Internet of Things Meets Vehicles: Sheltering In-Vehicle Network through Lightweight Machine Learning

Abstract

An internet of vehicles allows intelligent automobiles to interchange messages with other cars, traffic management departments, and data analysis companies about vehicle identification, accident detection, and danger warnings. The implementation of these features requires Internet of Things system support. Smart cars are generally equipped with many (hundreds or even thousands of) sensors and microcomputers so that drivers gain more information about travel. The connection between the in-vehicle network and the Internet can be leveraged by the attackers in a malicious manner and thus increases the number of ways the in-vehicle network can now be targeted. Protecting increasingly intelligent vehicle systems becomes more difficult, especially because a network of many different devices makes the system more vulnerable than ever before. The paper assumes a generic threat model in which attackers can access the controller area network (CAN) bus via common access points (e.g., Bluetooth, OBD-II, Wi-Fi, physical access, and cellular communication, etc). A machine learning based simplified attention (SIMATT)-security control unit (SECCU) symmetry framework is proposed towards a novel and lightweight anomaly detecting mechanism for the in-vehicle network. For this framework, we propose two new models, SECCU and SIMATT, and obtain state-of-the-art anomaly detecting performance when fusing the former to the latter. Regardless of the training phase or the detection phase, we strive to minimize the computational cost and thereby obtain a lightweight anomaly detection method. In particular, the SECCU model has only one layer of 500 computing cells and the SIMATT model has been improved to reduce its computational costs. Through substantial experiment comparisons (with various classical algorithms, such as LSTM, GRU, GIDS, RNN, or their variations), it is demonstrated that the SIMATT-SECCU framework achieves an almost optimal accuracy and recall rate.

Deep Transfer Learning Based Intrusion Detection System for Electric Vehicular Networks

“…In order to prove the efficiency of the this model, they applied it to other recent popular public datasets in the scope of CAN bus traffic intrusion detection. Xiao et al [ 30 ] proposed a lightweight ML algorithm based on RNN for IDS on the CAN bus network. The experimental evaluation using appropriate hyper-parameters demonstrated that the proposed model had good performance metrics, compared to LSTM and GAN models.…”

Intrusion Detecting System Based on Temporal Convolutional Network for In-Vehicle CAN Networks

“…Wang et al proposed a distributed anomaly detection system based on the hierarchical time memory (HTM) algorithm [19], which effectively realized the real-time prediction of the original CAN traffic data at the bit level. e method in [8,18,19] causes the loss of some information and relationships in the CAN network by establishing a model for an independent CAN ID or ECU [20], and the model becomes more complicated. Kang and Kang proposed a deep neural network (DNN)-based intrusion detection method [21], which used an unsupervised deep belief network (DBN) to pretrain the initialization parameters and test it on the simulation data set generated by the OCTANE platform.…”

EmbeddedPigDet—Fast and Accurate Pig Detection for Embedded Board Implementations

“…Even with low-cost embedded boards, the accuracy of the proposed method was not degraded and thus the proposed method can be a practical solution for large-scale pig farms. In fact, this analysis of cost effectiveness is closely related with the "on-device" AI issue (i.e., processing deep networks directly on embedded devices instead of cloud server platforms) [51][52][53][54][55]. For continuous monitoring of individual pigs with a cloud server, we should transmit the video stream of each pig room into the cloud server.…”

“…Since low-cost embedded boards have more limited computing power than typical PCs, fast and accurate detection of individual pigs for low-cost pig monitoring applications is very challenging. Because this research direction for a light-weight pig detector is a kind of "on-device" AI [51][52][53][54][55], it can also contribute to the on-device AI community.…”