Detection of attacks and intrusions on automotive engine IoT sensors

Denis Pejić, Visnja Krizanovic, K. Grgic
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引用次数: 2

Abstract

Predictive maintenance is used to predict system failures using deep learning algorithms and IoT sensors. However, IoT sensors and deep learning algorithms are susceptible to attacks, which at the same time poses a serious threat as far as car engine IoT sensors are concerned. This paper tends to research the consequence of false data injection on IoT automotive engine sensors, which can result in disastrous results. Also, the following deep learning algorithms are used in this paper to detect attacks and intrusions on automotive engine IoT sensors: RNN (Recurrent Neural Networks), LSTM (Long Short Term Memory Networks), GAN (Generative Adversarial Networks) and a new developed algorithm SPNN (Sequential Probability Neural Networks). The new SPNN algorithm was the fastest in detecting and preventing attacks/intrusions on automotive engine IoT sensors when it came to continuous attack, but the GAN algorithm was the fastest in detecting and preventing attacks/intrusions on automotive engine IoT sensors when it came to temporary attack.
检测对汽车发动机物联网传感器的攻击和入侵
预测性维护使用深度学习算法和物联网传感器来预测系统故障。然而,物联网传感器和深度学习算法容易受到攻击,这同时对汽车发动机物联网传感器构成了严重威胁。本文旨在研究虚假数据注入对物联网汽车发动机传感器的影响,它可能会导致灾难性的后果。此外,本文还使用以下深度学习算法来检测对汽车发动机物联网传感器的攻击和入侵:RNN(循环神经网络),LSTM(长短期记忆网络),GAN(生成对抗网络)和新开发的算法SPNN(顺序概率神经网络)。新的SPNN算法在检测和防止对汽车发动机物联网传感器的攻击/入侵方面是最快的,当涉及到持续攻击时,GAN算法在检测和防止对汽车发动机物联网传感器的攻击/入侵方面是最快的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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