{"id":25918178,"url":"https://github.com/karami-mehdi/cyberattackdetection","last_synced_at":"2025-03-03T14:16:45.509Z","repository":{"id":264363834,"uuid":"892806895","full_name":"karami-mehdi/CyberattackDetection","owner":"karami-mehdi","description":"This project focuses on detecting cyberattacks using advanced analytical techniques and a deep learning model. It leverages structured datasets and explores the use of algorithms for detecting anomalies or malicious behavior in network traffic or system logs.","archived":false,"fork":false,"pushed_at":"2025-02-21T15:23:35.000Z","size":415,"stargazers_count":6,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-21T16:28:15.003Z","etag":null,"topics":["cyber-attack-detection","cyberattack","cyberattack-detection","cybersecurity","intrusion-detection","long-short-term-memory","lstm","recurrent-neural-networks","rnn"],"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/karami-mehdi.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":"2024-11-22T20:20:21.000Z","updated_at":"2025-02-21T15:23:38.000Z","dependencies_parsed_at":"2025-02-21T16:28:23.260Z","dependency_job_id":"6d8663c9-0788-447c-b91d-c62fe5718ecd","html_url":"https://github.com/karami-mehdi/CyberattackDetection","commit_stats":null,"previous_names":["nsswifter/cyberattackdetection","karami-mehdi/cyberattackdetection"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/karami-mehdi%2FCyberattackDetection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/karami-mehdi%2FCyberattackDetection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/karami-mehdi%2FCyberattackDetection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/karami-mehdi%2FCyberattackDetection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/karami-mehdi","download_url":"https://codeload.github.com/karami-mehdi/CyberattackDetection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241678150,"owners_count":20001682,"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","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":["cyber-attack-detection","cyberattack","cyberattack-detection","cybersecurity","intrusion-detection","long-short-term-memory","lstm","recurrent-neural-networks","rnn"],"created_at":"2025-03-03T14:16:45.047Z","updated_at":"2025-03-03T14:16:45.496Z","avatar_url":"https://github.com/karami-mehdi.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# 🌐 Cyberattack Detection and Anomalous Behavior Analysis Using Recurrent Neural Networks (RNN)\n\nCybersecurity is a critical domain as network attacks increasingly threaten systems worldwide. Detecting cyberattacks and analyzing anomalous behaviors in network traffic is essential for securing digital environments. This project leverages **Recurrent Neural Network (RNN)**, technically **Long Short-Term Memory (LSTM)**, to identify abnormal patterns in network traffic that signal potential cyberattacks, such as DDoS, port scanning, and brute-force attacks.\n\n## Dataset\nThe project utilizes the **[CIC-IDS2017 Dataset](https://www.unb.ca/cic/datasets/ids-2017.html)**, developed by the [Canadian Institute for Cybersecurity](https://www.unb.ca). It is widely used for intrusion detection system evaluation and contains real-world attack simulations, including:\n\n- **DDoS attacks**\n- **Brute force attacks**\n- **SQL injection**\n- **Port scanning**\n- **Botnet activities**\n\nThe dataset is suitable for RNN-based methods due to its sequential nature, allowing temporal dependencies in network traffic to be analyzed effectively.\n\nFor easier access, we used the [Network Intrusion Dataset on Kaggle](https://www.kaggle.com/datasets/chethuhn/network-intrusion-dataset).\n\n## How to Run\n\n1. To run this project, clone the repository and navigate to the project directory:\n```bash\ngit clone https://github.com/karami-mehdi/CyberattackDetection.git\ncd CyberattackDetection\n```\n\n2. Ensure you have `Python 3.11.1+` and the required libraries installed. Use the command below to install dependencies:\n```bash\npip install -r requirements.txt\n```\n\n3. Open the Jupyter Notebook:\n```bash\njupyter notebook cyberattack_detection.ipynb\n```\n\n4. Execute the notebook cells sequentially to preprocess data, train the model, and evaluate results.\n\n## Results\nThe RNN model effectively detects cyberattacks with competitive accuracy, leveraging temporal patterns in the data. Detailed evaluation metrics and visualizations are included in the notebook.\n\n## License\nThis project is licensed under the [MIT License](LICENSE).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkarami-mehdi%2Fcyberattackdetection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fkarami-mehdi%2Fcyberattackdetection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fkarami-mehdi%2Fcyberattackdetection/lists"}