01964nas a2200301   4500000000100000000000100001008004100002260001200043653001800055653001500073653002300088653000900111653001900120653002400139653003200163100002200195700002300217700001700240700002700257700002200284700002700306245009000333856007900423300001000502490000600512520113000518022001401648       2023                            d  c09/202310aCybersecurity10aDoS Attack10aFeature Extraction10aMQTT10aSoft Computing10aSupervised Learning10aMachine Learning Classifier1 aÁlvaro Michelena1 aJose Aveleira-Mata1 aEsteban Jove1 aHéctor Alaiz-Moretón1 aHéctor Quintián1 aJosé Luis Calvo-Rolle00aDevelopment of an Intelligent Classifier Model for Denial of Service Attack Detection  uhttps://www.ijimai.org/journal/sites/default/files/2023-08/ijimai8_3_3.pdf  a33-420 v83 aThe prevalence of Internet of Things (IoT) systems deployment is increasing across various domains, from residential to industrial settings. These systems are typically characterized by their modest computationa requirements and use of lightweight communication protocols, such as MQTT. However, the rising adoption of IoT technology has also led to the emergence of novel attacks, increasing the susceptibility of these systems to compromise. Among the different attacks that can affect the main IoT protocols are Denial of Service attacks (DoS). In this scenario, this paper evaluates the performance of six supervised classification techniques (Decision Trees, Multi-layer Perceptron, Random Forest, Support Vector Machine, Fisher Linear Discriminant and Bernoulli and Gaussian Naive Bayes) combined with the Principal Component Analysis (PCA) feature extraction method for detecting DoS attacks in MQTT networks. For this purpose, a real dataset containing all the traffic generated in the network and many attacks executed has been used. The results obtained with several models have achieved performances above 99% AUC.  a1989-1660