Beta Hebbian Learning for Intrusion Detection in Networks of IoT Devices

[1]  Muhammad Aamir Cheema,et al.  Impact of COVID-19 on IoT Adoption in Healthcare, Smart Homes, Smart Buildings, Smart Cities, Transportation and Industrial IoT , 2021, Sensors.

[2]  Héctor Quintián-Pardo,et al.  Lithium iron phosphate power cell fault detection system based on hybrid intelligent system , 2020, Log. J. IGPL.

[3]  José Antonio López-Vázquez,et al.  Comparative Study of Imputation Algorithms Applied to the Prediction of Student Performance , 2020, Log. J. IGPL.

[4]  Alain Berro,et al.  Genetic algorithms and particle swarm optimization for exploratory projection pursuit , 2010, Annals of Mathematics and Artificial Intelligence.

[5]  Héctor Quintián-Pardo,et al.  Beta-Hebbian Learning for Visualizing Intrusions in Flows , 2020, CISIS.

[6]  Bernabé Dorronsoro,et al.  Towards a Reliable Comparison and Evaluation of Network Intrusion Detection Systems Based on Machine Learning Approaches , 2020, Applied Sciences.

[7]  Khaled Salah,et al.  IoT security: Review, blockchain solutions, and open challenges , 2017, Future Gener. Comput. Syst..

[8]  Álvaro Herrero,et al.  Key features for the characterization of Android malware families , 2017, Log. J. IGPL.

[9]  José Luís Casteleiro-Roca,et al.  Modelling the hypnotic patient response in general anaesthesia using intelligent models , 2018, Log. J. IGPL.

[10]  Héctor Quintián-Pardo,et al.  Gaining deep knowledge of Android malware families through dimensionality reduction techniques , 2018, Log. J. IGPL.

[11]  Héctor Quintián-Pardo,et al.  Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit , 2017, Int. J. Neural Syst..

[12]  Héctor Alaiz Moretón,et al.  PROTOTIPO DE IDS PARA DETECCIÓN DE INTRUSIONES CON MODELOS DE MACHINE LEARNING EN SISTEMAS IOT DE LA INDUSTRIA 4.0 , 2021 .

[13]  Héctor Alaiz-Moretón,et al.  Autoencoder Latent Space Influence on IoT MQTT Attack Classification , 2020, IDEAL.

[14]  R. Suman,et al.  Internet of things (IoT) applications to fight against COVID-19 pandemic , 2020, Diabetes & Metabolic Syndrome: Clinical Research & Reviews.

[15]  Álvaro Herrero,et al.  Clustering extension of MOVICAB-IDS to distinguish intrusions in flow-based data , 2017, Log. J. IGPL.

[16]  Emilio Corchado,et al.  Connectionist Techniques For The Identification And Suppression Of Interfering Underlying Factors , 2003, Int. J. Pattern Recognit. Artif. Intell..

[17]  José-Luis Casteleiro-Roca,et al.  Detección de anomalías basada en técnicas inteligentes de una planta de obtención de material bicomponente empleado en la fabricación de palas de aerogenerador , 2020 .

[18]  José Antonio López-Vázquez,et al.  Missing data imputation over academic records of electrical engineering students , 2019, Log. J. IGPL.