Inspiration

Cyber threats are evolving at an unprecedented rate, leveraging adaptive evasion tactics, zero-day exploits, and credential stuffing attacks that traditional machine learning (ML) models struggle to detect.

Modern cybersecurity defense mechanisms often rely on static heuristics and manually crafted rules, which fail against novel or sophisticated attacks.

Additionally, the sheer volume of security data has become overwhelming, making real-time analysis difficult and delaying responses to potential threats. Speed is critical—even a few seconds of delay in detecting an attack can mean millions in damages.

💡 To address this, we leveraged Quantum Random Forests (QRF)—a quantum-enhanced AI approach that boosts pattern recognition in cybersecurity threats. By encoding security data into high-dimensional quantum feature spaces, Quantumly Safe provides faster, more accurate anomaly detection for login attacks and malicious network behaviors.

What it does

Quantumly Safe is a cybersecurity platform that enhances traditional threat detection with quantum-powered machine learning. It:

✅ Detects cyber attacks and suspicious login behaviors by analyzing login patterns and network anomalies

✅ Uses quantum-enhanced feature encoding for improved classification accuracy and decision-making

✅ Classifies events as “safe” or “potentially malicious” to help security teams respond efficiently

✅ Generates detailed security reports, providing insights into attack trends and flagged anomalies

By leveraging quantum-enhanced AI, Quantumly Safe improves security intelligence, reduces detection latency, and minimizes bias toward non-attack cases.

How we built it

We developed Quantumly Safe using a hybrid quantum-classical machine learning pipeline, combining quantum feature encoding, classical ML techniques, and cybersecurity analytics.

📌 1. Data Preprocessing

🔹 Processed cybersecurity login datasets with key features such as:

🔹 Login attempts, failed logins, session duration

🔹Browser type (one-hot encoded), IP reputation scores (future improvement)

🔹 Min-max scaled numerical features to improve model performance

📌 2. Quantum Feature Encoding

🔹 Applied Quantum Angle Embedding to transform cybersecurity features into quantum states

🔹 Used Strongly Entangling Layers to encode complex relationships between

📌 3. Quantum Random Forest Model

🔹 Trained a RandomForestClassifier on quantum-enhanced feature representations

🔹 Used class weighting & SMOTE to balance attack vs. non-attack distributions

🔹 Evaluated performance using accuracy, precision, recall, and F1-score

Challenges we ran into

💥 Quantum Feature Encoding Complexity → Finding the optimal number of qubits and entanglement layers was challenging

💥 Class Imbalance in Cybersecurity Data → Attack instances were underrepresented, requiring oversampling techniques like SMOTE

💥 Hyperparameter Tuning → Running GridSearchCV on quantum-enhanced features required careful optimization

💥 Pandas FutureWarnings → Encountered dtype mismatches while scaling data, fixed with explicit float conversion

Accomplishments that we're proud of

🏆 Successfully integrated quantum feature transformations into a Random Forest model

🏆 Improved attack detection accuracy from 78% to 84.6%, significantly reducing false negatives

🏆 Balanced class distributions using SMOTE to prevent bias toward non-attack cases

🏆 Implemented Quantum Kernel Encoding, enhancing ML performance in security analytics

🏆 Built a scalable cybersecurity model for potential real-time network security applications

What we learned

🔍 Quantum computing can enhance machine learning models, but requires careful tuning of feature encodings

🔍 Feature engineering is critical—introducing login_failed_ratio significantly boosted detection accuracy

🔍 Handling class imbalance is essential for cybersecurity datasets, requiring synthetic data balancing techniques

🔍 Hybrid quantum-classical models are feasible today, but further optimization is needed for production use

What’s next for Quantumly Safe? 🚀

✅ Enhancing Quantum Feature Encoding → Experimenting with Quantum Kernel Methods (QKM) & Variational Quantum Models

✅ Real-time Threat Detection → Deploying Quantumly Safe as a live API for security monitoring

✅ Integrating IP Reputation Scores → Incorporating external IP risk databases for better anomaly classification

✅ Benchmarking Against Other ML Models → Comparing against XGBoost, CatBoost, and Deep Learning models

Quantumly Safe: The Future of Cybersecurity with Quantum AI

By combining quantum-enhanced AI with traditional cybersecurity techniques, Quantumly Safe pushes the boundaries of threat detection, response efficiency, and cyber defense intelligence.