CIC-IDS-2017
PulseAugur coverage of CIC-IDS-2017 — every cluster mentioning CIC-IDS-2017 across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New Latent Sculpting Framework Enhances Zero-Shot Anomaly Detection
Researchers have developed a novel two-stage anomaly detection framework called Latent Sculpting, designed to improve the robustness of intrusion detection systems against unseen cyber threats. The framework first uses …
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New JEPA-style model learns useful network fingerprint embeddings
Researchers have developed JA4-JEPA, a Transformer-based model that applies JEPA-style predictive learning to network fingerprints. This approach, which learns by matching latent predictions rather than regenerating inp…
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New PLAA method enhances adversarial attacks on network intrusion detection systems
Researchers have developed a new method called PLAA to create adversarial attacks specifically for network intrusion detection systems (NIDS). Unlike previous methods that adapted attacks from computer vision, PLAA focu…
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New SHIELD-IDS enhances ML intrusion detection against adversarial attacks
Researchers have developed SHIELD-IDS, an enhanced intrusion detection system designed to combat adversarial attacks on machine learning models. The system integrates gradient boosting models like XGBoost and LightGBM i…
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Machine learning models enhance network attack detection and synthetic data generation
Researchers have developed a unified multi-modal dataset for network intrusion detection systems (NIDS) by reprocessing existing datasets like CIC-IDS-2017 and UNSW-NB15. The study employs machine learning algorithms fo…