“I’m not someone who just configures firewalls. I am a systems thinker who combines deep technical curiosity with strategic awareness, which is rare. I’d thrive in roles that let me investigate, break, and rebuild secure systems, not just operate them.” – my personal assessment from ChatGPT 4o.
I am recent cybersecurity graduate from Eötvös Loránd University (ELTE). My focus areas include threat analysis, cryptography, zero-knowledge proofs (ZKP), and secure multiparty computation (MPC) in federated learning (FL). My academic work includes designing anomaly detection pipelines, developing privacy-preserving protocols, and research publication on Byzantine-resilient federated learning.
I previously gained industry experience as a Backend Engineer and System Operations Engineer, where I worked on software development, infrastructure automation, DevSecOps practices, and systems monitoring and logging. I am now seeking full-time roles or relevant projects in areas such as Secure Software Development, Security Engineering, Cryptography, Blockchain, Threat Analysis, or DevSecOps.
Experience
Experience
Budapest, HU
Eötvös Loránd University (ELTE)
2024 – Present
Research Asistant
Designed and deployed a real-time multimodal anomaly detection pipeline for critical infrastructure cybersecurity, using IoMT datasets. The system ingests real-time metrics via Apache Kafka, processes anomalies in Spark, stores time-series data in InfluxDB, and visualizes threat indicators in Grafana. Built as a fully containerized architecture (Docker, Kafka Manager, ZooKeeper), it delivers actionable alerts for both real-time and retrospective threat analysis.
Built a microservices architecture on AWS with Terraform and Kubernetes, integrated with centralized logging, access logging, and audit trails to support anomaly detection and log ingestion (into SIEM with Spark), and automated build/test/deploy with GitHub Actions and configured role-based access controls (RBAC) to secure deployments.
Performed simulations of advanced attack vectors (Byzantine faults) on distributed machine learning (ML) systems to analyze anomaly patterns and inform the design of threat mitigation techniques, published as "Byzantine-Resilient Federated Learning: Evaluating MPC Approaches".
Conducted development and evaluation of a secure aggregation scheme, integrated with secure multiparty computation (MPC) for privacy-preserving federated learning (FL) using Human Activity Recognition (HAR) dataset to improve data security and mitigate cyber attacks.
Developed a Zero-Knowledge Proof (ZKP)-based smart contract for verifiable threshold secret sharing on the Ethereum EVM, which enables secure distribution of confidential data across multiple parties with cryptographic verification of each share, without exposing the complete secret
Budapest, HU
Eötvös Loránd University (ELTE)
2024 – Present
Research Asistant
Designed and deployed a real-time multimodal anomaly detection pipeline for critical infrastructure cybersecurity, using IoMT datasets. The system ingests real-time metrics via Apache Kafka, processes anomalies in Spark, stores time-series data in InfluxDB, and visualizes threat indicators in Grafana. Built as a fully containerized architecture (Docker, Kafka Manager, ZooKeeper), it delivers actionable alerts for both real-time and retrospective threat analysis.
Built a microservices architecture on AWS with Terraform and Kubernetes, integrated with centralized logging, access logging, and audit trails to support anomaly detection and log ingestion (into SIEM with Spark), and automated build/test/deploy with GitHub Actions and configured role-based access controls (RBAC) to secure deployments.
Performed simulations of advanced attack vectors (Byzantine faults) on distributed machine learning (ML) systems to analyze anomaly patterns and inform the design of threat mitigation techniques, published as "Byzantine-Resilient Federated Learning: Evaluating MPC Approaches".
Conducted development and evaluation of a secure aggregation scheme, integrated with secure multiparty computation (MPC) for privacy-preserving federated learning (FL) using Human Activity Recognition (HAR) dataset to improve data security and mitigate cyber attacks.
Developed a Zero-Knowledge Proof (ZKP)-based smart contract for verifiable threshold secret sharing on the Ethereum EVM, which enables secure distribution of confidential data across multiple parties with cryptographic verification of each share, without exposing the complete secret
Surabaya, ID
Trustmedis.com
2020 – 2023
Backend Software Engineer
Developed and monitored containerized backend microservices (Laravel, NodeJS) deployed via cloud platforms, integrating real-time observability through Sentry.io and custom dashboards with alert rules to detect anomalies, latency spikes, availability and security issues.
Reviewed and secured codebases by following best practices in user authentication/role handling, input validation, error handling, and mitigating risks from security perspective such as hardcoded secrets or unsafe dependencies.
Implemented CI/CD pipelines using Python scripting and GitLab for improving deployment reliability and reducing release friction across environments.
Collaborated with QA and frontend teams to standardize logging formats and API response schemas, improving traceability and enabling streamlined ingestion into centralized monitoring pipelines.
Partnered with DevOps to optimize deployment workflows, resource allocation, and cloud infrastructure usage—reducing operational costs and improving reliability.
Participated in weekly retrospectives with engineering teams to prioritize fixes, share ideas for improvements, and provide solutions for technical and business issues.
Surabaya, ID
Trustmedis.com
2020 – 2023
Backend Software Engineer
Developed and monitored containerized backend microservices (Laravel, NodeJS) deployed via cloud platforms, integrating real-time observability through Sentry.io and custom dashboards with alert rules to detect anomalies, latency spikes, availability and security issues.
Reviewed and secured codebases by following best practices in user authentication/role handling, input validation, error handling, and mitigating risks from security perspective such as hardcoded secrets or unsafe dependencies.
Implemented CI/CD pipelines using Python scripting and GitLab for improving deployment reliability and reducing release friction across environments.
Collaborated with QA and frontend teams to standardize logging formats and API response schemas, improving traceability and enabling streamlined ingestion into centralized monitoring pipelines.
Partnered with DevOps to optimize deployment workflows, resource allocation, and cloud infrastructure usage—reducing operational costs and improving reliability.
Participated in weekly retrospectives with engineering teams to prioritize fixes, share ideas for improvements, and provide solutions for technical and business issues.
Jakarta, ID
KMK Online (BBM Messenger & Vidio.com)
2018 – 2019
System Operations Engineer
Designed and automated real-time observability on the cloud infrastructure using Datadog APM, Elasticsearch, and Grafana to monitor production systems, track metrics (CPU, memory, latency), and generate alerts for anomaly detection and incident response.
Investigated abnormal system behavior (e.g., resource exhaustion, instance bottlenecks) by correlating telemetry data and scripts automation for diagnosis using Bash/Python to improve detection and response times.
Built and maintained alerting pipelines with Grafana and Datadog, integrating escalation workflows through Opsgenie to support 24/7 on-call incident triage.
Partnered with DevOps teams to scope and implement enhanced telemetry (audit trails, user permission logs) feeding into automated alert playbooks in Ansible to achieve incident response readiness.
Automated data collection and reporting pipelines using Ansible and Bash/Python scripting to accelerate incident investigations, and developed unified dashboards that aggregated metrics and logs from multiple sources.
Jakarta, ID
KMK Online (BBM Messenger & Vidio.com)
2018 – 2019
System Operations Engineer
Designed and automated real-time observability on the cloud infrastructure using Datadog APM, Elasticsearch, and Grafana to monitor production systems, track metrics (CPU, memory, latency), and generate alerts for anomaly detection and incident response.
Investigated abnormal system behavior (e.g., resource exhaustion, instance bottlenecks) by correlating telemetry data and scripts automation for diagnosis using Bash/Python to improve detection and response times.
Built and maintained alerting pipelines with Grafana and Datadog, integrating escalation workflows through Opsgenie to support 24/7 on-call incident triage.
Partnered with DevOps teams to scope and implement enhanced telemetry (audit trails, user permission logs) feeding into automated alert playbooks in Ansible to achieve incident response readiness.
Automated data collection and reporting pipelines using Ansible and Bash/Python scripting to accelerate incident investigations, and developed unified dashboards that aggregated metrics and logs from multiple sources.
Education
Education
Budapest, HU
2023 – 2025
Master of Computer Science in Cybersecurity
Eötvös Loránd University (ELTE)
Grade: 4.7 out of 5 (Excellent)
Budapest, HU
2023 – 2025
Master of Computer Science in Cybersecurity
Eötvös Loránd University (ELTE)
Grade: 4.7 out of 5 (Excellent)
Surabaya, ID
2013 – 2018
Bachelor of Applied Science in Informatics
Politeknik Elektronika Negeri Surabaya (PENS)
Grade: 3.5 out of 4 (Cum laude)
Surabaya, ID
2013 – 2018
Bachelor of Applied Science in Informatics
Politeknik Elektronika Negeri Surabaya (PENS)
Grade: 3.5 out of 4 (Cum laude)
Certifications
Certifications
AI Summer School, 2025
ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI
AI Summer School, 2025
ENFIELD: European Lighthouse to Manifest Trustworthy and Green AI
AWS Academy Cloud Architecting, 2023
AWS Academy Graduate
AWS Academy Cloud Architecting, 2023
AWS Academy Graduate
Machine Learning with Python, 2019
Cognitive Class
Machine Learning with Python, 2019
Cognitive Class
Python for Data Science, 2019
Cognitive Class
Python for Data Science, 2019
Cognitive Class