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Ketan.Dav

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SYSTEM_STATUS: ONLINE
update LAST_DEPLOY: 24h AGO

// SYSTEM_PROJECTS

Listing active repositories and system architectures. Analyzing engineering depth, technical approaches, and core implementation details.

ID: 001

SMART NEXT-GEN FIREWALL

Active v2.4.0

error Problem Statement

Legacy firewalls rely heavily on static signature matching, which fails to detect zero-day polymorphic malware and encrypted command-and-control traffic in real-time.

build Technical Approach

Implemented a kernel-bypass networking stack using eBPF for high-throughput packet filtering. Integrated a heuristic analysis engine that scores connection entropy and packet timing to identify anomalies without decryption.

Core Technologies

Rust eBPF React (Admin Panel) Redis
schema [ ARCHITECTURE_SCHEMATIC ]
+-------------------+ +-------------------+ | Packet Ingress | ----> | eBPF Filter | +-------------------+ +-------------------+ | | v v +-------------------+ +-------------------+ | Log Aggregator | <---- | Heuristic Eng. | +-------------------+ +-------------------+
code [ VIEW_SOURCE_CODE ] arrow_forward
ID: 002

AI NETWORK MANAGER

Deployed v1.1.2

error Problem Statement

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) Docker Grafana Kubernetes
hub [ NETWORK_TOPOLOGY ]
(Node A) -- 10ms --> (Node B) | ^ | | 15ms 5ms | | v | (Node C) -- 20ms --> (Node D)
code [ VIEW_SOURCE_CODE ] arrow_forward
ID: 003

AI-BASED INTRUSION DETECTION

Beta v0.9.5

error Problem Statement

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.

Core Technologies

TensorFlow Keras Scikit-learn Pandas
neurology [ MODEL_ARCHITECTURE ]
Input Layer [X] | LSTM Cell 1 -- Hidden State --> LSTM Cell 2 | | Dropout Dropout | | Dense Output Dense Output
code [ VIEW_SOURCE_CODE ] arrow_forward