A permissioned blockchain ledger with an endorsement rule rejected all 50 poisoned signatures in a hybrid intrusion detection test, while...

A new study proposes a closed-loop AI framework that couples adaptive anomaly detection, probabilistic risk scoring, machine-learning measurement selection, game-theoretic...

A new forensic framework fuses network traffic metrics with physiological payload data, detecting data-alteration attacks on medical devices with an...

Researchers developed TriAlign-ATG, an AI framework that generates physically executable adversarial network traffic which evades deep-learning intrusion detectors while preserving...

Most intrusion detection research is tested on artificially balanced datasets, where attack and benign records appear in equal numbers. A...

A cluster-preserving feature selection framework cut intrusion detection features from 78 to 27 on the CICIDS2017 benchmark while Random Forest...

A blockchain-based framework using Ethereum smart contracts and a CNN detector blocked 99.8% of simulated DDoS attacks on client–server networks...

Researchers have developed FireMID, a firefly-inspired framework that combines feature selection and oversampling to improve DDoS attack detection on imbalanced...

A replication and extension study in Cluster Computing confirms that LSTM networks maintain high intrusion detection performance in IoT networks,...

A network study of 464 open-source cybersecurity projects finds that half of all cross-community work comes from just 36 contributors,...