Intelligent One-Class Classifiers for the Development of an Intrusion Detection System: The MQTT Case Study

Jove, Esteban; Aveleira‐Mata, José; Aláiz‐Moretón, Héctor; Casteleiro‐Roca, José‐Luis; Blanco, David Yeregui Marcos del; Zayas‐Gato, Francisco; Quintián, Héctor; Calvo-Rolle, José Luís

doi:10.3390/electronics11030422

Cited by 13 publications

(9 citation statements)

References 48 publications

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“…Another study [15] propose the implementation of an IDS that uses the Principal Component Analysis (PCA) classifier trained using a dataset created from the MQTTbased IoT network. The PCA revealed the best Area Under the Curve (AUC) of over 89% and the shortest training time.…”

Section: Related Workmentioning

confidence: 99%

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Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS

Alasmari,

Alhogail

2024

IEEE Access

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Smart homes are becoming more and more popular in the world, and they are mainly on Internet of Things (IoT) technologies to enable their functionality. However, since IoT devices have limited computing power and resources, implementing strong security measures is difficult, which makes the use of intrusion detection systems (IDS) an appropriate option. In this paper, we propose an optimized model with high performance for intrusion detection in Message Queue Telemetry Transport protocol (MQTT)based IoT networks for smart homes. This is done by studying a total of 22 Machine Learning (ML) algorithms based on an extended two-stage evaluation approach that includes several aspects for optimizing and validating performance for finding the ideal model. Based on the empirical evaluation, the GLM classifier with the random over-sampling technique produces the best detection performance with 100% accuracy and an f-score of 100%, outperforming the previous studies. This paper also investigates the influence of automatic feature engineering technique on the performance of algorithms. With the automatic feature engineering technique, the performance increased by up to 38.9%, and the time consumed to classify attacks decreased by up to 67.7%. This shows that automatic feature engineering can improve performance and reduce detection time.

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Section: Related Workmentioning

confidence: 99%

“…After training and testing the DT classifier and k-nearest neighbors algorithm (KNN), the results showed that DT's performance outperformed the other with an average Fmeasure of 93.81%. However, the imbalanced dataset issue was not addressed or resolved in either of the three previous papers [13], [15], [16].…”

Section: Related Workmentioning

confidence: 99%

Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS

Alasmari,

Alhogail

2024

IEEE Access

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“…Anomaly-based identification, on the other hand, is used to detect unknown assaults or attacks with a lack of clearly established patterns. Machine learning algorithms have recently been shown to prevent many security vulnerabilities and improve the performance of anomaly-based detection methods [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

Qaddoori¹,

Ali²

2023

(AREJ)

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This paper presents a security paradigm for edge devices to defend against various internal and external threats. The first section of the manuscript proposes employing machine learning models to identify MQTT-based (Message Queue Telemetry Transport) attacks using the Intrusion Detection and Prevention System (IDPS) for edge nodes. Because the Machine Learning (ML) model cannot be trained directly on lowperformance platforms (such as edge devices),a new methodology for updating ML models is proposed to provide a tradeoff between the model performance and the computational complexity. The proposed methodology involves training the model on a high-performance computing platform and then installing the trained model as a detection engine on low-performance platforms (such as the edge node of the edge layer)to identify new attacks. Multiple security techniques have been employed in the second half of the manuscript to verify that the exchanged trained model and the exchanged data files are valid and undiscoverable (information authenticity and privacy) and that the source (such as a fog node or edge device) is indeed what it it claimed to be (source authentication and message integrity). Finally, the proposed security paradigm is found to be effective against various internal and external threats and can be applied to a low-cost singleboard computer (SBC).

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“…Furthermore, the hand's positioning and posture in connection with its body are crucial to these manual expressions' foundation. All of these complementing primitives should be considered in a succession of frames in an efficient recognition system [4]. Although these frames are time-dependent, it is impossible to evaluate the blocks in Euclidean space because of this time dependency [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Adhikari¹,

Gangopadhayay²,

Pal³

et al. 2023

Computer Systems Science and Engineering

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Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research span from online gaming to surgical robotics. The location of the hands, the alignment of the fingers, and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. In this scenario, it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling. When a user performs the same dynamic gesture, the hand shapes and speeds of each user, as well as those often generated by the same user, vary. A machine-learning-based Gesture Recognition Framework (ML-GRF) for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation. We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions, and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes. The findings from the simulation support the accuracy, precision, gesture recognition, sensitivity, and efficiency rates. The Machine Learning-based Gesture Recognition Framework (ML-GRF) had an

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Intelligent One-Class Classifiers for the Development of an Intrusion Detection System: The MQTT Case Study

Cited by 13 publications

References 48 publications

Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS

Protecting Smart-Home IoT Devices From MQTT Attacks: An Empirical Study of ML-Based IDS

An Efficient Security Model for Industrial Internet of Things (IIoT) System Based on Machine Learning Principles

A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

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