A Kohonen SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks

Barletta, Vita Santa; Caivano, Danilo; Nannavecchia, Antonella; Scalera, Michele

doi:10.3390/app10155062

Cited by 26 publications

(5 citation statements)

References 45 publications

(61 reference statements)

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“…Alshammari et al [12] proposed an intrusion classification model to identify CAN intrusions on in-vehicle networks utilizing support vector machine (SVM) and k-nearest neighbors (KNN) algorithms. Barletta et al [13] proposed a distance-based IDS for CAN intrusion detection using a X-Y fused Kohonen network with the k-means algorithm (XYF-K). The proposed method shows high accuracy on the CAN-intrusion dataset, but its main limitation is the high computational complexity.…”

Section: Related Work a Can Intrusion Detectionmentioning

confidence: 99%

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

164

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Modern vehicles, including connected vehicles and autonomous vehicles, nowadays involve many electronic control units connected through intra-vehicle networks to implement various functionalities and perform actions. Modern vehicles are also connected to external networks through vehicle-toeverything technologies, enabling their communications with other vehicles, infrastructures, and smart devices. However, the improving functionality and connectivity of modern vehicles also increase their vulnerabilities to cyber-attacks targeting both intra-vehicle and external networks due to the large attack surfaces. To secure vehicular networks, many researchers have focused on developing intrusion detection systems (IDSs) that capitalize on machine learning methods to detect malicious cyberattacks. In this paper, the vulnerabilities of intra-vehicle and external networks are discussed, and a multi-tiered hybrid IDS that incorporates a signature-based IDS and an anomaly-based IDS is proposed to detect both known and unknown attacks on vehicular networks. Experimental results illustrate that the proposed system can detect various types of known attacks with 99.99% accuracy on the CAN-intrusion-dataset representing the intra-vehicle network data and 99.88% accuracy on the CICIDS2017 dataset illustrating the external vehicular network data. For the zero-day attack detection, the proposed system achieves high F1-scores of 0.963 and 0.800 on the above two datasets, respectively. The average processing time of each data packet on a vehicle-level machine is less than 0.6 ms, which shows the feasibility of implementing the proposed system in real-time vehicle systems. This emphasizes the effectiveness and efficiency of the proposed IDS.

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Section: Related Work a Can Intrusion Detectionmentioning

confidence: 99%

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Yang

Moubayed

Shami

2022

IEEE Internet Things J.

164

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“…Barletta et al 42 open the subject about the CAN security on communication networks. Their main concern is on anomalies and intrusion detection using a Kohonen SOM (self‐organizing map) network.…”

Section: Methodsmentioning

confidence: 99%

Intelligent charging station in 5G environments: Challenges and perspectives

2021

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Summary The popularity of the electric vehicles has been growing due to the concern of increase in environmental pollution. In order to overcome the environmental issues, electric vehicles have been introduced all over the globe. As the use and number of electric vehicles is growing, there is a great demand to identify the sustainable solutions for intelligent charging station dedicated to electric vehicles. The objective of this paper is to explore the possibilities for intelligent charging station using 5G services. This paper is a state‐of‐the‐art concept to identify the current challenges and issues of intelligent charging station. It should be noted that in the future, the use of the smart charging station will dramatically improve the efficiency of current charging stations. It is identified based on several existing techniques that the biggest challenges are the connectivity between the vehicles and among different charging stations. This manuscript could be useful for researchers working on intelligent charging station worldwide and on their integration with the recent communication technologies like 5G. The features of intelligent charging stations combined with the fifth‐generation network represent a new path for humanity and introduce a safer and more advanced environment. From data privacy to human safety, the network provides enough resources to rise driving quality, time and fuel economy and human lives saving. The results of this research can be well applied in the industry or in the field of academic research.

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“…The parameter α(t) deals with the relative weighting between similarities of the maps X map and Y map . The t argument in α(t) is the number of iterations for training [42,60]. Figure 8 shows the structure of this type of neural network.…”

Section: X-y Fused Kohonen (Xyf)mentioning

confidence: 99%

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

Angulo-Saucedo

Leon-Medina

Pineda-Muñoz

et al. 2022

Sensors

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Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied.

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A Kohonen SOM Architecture for Intrusion Detection on In-Vehicle Communication Networks

Cited by 26 publications

References 45 publications

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

MTH-IDS: A Multitiered Hybrid Intrusion Detection System for Internet of Vehicles

Intelligent charging station in 5G environments: Challenges and perspectives

Damage Classification Using Supervised Self-Organizing Maps in Structural Health Monitoring

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