Python SOC detection pipeline for identifying rate anomalies and DoS indicators from HTTP and TCP telemetry.
Modular cybersecurity suite with 10 professional security tools (CLI & Web GUI), including vulnerability scanning, threat intelligence, intrusion detection and network analysis.
ML-based DoS/DDoS detection using Random Forest with real-time GUI and auto iptables IP blocking | Python · ML · Network Security
Secure P2P messaging prototype: E2E (ECDH+AES-GCM), DoS detection, injection prevention
A hybrid Python/C Network Intrusion Detection & Prevention System (NIDS/NIPS). Features multi-threaded L2-L7 analysis, C-accelerated DPI, dynamic IP isolation, and an integrated Docker observability stack (Grafana, Loki, Promtail) with Dashboards as Code.
This mini-lab demonstrates my ability to work with Wireshark and Docker in a controlled environment. It includes network traffic captures, automated analysis scripts, and visualizations showing ARP anomalies and traffic spikes. A concise portfolio project showcasing practical skills in network monitoring and basic attack detection.
Protocol Based Deep Intrusion Detection (PB-DID): stacked-LSTM classifier for Normal/DoS/DDoS network traffic on UNSW-NB15 + Bot-IoT, with a Flask analysis dashboard. B.Tech major project.
Random Forest classifier detecting DoS attacks (GoldenEye, Slowloris) on CIC-IDS2017 network traffic — 99.99% accuracy with SMOTE balancing
Python-based intelligent firewall with Intrusion Detection System (IDS), DoS detection, port scan detection, brute-force protection, real-time packet monitoring, and Flask dashboard.
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