Amitesh Jain AmiteshJ

Amitesh Jain ✨

Computer Engineering Student | Cybersecurity | AI & ML | Full-Stack Development | Data & Automation

🌐 Connect with Me

📌 Email: amiteshj2703@gmail.com

👨🏻‍💻 About Me

Hello! I'm Amitesh Jain, a final-year Computer Engineering student at A. P. Shah Institute of Technology (APSIT), Thane, affiliated with the University of Mumbai.

I'm interested in building practical systems at the intersection of Cybersecurity, Artificial Intelligence, Machine Learning, Data Analytics, and Software Development. My current work focuses on applying ML, graph-based algorithms, threat intelligence, and modern web technologies to real-world security problems.

I enjoy working across the complete development lifecycle — from understanding a problem and designing the architecture to developing APIs, machine-learning pipelines, dashboards, databases, and user-facing applications.

Currently, I'm working on SentinelX, my BE Major Project: an ML- and graph-based Security Operations Center (SOC) platform for advanced threat detection and intelligent incident investigation.

🏆 Achievements & Highlights

Important

Smart India Hackathon 2026 – Participated in the SIH 2026 screening/final-selection process with a cybersecurity-focused solution. Detailed result/status: Updated Soon
KPMG Assurance and Consulting Services LLP – Selected as an Academic Trainee / Intern in DT-Cyber Transformation (CTFR), 2026.
BE Semester 6 SGPA: 8.57.
Current CGPA / academic standing: Updated Soon.
Additional hackathons, competitions and rankings: Updated Soon.

🚀 Featured Projects

🛡️ SentinelX – ML & Graph-Based Security Operations Center

Overview: SentinelX is my BE Major Project focused on building a practical Security Operations Center (SOC) platform for advanced threat detection and intelligent incident investigation.

The system combines rule-based detection, machine-learning anomaly detection, graph-based alert correlation, threat intelligence, MITRE ATT&CK mapping, risk scoring, and RAG-based investigation assistance.

Key Areas:

🔍 Detection Engine – Rule-based detection for suspicious activities such as brute-force attempts, privilege escalation, malicious PowerShell activity, and malware indicators.
🤖 ML Anomaly Detection – Isolation Forest-based anomaly detection for identifying unusual telemetry.
🕸️ Graph-Based Correlation – BFS/DFS-based alert correlation for connecting related security events.
🎯 Risk Scoring – Risk evaluation using severity, asset criticality, IOC reputation, MITRE ATT&CK relevance, and confidence.
🧠 Threat Intelligence – Threat actor profiling, IOC analysis, and security intelligence workflows.
🔎 Threat Hunting Workspace – Search and filter raw telemetry by IP, port, protocol, and other event attributes.
⚙️ Dynamic Detection Rules – CRUD-based rule management with Sigma-inspired detection logic.
📚 RAG Investigation Assistant – Retrieval-Augmented Generation workflow for investigation support.
📊 SOC Dashboard – Centralized visibility into alerts, incidents, risk, intelligence, and investigations.

Tech Stack: Python • FastAPI • React • MongoDB • PostgreSQL • Redis • Scikit-learn • Graph Algorithms • RAG • Groq • MITRE ATT&CK • REST APIs

Repository: AmiteshJ/SentinelX

🛡️ CyberShield – Agentic AI Cybersecurity Assistant

Overview: CyberShield is a cybersecurity-focused solution developed around an agentic AI workflow for log analysis, threat investigation, risk scoring, evidence gathering, and response assistance.

Key Areas:

🤖 Agentic AI for cybersecurity investigation
📜 Log analysis and threat investigation
🎯 Risk scoring and evidence-based findings
🦠 Malware and phishing detection workflows
🕵️ Vulnerability and threat monitoring
🍯 Honeypot / security log analysis
💬 Security policy chatbot
📊 Investigation and security dashboards

Status: Project details and implementation documentation Updated Soon.

🍲 Recipe Lens – AI-Based Food Recognition & Voice Cooking Assistant

Overview: Recipe Lens is an academic project focused on combining computer vision and voice interaction to support cooking workflows.

The project includes VisionChef for image-based food recognition and VoiceChef for voice-assisted cooking interactions.

Key Areas:

🖼️ Image-based food recognition
🎙️ Voice-assisted cooking workflow
🥘 Food / sabzi classification
🤖 Deep learning model experimentation
📱 User-friendly cooking interface

Tech Stack: Python • TensorFlow/Keras • YOLO • Computer Vision • Voice Processing

Status: Detailed deployment/demo information Updated Soon.

🤖 MediMate

Overview: AI-oriented healthcare assistance project designed to explore intelligent information support and user interaction.