societal Impact

Cyberattacks against cloud platforms, energy systems, and financial infrastructure pose direct risks to economic stability and public safety.

My work contributes to national cybersecurity resilience by enabling early threat detection, reducing breach impact, and supporting secure digital infrastructure at scale.

Critical Infrastructure Protection

Energy systems, water treatment facilities, and transportation networks depend on secure digital infrastructure. Cyber incidents in these sectors can cause cascading failures affecting millions.

Economic Stability

Financial institutions and payment systems process trillions in transactions. Breaches undermine consumer confidence and threaten market stability.

Public Safety

Healthcare systems, emergency services, and public utilities require uninterrupted operation. Ransomware attacks on hospitals directly endanger lives.

National Security

Government agencies and defense contractors manage sensitive information. Unauthorized access compromises strategic capabilities and national interests.

Contribution to National Security

Early Detection

Identifying threats before damage occurs through predictive intelligence and behavioral analysis

Risk Reduction

Reducing systemic vulnerabilities across cloud platforms and critical infrastructure

Scale & Resilience

Building systems that protect infrastructure serving millions of citizens and businesses

This work directly addresses requirements outlined in the Cybersecurity & Infrastructure Security Agency (CISA) strategic framework and aligns with national priorities for infrastructure protection and cyber resilience.

Projects & Case Studies

Real-world implementations demonstrating technical expertise and national-interest impact across threat detection, infrastructure defense, and data protection

ML-Based Ransomware Early Detection System

Problem

Traditional antivirus solutions detect ransomware after encryption begins, resulting in significant data loss and operational disruption.

Threat Model

Ransomware campaigns targeting healthcare and energy sectors

Technical Approach

Developed supervised learning model analyzing file system behavior, process execution patterns, and network traffic to identify ransomware signatures before encryption occurs.

Tools & Technologies

PythonScikit-learnAWS SageMakerCloudWatch

Outcome

Achieved 95% detection accuracy with 0.2% false positive rate. Reduced mean time to detection from hours to minutes, preventing data loss in production environments.

Protects critical healthcare data and energy infrastructure from ransomware attacks

Zero-Trust Cloud Breach Prevention Pipeline

Problem

Cloud misconfigurations and overly permissive IAM policies create attack vectors for data exfiltration and unauthorized access.

Threat Model

Insider threats and external attackers exploiting weak access controls

Technical Approach

Architected end-to-end security pipeline implementing least-privilege IAM, continuous authentication, network segmentation, and real-time configuration monitoring across AWS and Azure.

Tools & Technologies

TerraformAWS IAMAzure ADCloudTrailSplunk

Outcome

Reduced attack surface by 80%, eliminated public data exposure, and implemented automated remediation for policy violations. Achieved SOC 2 compliance.

Secures financial transaction data and prevents unauthorized access to sensitive systems

Threat Intelligence Aggregation & Analysis Engine

Problem

Security teams struggle to correlate threat intelligence from multiple sources, resulting in delayed response to emerging threats.

Threat Model

Zero-day vulnerabilities and advanced persistent threats

Technical Approach

Built automated threat intelligence platform aggregating data from OSINT sources, vulnerability databases, and dark web monitoring. Applied NLP and entity extraction to identify relevant threats.

Tools & Technologies

PythonApache KafkaElasticsearchMITRE ATT&CKSTIX/TAXII

Outcome

Reduced threat intelligence processing time from days to hours. Enabled proactive patching of critical vulnerabilities before widespread exploitation.

Enables early warning of cyber threats affecting critical infrastructure and enterprise systems

Secure Data Engineering Pipeline for PII Protection

Problem

ETL pipelines processing personally identifiable information (PII) lacked encryption, access controls, and audit logging.

Threat Model

Data breaches and regulatory compliance violations

Technical Approach

Designed secure-by-default ETL architecture with end-to-end encryption, column-level access control, data masking, and comprehensive audit trails using Azure Data Factory and Databricks.

Tools & Technologies

Azure Data FactoryDatabricksAzure Key VaultDelta Lake

Outcome

Achieved GDPR and CCPA compliance. Processed 10M+ records daily with zero security incidents. Implemented automated PII detection and classification.

Protects consumer data privacy and ensures regulatory compliance for financial services

Automated Security Incident Response Platform

Problem

Manual incident response processes resulted in slow threat containment and inconsistent remediation procedures.

Threat Model

Active breaches requiring immediate containment

Technical Approach

Developed orchestration platform integrating SIEM alerts with automated response playbooks. Implemented threat scoring, stakeholder notification, and forensic data collection.

Tools & Technologies

PythonAWS LambdaEventBridgeSNSSecurity Hub

Outcome

Reduced mean time to response (MTTR) from 4 hours to 15 minutes. Automated 70% of common incident response tasks.

Minimizes breach impact and prevents lateral movement in enterprise networks

SCADA System Anomaly Detection for Energy Infrastructure

Problem

Industrial control systems lack visibility into unauthorized access and malicious control commands.

Threat Model

Nation-state attacks and sabotage of critical energy infrastructure

Technical Approach

Developed time-series anomaly detection system monitoring SCADA protocol traffic and operational parameters. Used unsupervised learning to establish baseline behavior.

Tools & Technologies

PythonTensorFlowICS protocolsGrafanaInfluxDB

Outcome

Detected unauthorized access attempts and anomalous control commands with 92% accuracy. Prevented potential equipment damage and service disruptions.

Protects energy grid stability and prevents disruptions affecting millions of consumers

Professional Contact

Open to collaboration on national-interest cybersecurity initiatives, advanced research, and infrastructure defense engineering.

Send a Message

nitesh.jha@example.com

linkedin.com/in/niteshjha

Areas of Collaboration

•Critical infrastructure cybersecurity research and implementation
•Machine learning applications for threat detection and prevention
•Zero-trust architecture and cloud security engineering
•Advanced threat intelligence and MITRE ATT&CK framework development
•National security initiatives and government cybersecurity projects

All inquiries regarding cybersecurity research, consulting, or collaboration opportunities are welcome