Next-Generation AI Cybersecurity & Quantum-Safe Edge
Leading European research in on-device threat intelligence, near-sensor machine learning, and post-quantum cryptographic tunneling from Nicosia, Cyprus.
Proven Edge Security & EU R&D Impact
Malloc LTD bridges academic research and enterprise-grade deployment with over 1 million global installations and verified dual-use defensive architectures.
1. AI-Powered Federated Threat Detection Service
Grounded in near-sensor processing principles, Project "Preventing unattended data recording and leakage in smart devices" (#PRE_SEED/0719/0201) under the Pre-Seed programme, and Project DAEMON-AI (#CODEVELOP/0824), Malloc's threat detection service executes real-time behavioral monitoring locally across sensors, applications, network traffic, and files.
Inherited Malloc Algorithmic & Privacy Mechanisms
Grounded in Project #PRE_SEED/0719/0201, the service incorporates Malloc's core spyware and data-leakage detection models. These models use a custom machine learning classifier that continuously monitors device sensors (camera and microphone), data streaming rates, memory usage, and disk space to detect and block background recording and spyware activity in real time. The architecture integrates client-side privacy protection with federated learning, processing local update vectors securely so raw user data never leaves the device while threat detection models are continuously refined across edge networks.
Mobile On-Device Federated Cycle (Flower Framework)
- Base Model Deployment: A global base model is distributed to mobile client endpoints via the central Flower hub.
- On-Device Local Training: Training occurs entirely on-device using local sensor data, keeping raw user telemetry strictly isolated.
- Knowledge Sharing: Encrypted weight updates and local gradients are sent back to the Flower hub aggregator.
- Model Replacement: The server aggregates knowledge via FedAvg and replaces the on-device base model with the newly refined version.
2. DAEMON-AI: On-Device Unsupervised Anomaly Detection
Built upon Malloc's work in Project DAEMON-AI, the engine leverages lightweight, unsupervised machine learning models to perform real-time payload classification and on-device anomaly detection without cloud dependence. Operating with sub-50ms latency and less than 5% CPU utilization, it executes triple-source telemetry monitoring across 20+ mobile indicators (including device status diagnostics, sensor metadata auditing, and network traffic monitoring) to detect zero-day spyware and unauthorized data transmissions.
Key Technical Indicators & Features:
3. Malloc Post-Quantum Encrypted Tunneling & VLESS Infrastructure
To safeguard data-in-transit and federated weight updates across edge nodes, AI Factories, and EuroHPC supercomputing infrastructure, TRAPP-AI incorporates Malloc Post-Quantum Encrypted Tunneling & VLESS Infrastructure (TRL 9). Grounded in privacy-preserving trust models and refined under the European Defence Innovation Scheme (EUDIS Cohort #3), this service wraps telemetry and model updates in post-quantum cryptographic primitives (ML-KEM / CRYSTALS-Kyber key encapsulation) to establish zero-trust, quantum-safe communication channels.
Quantum-Resistant WireGuard & VLESS Channel
Protects telemetry and federated neural weights against "Harvest Now, Decrypt Later" threats across critical infrastructure nodes.
Partner with Malloc LTD
We are available to collaborate in Horizon Europe Cluster 3/4, EUDIS, and EDF 2025/2026 defense initiatives—bringing proven TRL 9 post-quantum tunneling, federated learning frameworks, and low-latency edge AI anomaly detection to mission-critical platforms.
Contact Research Engineering: info@mallocprivacy.com