{"id":25256780,"url":"https://github.com/onome-joseph/anomaly-detection","last_synced_at":"2026-04-28T08:05:10.002Z","repository":{"id":272206434,"uuid":"915825744","full_name":"Onome-Joseph/Anomaly-Detection","owner":"Onome-Joseph","description":"Comprehensive anomaly detection algorithm designed to analyze and identify anomalies in computer internal features.","archived":false,"fork":false,"pushed_at":"2025-01-12T22:58:19.000Z","size":301,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-06T00:28:09.578Z","etag":null,"topics":["anomaly-detection-algorithm","hardware-monitoring","python","z-score"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Onome-Joseph.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-12T22:28:47.000Z","updated_at":"2025-01-12T23:01:25.000Z","dependencies_parsed_at":"2025-01-12T23:27:34.473Z","dependency_job_id":"d5f830c5-2f5e-4f0d-954e-5eaf62988525","html_url":"https://github.com/Onome-Joseph/Anomaly-Detection","commit_stats":null,"previous_names":["onome-joseph/anomaly-detection"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Onome-Joseph/Anomaly-Detection","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FAnomaly-Detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FAnomaly-Detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FAnomaly-Detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FAnomaly-Detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Onome-Joseph","download_url":"https://codeload.github.com/Onome-Joseph/Anomaly-Detection/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Onome-Joseph%2FAnomaly-Detection/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32371708,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-27T20:07:02.737Z","status":"online","status_checked_at":"2026-04-28T02:00:07.250Z","response_time":56,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["anomaly-detection-algorithm","hardware-monitoring","python","z-score"],"created_at":"2025-02-12T06:27:33.290Z","updated_at":"2026-04-28T08:05:09.983Z","avatar_url":"https://github.com/Onome-Joseph.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Anomaly Detection Algorithm for Computer Internal Features\n\nThis repository contains a comprehensive anomaly detection algorithm designed to analyze and identify anomalies in computer internal features such as:\n- CPU usage\n- Temperature\n- Battery status\n- Memory usage\n---\nThe dataset comprises **1GB of synthetic multivariate time series data**, collected from the internal features of a computer. It includes **8,712,000 samples**, intentionally augmented with extreme values to test and evaluate the performance of the anomaly detection algorithms.\n## Algorithms Used\n1. **Z-Score Method**:\n   - Detects anomalies by calculating the standard score of each data point.\n   - Data points with a z-score above or below a specified threshold are flagged as anomalies.\n    \n2. **Interquartile Range (IQR) Method**:\n   - Identifies anomalies by analyzing the interquartile range of the data.\n   - Data points falling outside the lower and upper bounds (calculated using the IQR) are considered anomalies.\n### Visualization\nThe anomalies detected by the z-score method are visualized to provide clear insights into the data patterns and deviations. The visualizations highlight anomalies within the multivariate time series data for easier interpretation.\n\n## Key Features\n- Handles large-scale multivariate time series data effectively.\n- Implements robust statistical methods for anomaly detection.\n- Includes synthetic data with extreme values to test the reliability of the model.\n## Applications\n1. **System Monitoring**: Continuously monitor system performance and detect irregularities in real-time.\n2. **Security**: Detect abnormal behavior that may indicate security breaches or potential threats.  \n3. **Performance Optimization**: Gain insights into resource usage patterns and optimize system performance.\n## How to Use\n1. Ensure OpenHardwareMonitor is Installed and Running:\n   ```bash\n   https://openhardwaremonitor.org/\n   ```\n2. Clone this repository:\n   ```bash\n   git clone https://github.com/Onnome-Joseph/Anomaly-detection.git\n   ```\n3. Run the anomaly detection script:\n   ```bash\n   python src/anomaly_detection.ipynb\n   ```\n4. View the visualizations in the `visualizations/` folder.\n\n## Future Work\n\n- Extend the algorithm to include machine learning-based anomaly detection techniques.\n- Integrate real-time data streaming and detection.\n- Explore additional visualization techniques for better data analysis.\n\n## Contributing\nContributions are welcome! Feel free to open an issue or submit a pull request for any improvements or suggestions.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fonome-joseph%2Fanomaly-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fonome-joseph%2Fanomaly-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fonome-joseph%2Fanomaly-detection/lists"}