TY - JOUR
KW - Cybersecurity
KW - DoS Attack
KW - Feature Extraction
KW - MQTT
KW - Soft Computing
KW - Supervised Learning
KW - Machine Learning Classifier
AU - Álvaro Michelena
AU - Jose Aveleira-Mata
AU - Esteban Jove
AU - Héctor Alaiz-Moretón
AU - Héctor Quintián
AU - José Luis Calvo-Rolle
AB - The 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.
IS - Special Issue on Practical Applications of Agents and Multi-Agent Systems
M1 - 3
N2 - The 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.
PY - 2023
SE - 33
SP - 33
EP - 42
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Development of an Intelligent Classifier Model for Denial of Service Attack Detection
UR - https://www.ijimai.org/journal/sites/default/files/2023-08/ijimai8_3_3.pdf
VL - 8
SN - 1989-1660
ER -