CSE-CIC-IDS2018

The 'CSE-CIC-IDS2018' dataset contains network traffic data used to evaluate machine learning techniques for the identification and classification of various cyber attacks.

Papers using CSE-CIC-IDS2018 (15)

Agentic Intelligence for Unified Cyber Defense: A Self-Adaptive Framework for Threat Detection Across Cloud, Edge, and IoT Systems2026 · 2 citesTraffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection2026Quantum transfer learning for cross-domain cybersecurity threat detection and categorization2026 · 1 citesHybrid Reputation Aggregation: A Robust Defense Mechanism for Adversarial Federated Learning in 5G and Edge Network Environments2025 · 2 citesHybrid CNN-LSTM Framework for Intelligent Cyber Attack Detection and Prevention in U.S. Critical Digital Infrastructure: A Comparative Machine Learning Evaluation on CSE-CIC-IDS20182026Developed Lightweight Endpoint Detection and Response Prototype Using Machine Learning for Efficient Threat Detection and Prioritization2026An intelligence framework for threat detection and response in cloud–IoT-assisted enterprise environments2026Machine learning-based framework for dynamic risk assessment and early warning in wireless network security environments2026Enterprise Network Security Using Few‐Shot Meta‐Learning2026Improving Network Security Through Machine Learning Based Traffic Classification: A Comparative Survey2026FIRCE: A Framework for Intrusion Response and Conformal Evaluation2026From Detection to Response: A Deep Learning and Retrieval-Augmented Generation Framework for Network Intrusion Mitigation2026AI-Powered Hybrid Intrusion Detection Framework for Cloud Security Using Novel Metaheuristic Optimization2026Hierarchical Multi-Modal Threat Intelligence Fusion Without Aligned Data: A Practical Framework for Real-World Security Operations2025A Review of Various Datasets for Machine Learning Algorithm-Based Intrusion Detection System: Advances and Challenges2025