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[HTML][HTML] Artificial intelligence for cybersecurity: Literature review and future research directions
Artificial intelligence (AI) is a powerful technology that helps cybersecurity teams automate
repetitive tasks, accelerate threat detection and response, and improve the accuracy of their …
repetitive tasks, accelerate threat detection and response, and improve the accuracy of their …
Recent endeavors in machine learning-powered intrusion detection systems for the internet of things
The significant advancements in sensors and other resource-constrained devices, capable
of collecting data and communicating wirelessly, are poised to revolutionize numerous …
of collecting data and communicating wirelessly, are poised to revolutionize numerous …
[HTML][HTML] Enhancing IoT network security through deep learning-powered Intrusion Detection System
The rapid growth of the Internet of Things (IoT) has brought about a global concern for the
security of interconnected devices and networks. This necessitates the use of efficient …
security of interconnected devices and networks. This necessitates the use of efficient …
[HTML][HTML] A stacking ensemble of deep learning models for IoT intrusion detection
The number of Internet of Things (IoT) devices has increased considerably in the past few
years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a …
years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a …
[HTML][HTML] Dtl-ids: An optimized intrusion detection framework using deep transfer learning and genetic algorithm
In the dynamic field of the Industrial Internet of Things (IIoT), the networks are increasingly
vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the …
vulnerable to a diverse range of cyberattacks. This vulnerability necessitates the …
Anomaly-based intrusion detection system in the Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm
Nowadays, the growth and pervasiveness of Internet of Things (IoT) devices have led to
increased attacks by hackers and attackers. On the other hand, using IoT infrastructure in …
increased attacks by hackers and attackers. On the other hand, using IoT infrastructure in …
HDL-IDS: a hybrid deep learning architecture for intrusion detection in the Internet of Vehicles
Internet of Vehicles (IoV) is an application of the Internet of Things (IoT) network that
connects smart vehicles to the internet, and vehicles with each other. With the emergence of …
connects smart vehicles to the internet, and vehicles with each other. With the emergence of …
[HTML][HTML] Ensemble-based deep learning models for enhancing IoT intrusion detection
Cybersecurity finds widespread applications across diverse domains, encompassing
intelligent industrial systems, residential environments, personal gadgets, and automobiles …
intelligent industrial systems, residential environments, personal gadgets, and automobiles …
A federated learning framework for cyberattack detection in vehicular sensor networks
Abstract Vehicular Sensor Networks (VSN) introduced a new paradigm for modern
transportation systems by improving traffic management and comfort. However, the …
transportation systems by improving traffic management and comfort. However, the …
[HTML][HTML] DRaNN_PSO: A deep random neural network with particle swarm optimization for intrusion detection in the industrial internet of things
Abstract The Industrial Internet of Things (IIoT) is a rapidly emerging technology that
increases the efficiency and productivity of industrial environments by integrating smart …
increases the efficiency and productivity of industrial environments by integrating smart …