Nanjing University of Science and Technology

Jiangsu Huachuang Microsystem Company Limited

Nanjing Research Institute of Electronics Technology

Yong Qi

Mingjun Liu

Jianye Yu

Yue Wang

Guoqiang He

Yu Zheng

PengZhen Du

Kun Qi

Dingliang Xu

Xian Lv

WeiBin Zhang

Yuan Zhuang

Yangwei Sun

The present disclosure belongs to the technical field of security of the Internet of vehicles, and discloses an intrusion detection method and device for an in-vehicle controller area network. The method of the present disclosure includes: digitizing and normalizing collected original data, obtaining preprocessed data, and dividing the preprocessed data into a training set and a test set; conducting feature selection on the preprocessed data through a particle swarm optimization (PSO)-light gradient boosting machine (GBM) bidirectional feature selection method; and classifying test set data subjected to the feature selection with a stacking integrated model, and obtaining an intrusion detection result. The present disclosure is configured to efficiently and accurately detect intrusion information appearing in the in-vehicle controller area network, and prevent security incidents of the Internet of vehicles caused by intrusion into the in-vehicle controller area network.

Intrusion detection method and device for in-vehicle controller area network

The present disclosure provides a security authentication method, system and device for IoV communication based on a national cryptographic algorithm. The method of the present disclosure includes: constructing, by an on-board unit, an identity authentication request message for transmitting to a road side unit; receiving, by the on-board unit, an identity authentication response message, and verifying a signature; and if the verification is successful, calculating, by the on-board unit, a session key and a hash value thereof, and constructing an acknowledgment message for transmitting to the road side unit, where the acknowledgment message is used in calculating a session key and a hash value thereof by the road side unit for contrast verification; if hash values are equal, security authentication is successful and a session key is generated; and if the hash values are not equal, the security authentication is failed.

Security authentication method, system and device for IoV communication based on national cryptographic algorithm

The present disclosure belongs to the technical field of Internet of vehicles (IoV) security and provides an IoV intrusion detection method and device based on an improved convolutional neural network. The method of the present disclosure includes: collecting original data of data traffic during IoV communication, and inputting the original data to a data dimension reduction algorithm model for IoV intrusion detection for preprocessing to obtain standardized data for IoV data analysis; inputting the standardized data for IoV data analysis to an improved convolutional neural network model for calculation, including: performing convolutional calculation and nonlinear activation on the input data for layering; performing two convolutional operations, two pooling operations and one full connection operation on each layer of data; and classifying a data set output by the improved convolutional neural network model through a SoftMax layer.

IoV intrusion detection method and device based on improved convolutional neural network

A two-sided time-sharing driving and acquisition system based on dry contacts comprises k dry contacts, a processor, p acquisition circuits, and m driving circuits. The p acquisition circuits are evenly divided into two groups which are separately disposed at two sides of the k dry contacts. The m driving circuits are separately disposed at the two sides of the k dry contacts. A system fault detection method and the two-sided time-sharing driving and acquisition system have the following advantages: the state of input acquisition circuits and cables monitored in real time during system debugging or running, and faults caused by exceptions of the acquisition circuits and the cables can be reported in time.

Two-sided time-sharing driving and acquisition system based on dry contacts and system fault detection method therefor

A driver fatigue detection method based on combining a pseudo-three-dimensional (P3D) convolutional neural network (CNN) and an attention mechanism includes: 1) extracting a frame sequence from a video of a driver and processing the frame sequence; 2) performing spatiotemporal feature learning through a P3D convolution module; 3) constructing a P3D-Attention module, and applying attention on channels and a feature map through the attention mechanism; and 4) replacing a 3D global average pooling layer with a 2D global average pooling layer to obtain more expressive features, and performing a classification through a Softmax classification layer. By analyzing the yawning behavior, blinking and head characteristic movements, the yawning behavior is well distinguished from the talking behavior, and it is possible to effectively distinguish between the three states of alert state, low vigilant state and drowsy state, thus improving the predictive performance of fatigue driving behaviors.

Driver fatigue detection method and system based on combining a pseudo-3D convolutional neural network and an attention mechanism

An intrusion detection method and system for Internet of Vehicles based on Spark and combined deep learning are provided. The method includes the following steps: S1: setting up Spark distributed cluster; S2: initializing the Spark distributed cluster, constructing a convolutional neural network (CNN) and long short-term memory (LSTM) combined deep learning algorithm model, initializing parameters, and uploading collected data to a Hadoop distributed file system (HDFS); S3: reading the data from the HDFS for processing, and inputting the data to the CNN-LSTM combined deep learning algorithm model, for recognizing the data; and S4: dividing the data into multiple resilient distributed datasets (RDDs) for batch training with a preset number of iterations.

Intrusion detection method and system for internet of vehicles based on spark and combined deep learning

A method for vehicle detection based on single-axis geomagnetic sensor is disclosed. The method includes the following: arranging two geomagnetic sensors in tandem at a fixed distance in the same lane, and acquiring, by the two geomagnetic sensors, data when a vehicle passes by; performing data cleansing and denoising on the data acquired by the geomagnetic sensors; detecting, by the geomagnetic sensors, the vehicle by using an state machine algorithm based on a fixed threshold, and extracting moments when the vehicle enters and leaves detection ranges of the sensors; and collecting statistics on traffic flow in a predetermined period of time, and calculating the velocity and length of the vehicle. The presently disclosed method and apparatus can be independently used to obtain vehicle parameters at important traffic sections or intersections, or can be used in combination with other traffic detection devices to obtain more comprehensive traffic information. The presently disclosed method and apparatus has low power consumption, high detection sensitivity, is easy to install and resists the impact of a harsh environment.

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Nanjing, China

Yong Qi