We stop IoT attacks before they happen.
ZNET AI Defender builds on Dr. Akere's doctoral research. Development continues independently at A4C Labs, where models are fine-tuned on Azure Machine Learning and deployed to the edge on NVIDIA Jetson Orin Nano. Figures shown are results from the doctoral research, on a labeled malicious/benign IoT network traffic dataset (Stratosphere Laboratory, via Kaggle).
Traditional security tools can't keep pace with IoT expansion
IoT devices projected to grow from 10 billion (2019) to 30 billion (2030) — each a potential attack vector
CISOs struggle to detect unauthorized IoT access and cross-segment intrusions
Breaches go undetected for weeks, causing massive supply-chain risk
Existing EDR/XDR tools don't scale to IoT telemetry volume or protocol diversity
Hybrid AI-Powered IoT Threat Detection
Supervised + Unsupervised Learning Models
Evaluated on normal traffic plus DDoS, DoS TCP flood, port scanning, ICMP flood and other anomaly classes
In doctoral research, the hybrid LSTM + Isolation Forest reached 99.95% accuracy and a 0.30% false-positive rate, vs. 99.94% and 0.38% for a stand-alone LSTM
Minimal resource footprint with cloud-based analytics and visualization
Research-driven, transparent AI for enterprise trust
Multi-protocol support: Pcap, MQTT, Modbus logs, and custom IoT protocols
Python, Keras (LSTM) and scikit-learn (Isolation Forest); fine-tuning on Azure ML; edge deployment on NVIDIA Jetson Orin Nano
Adaptive thresholding with explainable AI for security analysts
Secure IoT infrastructure for critical government and defense systems
Protect medical devices, patient data, and critical infrastructure from sophisticated attacks
Secure OT/IT convergence with threat detection for industrial control systems
Safeguard critical infrastructure, transportation, and utilities from cyber threats
Transparent, research-driven AI architecture
| Competitor | Approach | Limitation |
|---|---|---|
| Armis | Device inventory focus | Limited predictive AI capability |
| Nozomi | ICS/OT visibility | Not adaptive for IoT consumer devices |
| Darktrace | Network ML analytics | High cost, black-box models |
| ZNET AI Defender | Hybrid AI (LSTM + Isolation Forest) | Transparent, research-driven, cost-effective |
Expertise in cybersecurity, AI/ML, and research
Co-Founder & CEO
Cybersecurity Engineer, vCISO with 10+ years experience. D.Eng in Cybersecurity Analytics (GWU)
Request a demo or contact us to learn how ZNET AI Defender can protect your organization
We're seeking $250K in pre-seed funding to bring our commercial MVP to RSAC 2026
Investment Inquiry: invest@znet.ai