AI-Powered Anomaly Detection System

[Nanle-code]

Create an AI system that detects anomalous code patterns that may indicate backdoors, malicious code, or unusual patterns:

1. Backdoor Detection: Identify potential backdoors and hidden functionality
2. Anomaly Detection: Detect unusual code patterns compared to normal contracts
3. Code Similarity Analysis: Identify code that's unusually similar to other projects
4. Obfuscation Detection: Detect code obfuscation attempts
5. Logic Bomb Detection: Identify time-based or condition-based malicious logic
6. Honeypot Detection: Identify potential honeypot contracts
7. Developer Fingerprinting: Identify coding style patterns for attribution
8. Continuous Learning: Learn from new patterns and update detection models

Requirements:

• Build dataset of normal vs malicious contract patterns
• Implement anomaly detection algorithms (isolation forests, autoencoders)
• Create code similarity and plagiarism detection
• Build obfuscation detection algorithms
• Implement logic bomb and honeypot detection
• Create developer fingerprinting system
• Build continuous learning and model updating
• Implement explainable AI for anomaly explanations
• Create integration with security databases
• Build alerting and reporting for detected anomalies

Acceptance Criteria:

• Detect 80%+ of known backdoor patterns
• False positive rate <5% on normal contracts
• Code similarity detection identifies 90%+ of copied code
• Obfuscation detection accuracy >85%
• Explanations for anomalies are clear and actionable
• Continuous learning improves detection over time
• Integration with existing ChainProof workflow

Difficulty: High - Requires ML for anomaly detection, security research expertise

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Maintainer Scope Upgrade

This issue is being expanded into a substantial, production-quality ChainProof enhancement. The implementation should be designed as maintainable platform work, not as a narrow proof of concept. A successful pull request must provide a cohesive user-facing capability, typed internal APIs, robust tests, documentation, and CI-safe behavior.

Expanded Objective

Create anomaly detection over scan outputs, code metrics, and project history. The feature should fit the existing monorepo architecture, reuse current scanner/report/CLI patterns where appropriate, and avoid introducing ad hoc subsystems that are difficult to test or maintain.

Required Implementation Depth

This issue is intentionally scoped to require more than 700 lines of meaningful implementation work. The line count expectation applies to purposeful source, tests, fixtures, and documentation that are necessary to deliver the feature. It must not be satisfied through generated output, lockfile churn, formatting-only changes, duplicated boilerplate, or artificial padding.

Expected work includes:

• Feature extraction for findings, complexity metrics, dependency changes, and scan history.
• Anomaly scoring with explainable reasons and configurable thresholds.
• Regression tests over synthetic history data and edge cases for sparse projects.
• Public or internal types/interfaces where they clarify behavior and reduce future integration risk.
• Failure-mode handling for invalid input, missing configuration, unavailable optional dependencies, and degraded execution paths.
• Documentation updates that explain how maintainers and users should operate the new capability in local and CI environments.

Professional Quality Bar

The implementation must be production-ready and reviewable in isolation. Contributors should include clear separation between parsing, analysis, reporting, CLI/action integration, and persistence or provider code where those concerns apply. The code should follow existing ChainProof conventions, keep behavior deterministic in tests, and avoid coupling core analysis to network-only services unless explicitly optional and mocked.

Acceptance Criteria

• The delivered PR contains more than 700 meaningful lines of implementation across source, tests, fixtures, and docs, excluding generated files and lockfile-only changes.
• The feature is integrated into the relevant package entrypoints, CLI commands, report formats, GitHub Action behavior, or documentation as appropriate for this issue.
• Unit tests cover normal operation, edge cases, invalid inputs, and at least one realistic fixture or end-to-end workflow.
• Any optional external service, model provider, database, or platform integration has deterministic mocks or fallbacks so CI does not depend on secrets or network availability.
• User-facing output is documented and stable enough for downstream automation.
• Backward compatibility is preserved unless the PR explicitly documents a migration path and the maintainer approves it.

Mandatory CI and Merge Requirements

A PR resolving this issue must not be merged until all repository CI checks pass. At minimum, reviewers should verify the following from a clean checkout:

• npm ci
• npm run lint
• npm run build --workspaces --if-present
• npm run test:ci --workspace=packages/core
• npm test --workspaces --if-present
• npm run build --workspace=packages/core && npm run docs --workspace=packages/core
• Any package-specific tests, examples, validators, or integration checks introduced by the PR

If the PR adds a GitHub Action, report format, dashboard, persistence layer, or external integration, it must also include CI coverage or a documented local verification command for that path. Known warnings are acceptable only when they are documented and do not hide failures.

Review Expectations

Reviewers should reject PRs that only stub APIs, add superficial wrappers, omit tests for critical behavior, rely on live secrets in CI, or meet the line-count target through non-functional bulk changes. The preferred solution is a focused but complete vertical slice that leaves ChainProof more reliable, easier to operate, and easier to extend.