A hybrid CNN + Bi-LSTM + attention model with transfer learning detects IoT cyberattacks across different networks with 96-98% accuracy, beating existing detectors.
Adding federated trust learning, quantum-resistant encryption, and reusable trust tokens to a vehicle authentication framework cut cross-domain authentication delay by 45%.
A federated CNN-Transformer model detects attacks on connected medical devices with ~99% accuracy across three datasets without pooling hospitals' raw data centrally.
Connecting solar, wind and batteries to digital grids opens serious hacking routes, and current defenses lack real-time detection, cross-domain data protection and harmonized policy.
A broad review of intrusion detection systems, mapping how they work today across cloud, IoT and industrial networks, and where AI and blockchain could take them next.
A review of explainable-AI intrusion detection for IoT finds the field now chases lightweight, transparent, privacy-preserving designs — but rarely validates its explanations or reproduces results.
Software-only security testing misses how car cyberattacks physically behave; hardware- and vehicle-in-the-loop rigs plus AI detection are needed to realistically test and defend connected vehicles.
A new method lets federated IoT intrusion detectors erase a device's data influence almost as well as full retraining, but far faster.
A drone-assisted graph learning system spots coordinated DDoS attacks across EV charging networks with 97.7% accuracy, beating localized traffic-only detectors.
A hybrid Transformer-CNN trained with federated learning identifies users from mouse movements with 99.51% accuracy while keeping raw behavioral data on their own devices.
Combining ant-colony feature selection with a CNN-BiLSTM attention network detects cyberattacks on smart consumer-electronics networks more accurately than existing intrusion detection methods.
AI intrusion detection for industrial IoT scores well on benchmarks but still fails on generalization, explainability, adversarial attacks, and real-world edge deployment.
Pre-training intrusion detectors on big computer-network attack datasets, then fine-tuning on small IoT ones, beats training from scratch and compresses to run on edge devices.
A unified deep learning system, X-THREAT, spots rare and never-before-seen cyberattacks with 93-96% accuracy across three benchmarks while explaining its decisions.
HyperIDS combines hypergraph neural networks, quantum-inspired feature selection and a transformer ensemble to detect IoT cyberattacks with about 98.9% accuracy.
Combining supervised and unsupervised detectors in a weighted ensemble, plus a hash-chained audit log, flags network intrusions with 98.85% accuracy and tamper-evident records.
A federated learning intrusion detector spots attacks on car networks with over 99% F1 score using just 1.11 MB of memory.
A recurrent neural network whose settings are tuned by a nature-inspired "fox" search algorithm detects network intrusions 4-15% more accurately than four competing deep learning models.
A combined deep-learning and blockchain system detects attacks in cloud-connected sensor networks and logs them tamper-evidently, beating LSTM and CNN-RNN baselines on two datasets.
Combining boosted decision trees and bagged random forests detects IoT botnet traffic more sensitively, and SHAP explains which network features drove each decision.
FedShield-IDS trains a CNN-LSTM intrusion detector across smart-home devices without sharing raw traffic, and uses SHAP to explain every alert.
Combining self-supervised learning, prototypical few-shot networks and continual learning detects new botnet attacks from very few samples, hitting 98.50% accuracy on CIC-IoT-2023.
A new intrusion detection system combines three machine learning models with an attention layer, SHAP explanations, and tamper-proof hashed logs to make attack alerts auditable.
A review of intrusion detection systems argues signature-based tools miss novel attacks while anomaly-based ones trigger false alarms, and evaluation datasets are outdated.
A deep neural network beats classical machine learning at spotting network and host intrusions across six benchmark attack datasets, and the authors propose a scalable real-time monitoring framework.
A survey mapping how machine learning and deep learning are used to detect network intrusions, plus the datasets, limits, and open problems in the field.
Turning just five consecutive network packets into an RGB image lets a lightweight intrusion detector spot attacks early with 99% accuracy.
A lightweight LSTM-based system can spot attacks on a car's internal CAN network in near real time without the overhead of encryption.