Tag: network security

A new distributed intrusion detection system uses machine learning, blockchain and collaborating fog nodes, with reported zero-day F1-score rising from ...

A new study reports that Random Forest and Gaussian and Sigmoid kernel Support Vector Machines, combined with systematic preprocessing, achieved ...

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

Researchers report that a hybrid model combining dynamic convolution, a Kolmogorov-Arnold network and a closed-form continuous-time neural network outperformed classical ...

A simulation study in Cluster Computing proposes a trust-aware blockchain sharding framework that improved shard load balance by up to ...

A deployment-efficient CNN-LSTM framework with channel attention detects encrypted, imbalanced, and heterogeneous malicious traffic in real time on CPU-only hardware, ...

A hierarchical attention model reported 0.913 detection accuracy, 83.6% zero-shot attack recognition and 29.8-millisecond average detection latency in computational experiments.

A benchmark study found that concurrency-aware AI achieved up to 98.9% intrusion-detection accuracy, 2.1-times throughput and 57.5% lower average latency ...

In a computational evaluation on 5G-NIDD, a deep autoencoder trained only on benign flows and thresholded with an adaptive IQR ...

Researchers in India have built a hybrid intrusion detection system combining causal learning, entropy-based feature compression, adversarial calibration, and federated ...