Listing active repositories and system architectures. Analyzing engineering depth, technical approaches,
and core implementation details.
ID: 001
AI-Based Next Generation Firewall (NGFW)
Activev2.4.0
error Problem Statement
Traditional firewalls struggle to detect zero-day polymorphic malware and encrypted command-and-control traffic in real-time.
zero-day polymorphic malware and encrypted command-and-control traffic in real-time.
build Technical Approach
Built a production-style AI-driven NGFW operating inline using Linux raw sockets. Implemented stateful flow reconstruction and early-flow ML classification for real-time threat detection. Integrated encrypted traffic fingerprinting (JA3), Suricata IDS, and designed automated SOAR-based containment workflows.
filtering. Integrated a heuristic analysis engine that scores connection entropy and
packet timing to identify anomalies without decryption.
Static routing protocols in 5G networks cannot adapt quickly enough to micro-bursts
of traffic, leading to localized congestion and latency spikes for critical
services.
build Technical Approach
Developed a Multi-Agent Reinforcement Learning (MARL) system. Agents deployed on
edge nodes observe local queue lengths and collaboratively decide on routing updates
to minimize global latency.
Core Technologies
Python
(PyTorch)DockerGrafanaKubernetes
hub[ NETWORK_TOPOLOGY ]
(Node A) -- 10ms --> (Node B)
| ^
| |
15ms 5ms
| |
v |
(Node C) -- 20ms --> (Node D)
High false-positive rates in signature-based IDS fatigue security analysts, causing
real threats to be overlooked amidst the noise of alerts.
build Technical Approach
Utilized Long Short-Term Memory (LSTM) recurrent neural networks trained on a
baseline of 'normal' business traffic. The model flags deviations in sequence
patterns rather than matching static signatures.