Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Dini, Pierpaolo; Begni, Andrea; Ciavarella, Stefano; Paoli, Emiliano De; Fiorelli, Giuseppe; Silvestro, Carmelo; Saponara, Sergio

doi:10.1109/access.2022.3186026

Cited by 29 publications

(18 citation statements)

References 36 publications

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“…These are fundamental aspects in the context of PHM (predictive/prognostic-healthmaintenance), as lower thermal stress is certainly one of the decisive factors in defining device maintenance protocols and in increasing the device's operational utilization time. In addition, this "intelligent" auto-derating function could be placed in the context of predictive monitoring systems, based on machine learning and artificial intelligence algorithms [55][56][57][58], which could extrapolate from the derating itself peculiar characteristics correlated with the ageing of components and the entire OBC device.…”

Section: Real-time Pil Setup and Resultsmentioning

confidence: 99%

Real‐time electro‐thermal modelling and predictive control design of resonant power converter in full electric vehicle applications

Dini,

Ariaudo,

Botto

et al. 2023

IET Power Electronics

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In this paper, a resonant converter model is proposed that takes into account the thermal and electrical dynamics of the main components. With respect to state‐of‐art, where model‐based design approaches are often implemented off‐line, the proposed model has a low computational complexity such that it can be simulated in real‐time. This real‐time electro‐thermal model enables also the design of a predictive control algorithm, that is proposed to optimize the current control of a SiC‐based resonant converter by handling both thermal and electrical operating limits of its components. To validate the interaction between the converter model and the predictive control algorithm, validation is proposed via real‐time processor‐in‐the‐loop, with application to the on‐board charger for high‐voltage Li‐ion battery pack, typical of modern fully electrified vehicles.

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Section: Real-time Pil Setup and Resultsmentioning

confidence: 99%

Real‐time electro‐thermal modelling and predictive control design of resonant power converter in full electric vehicle applications

Dini,

Ariaudo,

Botto

et al. 2023

IET Power Electronics

Self Cite

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“…Data transfers could be compromised, posing manipulation risks from external sources. [26][27][28]. Another widely used and effective approach in monitoring power electronic devices on vehicles involves integrating algorithms based on artificial intelligence (AI).…”

Section: State Of the Art Overviewmentioning

confidence: 99%

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Dini,

Basso,

Saponara

et al. 2024

IET Power Electronics

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The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

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“…In [27], the authors introduced an Extreme Learning Machine, Support Vector Machine models, and a rule-based intrusion detection system similar to SNORT for IIoT networks. The performance of the proposed model was evaluated using the KDD99, UNSW-NB15, CSE-CIC-IDS-2018, and Edge-IIoTset datasets.…”

Section: Related Workmentioning

confidence: 99%

A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection

Alshehri,

Saidani,

Alrayes

et al. 2024

IEEE Access

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The Industrial Internet of Things (IIoT) comprises a variety of systems, smart devices, and an extensive range of communication protocols. Hence, these systems face susceptibility to privacy and security challenges, making them prime targets for malicious attacks that can result in harm to the overall system. Privacy breach issues are a notable concern within the realm of IIoT. Various intrusion detection systems based on machine learning (ML) and deep learning (DL) have been introduced to detect malicious activities within these networks and identify attacks. The existing ML and DL-based models face challenges when confronted with highly imbalanced training. Repetitive data in network datasets inflates model performance, as the model has encountered much of the test set data during training. Moreover, these models decrease performance when confronted with datasets that include repetitions of similar data across various classes, where only the class labels are different. To overcome the challenges inherent in existing systems, this paper presents a self-attention-based deep convolutional neural network (SA-DCNN) model designed for monitoring the IIoT networks and detecting malicious activities. Additionally, a two-step cleaning method has been implemented to eliminate redundancy within the training data, considering both intra-class and cross-class samples. The performance of the SA-DCNN model is assessed using IoTID20 and Edge-IIoTset datasets. Furthermore, the proposed study is demonstrated through a comprehensive comparison with other ML and DL models, as well as against relevant studies, showcasing the superior performance and efficacy of the proposed model.

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Design and Testing Novel One-Class Classifier Based on Polynomial Interpolation With Application to Networking Security

Cited by 29 publications

References 36 publications

Real‐time electro‐thermal modelling and predictive control design of resonant power converter in full electric vehicle applications

Real‐time electro‐thermal modelling and predictive control design of resonant power converter in full electric vehicle applications

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

A Self-Attention-Based Deep Convolutional Neural Networks for IIoT Networks Intrusion Detection

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