Chapter 21 — AlgoDetect
Based on: SIH1681 — AI-Based Algorithm Identification System (NTRO)
Skills Required:
- Artificial intelligence and machine learning
- Algorithmic analysis and classification
- Data preprocessing and feature engineering
- Python with ML libraries (scikit-learn, TensorFlow, PyTorch)
- Signal processing and feature extraction
- Software reverse engineering basics
Project Description:
AlgoDetect is an AI-powered system designed to automatically identify and classify algorithms implemented in software binaries or source code. It employs advanced feature extraction techniques from program characteristics, execution traces, or binary patterns, combined with machine learning models to detect known cryptographic, sorting, or compression algorithms. The tool assists security analysts and reverse engineers by accelerating algorithm recognition in unknown samples.
Tech Stack & Tools:
- Python for data processing and ML pipeline
- Feature extraction toolkits (disassemblers, instrumentation)
- Scikit-learn, TensorFlow, or PyTorch for modeling
- Jupyter notebooks for experimentation
- Dataset management and labeling tools
- Optional binaries and sample sets for supervised learning
Week-wise Roadmap
Week 1 — Requirements and Dataset Collection
- Define target algorithms and problem scope.
- Collect labeled datasets of software implementing the target algorithms (e.g., AES, SHA, RSA, QuickSort).
- Set up environment and repository structure.
- Deliverable: Dataset collection and baseline problem statement.
Week 2 — Feature Engineering & Extraction
- Implement static and dynamic feature extraction from binaries or source code: opcode sequences, control flow graphs, instruction histograms.
- Explore execution trace collection for dynamic analysis.
- Deliverable: Feature extraction scripts and initial dataset features.
Week 3 — Model Selection and Baseline Training
- Train baseline ML classifiers (Random Forest, SVM) on extracted features.
- Evaluate accuracy, precision, recall on test sets.
- Deliverable: Baseline classification models with evaluation reports.
Week 4 — Deep learning and Advanced Models
- Implement deep learning models (CNN, RNN) for sequence data.
- Explore transfer learning approaches and embeddings.
- Deliverable: Advanced model prototypes and performance comparisons.
Week 5 — Model Explainability & Refinement
- Integrate model explainability tools (SHAP, LIME) to interpret classifier decisions.
- Refine feature sets to improve accuracy or reduce false positives.
- Deliverable: Explainability reports and optimized models.
Week 6 — System Integration and API Development
- Develop API endpoints to submit binaries/text and return predicted algorithm classifications.
- Build CLI or simple UI for end-user usage.
- Deliverable: Functional system API and basic client.
Week 7 — Testing and Validation
- Test system performance on unknown samples and real-world binaries.
- Validate predictions and refine pipelines as needed.
- Deliverable: Test suite and accuracy reports.
Week 8 — Documentation and Deployment
- Prepare developer and user documentation.
- Containerize all components for easy deployment.
- Create tutorial notebooks and demos.
- Deliverable: Production-ready system with comprehensive docs.
Testing & Deliverables
- Collect multiple versions of target algorithm implementations (obfuscated and plain).
- Validate prediction accuracy and speed.
- Deliver source code repository, datasets, trained models, and demo videos.