Automates incident response, playbooks, and enterprise workflows.
Analyzes logs, emails, and user behavior to uncover threats.
Safeguards research, designs, and sensitive data across all devices.
Our detection engine runs on predictive, error-compensating neural networks, a brain-inspired method that forecasts its own errors and corrects them in real time.
Architected by Alper Cem Yılmaz, the approach reflects a research direction independently validated in the peer-reviewed literature, including work in IEEE Access on error-compensated wavelet neural networks.1 It is the foundation of CYBERDROID Neural Detection.
Each family targets a different class of adversary behavior. Findings fuse into coherent attack-chain narratives. No single-method blind spots.
Five more proprietary families are available under NDA during evaluation, covering identity drift, kernel-level egress, and cross-product attack-chain reconstruction.
Cortical coding and the 180+ algorithm portfolio aren't lab curiosities. Two production surfaces put them in front of SOC teams every day.
The cortical coding research, in production. CYBERDROID fuses 11 telemetry classes across all 15 algorithm families into one actor-centric reasoning graph.
.avif)
Where detection meets the operator. Every event arrives as a structured investigation with actor context, evidence chains, and recommended actions, not a line in a queue.
Not assembled from acquired startups. One continuous research program, led by the same people who designed it.
Two decades leading enterprise security deployments, AI research, and product development. Architect of the full ARRTECH suite, developed CRYPTOSIM and CRYPTOLOG.
One of the globally leading researchers in brain-inspired AI and anomaly detection. Full Professor at Istanbul Technical University and scientific advisor of ARRTECH.
0000-0001-8143-9434A five-year research program in predictive error-compensated architectures, the foundation underneath CYBERDROID Neural Detection. Full bibliography available under NDA.
High-Performance Time Series Prediction With Predictive Error Compensated Wavelet Neural Networks.
IEEE Access, 8, 210532–210541.
Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks.
Computers, Materials & Continua, 68(3), 3577–3593. 33% RMSE improvement over LSTM.
Predictive Error Compensating Wavelet Neural Network Model for Multivariable Time Series Prediction.
TEM Journal, 10(4), 1955–1963. Extends PEC-WNN to multivariable input.
Improvement in Prediction Performance Using Predictive Error Compensated Neural Networks.
Springer Lecture Notes in Networks and Systems. Ongoing program investment through 2024.
A 60-minute working session with the engineers who built it. We cover the architecture, the algorithm portfolio, a live investigation on your own telemetry, and the full bibliography under NDA.
Under mutual NDA · Engineer-led session · No sales overlay