The challenge
Business Email Compromise is one of the fastest-growing attack vectors in enterprise cybersecurity. Detecting it requires analyzing thousands of signals per message — links, metadata, keywords, attachments, behavioral patterns — across tens of thousands of mailboxes, in real time. Classical approaches either miss the nuance or collapse under the volume.
Advanced BEC Detection
Quantum-Optimized Feature Selection
Real-Time Email Classification
Analyze thousands of signals per message.
Optimization-driven detection pipeline
Less noise. More relevant features.
Hybrid quantum-classical detection engine
Faster, lighter, and more accurate fraud detection
The Solutions
We developed Q-BEC, a hybrid quantum-classical algorithm for email classification and fraud detection. The system works in three phases.
First, it extracts a rich set of features from every incoming message — binary signals, metadata, content patterns, and attachment behavior. Second, it applies QUBO (Quantum Unconstrained Binary Optimization) to select the optimal subset of features — a combinatorial problem where quantum computing provides a decisive advantage over classical solvers at scale. Third, a hybrid decision engine combining machine learning and heuristic rules classifies each email into one of four categories: legitimate, spam, advertisement, or BEC.
The result is a pipeline that is faster, lighter, and more accurate than classical alternatives — and that scales without the exponential time growth that makes classical feature selection impractical on large datasets.
The result
* Ability to handle tens of thousands of features without performance degradation
* Higher-quality classification even where classical solvers reach their limits
* A lighter downstream ML pipeline, with fewer and more relevant features
* Real-time applicability across high-dimensional, rapidly growing datasets
Business model Q-BEC is available as a subscription service, priced by number of monitored mailboxes. It can be deployed as a standalone product or integrated into existing security portfolios.