Anvesh Raju Vishwaraju
SOC Analyst · Detection Engineer · Threat Intelligence
anvesh65422@gmail.com +91 79812 93129 linkedin.com/in/arv007 github.com/its-me-anvesh-var Hyderabad, India

Cybersecurity professional with dual postgraduate degrees in Cybersecurity (UNC Charlotte, USA) and AI (University of Hyderabad). Hands-on across SOC operations, detection engineering, threat intelligence, and cloud security. Built a production-grade RAG-based cybersecurity assistant and a 7-project GitHub security portfolio spanning SIEM, adversarial ML, and BFSI threat intel. Holds CompTIA Security+, eJPTv2, AWS CCP, and CASA-APIsec certifications. Currently seeking SOC Analyst, SIEM Engineer, and Threat Intelligence roles in Hyderabad.

Experience
Assistant Professor — CSE (AI&ML & Cybersecurity)
Malla Reddy College of Engineering · Hyderabad, India
Apr 2026 — Present
  • Teaching NLP, Database Security, SIEM tools, and EDR/SOC L1 content to undergraduate students in AI&ML and Cybersecurity programs.
  • Designed hands-on lab curriculum covering Wazuh deployment, Splunk SPL queries, and MITRE ATT&CK threat mapping.
  • Delivered guest lectures across colleges in Hyderabad on Detection Engineering and AI applications in cybersecurity.
Security Operations Support Analyst (Independent)
Freelance & Independent Research · Hyderabad, India
Jan 2026 — Mar 2026
  • Conducted BFSI-focused threat intelligence research — IOC enrichment, threat actor profiling, and India-specific threat landscape reporting using OTX AlienVault, VirusTotal, and Shodan.
  • Built and published 7 open-source security projects covering SOC home lab, adversarial ML, RAG-based threat assistant, and LLM-powered threat intel summariser.
  • Provided freelance Wazuh SIEM setup, Splunk detection rule development, and security policy drafting services to SMB clients.
AI Security Researcher — Adversarial ML
IDRBT (Institute for Development & Research in Banking Technology, RBI) · Hyderabad
2023 — 2024
  • Thesis: Adversarial Machine Learning in Cybersecurity — BFSI Sector Focus. Researched FGSM and PGD adversarial attacks on neural networks with adversarial training defences.
  • Investigated practical implications of adversarial ML for fraud detection and anomaly detection models deployed in Indian banking infrastructure.
  • Delivered research findings as part of MTech AI dissertation at University of Hyderabad.
Teaching Assistant & Technical Support
UNC Charlotte · Charlotte, NC, USA
Aug 2022 — Dec 2023
  • Supported graduate-level cybersecurity coursework as Teaching Assistant — assisted students with lab assignments covering network security, cryptography, and incident response.
  • Provided technical support for university systems while completing MS in Cybersecurity.
Education
M.S. Cybersecurity
University of North Carolina at Charlotte (UNC Charlotte) — USA
Focus: Network Security, Digital Forensics, Incident Response, Cryptography
Aug 2022 — Dec 2023
M.Tech Artificial Intelligence
University of Hyderabad — India
Thesis: Adversarial ML in Security — BFSI Sector Focus (in collaboration with IDRBT, RBI)
2022 — 2024
B.Tech Computer Science Engineering
Vasavi College of Engineering — Hyderabad, India
2016 — 2020
Certifications
🛡️
CompTIA Security+
Valid: Feb 2027
⚔️
eJPTv2 — eLearnSecurity
Valid: Jul 2027
☁️
AWS Cloud Practitioner
Valid: Sep 2026
🔐
CASA-APIsec
Jan 2025
🕵️
TCM Security: OSINT
Jun 2024
💻
TCM: Privilege Escalation
Jun 2024
🎯
TryHackMe: Jr. Pen Tester
Completed
Key Projects
Cybersecurity RAG Assistant
Production-grade RAG pipeline (~1,500 lines) using LangChain, Claude API, ChromaDB, and Streamlit. Achieves 85% retrieval precision over a cybersecurity knowledge base with semantic search.
PythonLangChainClaude APIChromaDBStreamlitRAG
SOC Home Lab
Full-stack home SOC environment with Wazuh HIDS, Splunk, Suricata IDS, and Active Directory. Includes MITRE ATT&CK mapped detection rules and incident response playbooks.
WazuhSplunkSuricataActive DirectoryMITRE ATT&CK
ML SOC Alert Classifier
Random Forest + SMOTE model to classify SOC alerts as true/false positive, reducing analyst fatigue. Flask API for real-time inference integration.
scikit-learnSMOTEFlaskPython
BFSI Threat Intelligence Platform
Automated IOC enrichment pipeline with OTX AlienVault, VirusTotal, and Shodan. Generates TLP-classified India-focused BFSI threat landscape reports.
PythonOTX AlienVaultThreat IntelBFSI
Adversarial ML Security Research
FGSM and PGD adversarial attack simulation on neural networks with adversarial training defence — research-grade PyTorch implementation for BFSI fraud detection context.
PyTorchFGSMPGDAdversarial ML
Technical Skills
SIEM & Monitoring
Splunk (SPL, dashboards), Wazuh, Azure Sentinel, Suricata
Threat Detection & IR
MITRE ATT&CK, Sigma, YARA, IOC enrichment, NIST SP 800-61
Threat Intelligence
ThreatConnect, MISP, OTX AlienVault, VirusTotal, Shodan
IAM & Identity
Active Directory, Azure AD / Entra ID, SAML 2.0, OAuth 2.0, MFA, PIM
Cloud Security
AWS IAM, CloudTrail, GuardDuty, S3, VPC, Azure baseline
Pen Testing & API
Metasploit, Burp Suite, OWASP Top 10, OWASP API Top 10
Scripting
Python, PowerShell, Bash, Flask, FastAPI, scikit-learn
Frameworks
NIST CSF, NIST SP 800-61, CIS Controls, ISO 27001