{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tii-ssrc-23-dataset-typological-exploration","title":"TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection","arxiv_id":"2310.10661","date":"2023-09-14","proceeding":null,"authors":["Dania Herzalla","Willian T. Lunardi","Martin Andreoni Lopez"],"abstract":"The effectiveness of network intrusion detection systems, predominantly based on machine learning, are highly influenced by the dataset they are trained on. Ensuring an accurate reflection of the multifaceted nature of benign and malicious traffic in these datasets is essential for creating models capable of recognizing and responding to a wide array of intrusion patterns. However, existing datasets often fall short, lacking the necessary diversity and alignment with the contemporary network environment, thereby limiting the effectiveness of intrusion detection. This paper introduces TII-SSRC-23, a novel and comprehensive dataset designed to overcome these challenges. Comprising a diverse range of traffic types and subtypes, our dataset is a robust and versatile tool for the research community. Additionally, we conduct a feature importance analysis, providing vital insights into critical features for intrusion detection tasks. Through extensive experimentation, we also establish firm baselines for supervised and unsupervised intrusion detection methodologies using our dataset, further contributing to the advancement and adaptability of intrusion detection models in the rapidly changing landscape of network security. Our dataset is available at https://kaggle.com/datasets/daniaherzalla/tii-ssrc-23.","url_abs":"https://arxiv.org/abs/2310.10661v1","url_pdf":"https://arxiv.org/pdf/2310.10661v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"feature-importance","task_name":"Feature Importance"},{"task_slug":"intrusion-detection","task_name":"Intrusion Detection"},{"task_slug":"multi-class-classification","task_name":"Multi-class Classification"},{"task_slug":"network-intrusion-detection","task_name":"Network Intrusion Detection"}],"methods":[],"datasets_introduced":[{"slug":"tii-ssrc-23","name":"TII-SSRC-23","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-tii-ssrc-23","task":"Anomaly Detection","dataset":"TII-SSRC-23","model":"Deep SVDD","rank_in_archive_order":1,"of":1,"metrics":{"AUC":"97.84"},"uses_additional_data":false},{"leaderboard":"/sota/binary-classification-on-tii-ssrc-23","task":"Binary Classification","dataset":"TII-SSRC-23","model":"XGBoost","rank_in_archive_order":1,"of":1,"metrics":{"F1-Score":"98.79"},"uses_additional_data":false},{"leaderboard":"/sota/multi-class-classification-on-tii-ssrc-23","task":"Multi-class Classification","dataset":"TII-SSRC-23","model":"Extra Trees","rank_in_archive_order":1,"of":1,"metrics":{"F1-Score":"93.36"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2310.10661","atlas_url":"https://app.syntology.ai/?focus=2310.10661","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}