{"id":25975774,"url":"https://github.com/erikglz/coap-mtd","last_synced_at":"2026-04-17T02:31:54.783Z","repository":{"id":279260641,"uuid":"937838620","full_name":"ErikGlz/CoAP-MTD","owner":"ErikGlz","description":"Repository for an IoT security project implementing Moving Target Defense (MTD) through CoAP protocol randomization to mitigate spoofing attacks and enhance adaptive security.","archived":false,"fork":false,"pushed_at":"2025-02-24T21:02:08.000Z","size":725,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-05T03:27:17.726Z","etag":null,"topics":["coap-protocol","cybersecurity","iot","machine-learning","python","scikit-learn","spoofing"],"latest_commit_sha":null,"homepage":"","language":"Python","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/ErikGlz.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":"2025-02-24T01:39:45.000Z","updated_at":"2025-02-24T21:02:12.000Z","dependencies_parsed_at":"2025-02-24T17:46:27.740Z","dependency_job_id":null,"html_url":"https://github.com/ErikGlz/CoAP-MTD","commit_stats":null,"previous_names":["erikglz/coap-mtd"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ErikGlz/CoAP-MTD","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ErikGlz%2FCoAP-MTD","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ErikGlz%2FCoAP-MTD/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ErikGlz%2FCoAP-MTD/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ErikGlz%2FCoAP-MTD/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ErikGlz","download_url":"https://codeload.github.com/ErikGlz/CoAP-MTD/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ErikGlz%2FCoAP-MTD/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":270579613,"owners_count":24610044,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-08-15T02:00:12.559Z","response_time":110,"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":["coap-protocol","cybersecurity","iot","machine-learning","python","scikit-learn","spoofing"],"created_at":"2025-03-05T03:24:02.802Z","updated_at":"2026-04-17T02:31:54.070Z","avatar_url":"https://github.com/ErikGlz.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CoAP-MTD\n\n## Overview  \nThis project explores security in **Internet of Things (IoT)** systems by implementing a **Moving Target Defense (MTD)** technique. The approach involves continuously reconfiguring the **CoAP (Constrained Application Protocol)** communication to mitigate **spoofing attacks** and other network threats.  \n\nBy randomizing CoAP protocol dialects and integrating machine learning-based decision-making, the system enhances resilience and adaptability against cyber threats in IoT environments.  \n\n## Features  \n- **CoAP Protocol Dialect Randomization** – Implemented using the `aiocoap` Python library.  \n- **Dynamic Reconfiguration** – The system continuously modifies CoAP communications to increase security.  \n- **Machine Learning Integration** – Uses a **decision tree algorithm** from `scikit-learn` to analyze and adapt security measures.  \n- **IoT System Implementation** – Built with a **Raspberry Pi 4** as a sensor node and a **Linux client** for communication.  \n- **Attack Simulation** – Uses **Scapy** to generate and test spoofing and network-based attacks.  \n\n## Technologies Used  \n- **Python** (Core language)  \n- **aiocoap** (CoAP protocol implementation)  \n- **scikit-learn** (Decision tree algorithm)  \n- **Scapy** (Network attack simulation)  \n- **Raspberry Pi 4** (IoT sensor node)  \n- **Linux** (Client system)\n\n## Future Work\n- Enhancing the adaptability of the MTD technique.\n- Expanding machine learning models for better threat detection.\n- Testing with additional IoT devices and protocols.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ferikglz%2Fcoap-mtd","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ferikglz%2Fcoap-mtd","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ferikglz%2Fcoap-mtd/lists"}