Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security

By Min-Joo Kang, Je-Won Kang. 2016

606 citations recorded by the source corpus.

A novel intrusion detection system (IDS) using a deep neural network (DNN) is proposed to enhance the security of in-vehicular network. The parameters building the DNN structure are trained with probability-based feature vectors that are extracted from the in-vehicular network packets. For a given packet, the DNN provides the probability of each class discriminating normal and attack packets, and, thus the sensor can identify any malicious attack to the vehicle. As compared to the traditional artificial neural network applied to the IDS, the proposed technique adopts recent advances in deep learning studies such as initializing the parameters through the unsupervised pre-training of deep belief networks (DBN), therefore improving the detection accuracy. It is demonstrated with experimental results that the proposed technique can provide a real-time response to the attack with a significantly improved detection ratio in controller area network (CAN) bus.

Subfields: Building and Construction, Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “study [108], ITS data are exposed to an intrusion detection system based on DNN to improve the security of in-vehicular”
Dynamic memristor-based reservoir computing for high-efficiency temporal signal processing (2021, methodology): “4 and 5, where a method of using the peaks of the reservoir states in response to different input waveforms is developed to quantitatively analyze these two parameters.”
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Convolutional Networks (2018, background): “Given their increased use in safety-critical and security applications such as autonomous vehicles [4, 74, 76], intrusion detection [28, 29], malicious string detection [65], and facial recognition [39, 70], it is important to ensure that such algorithms are robust to malicious adversaries.”
A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN) (2016, methodology): “In [17], Kang et al. proposed an NIDS for the security of in-vehicular networks using DBN and improved detection accuracy compared to previous approaches.”
Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “Besides, the vehicle is a sophisticated equipment, which has complex security issue itself [134].”
Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT (2017, methodology): “The work in [11] presents a classiﬁer which detects intrusions in an in-vehicle Controller Area Network (CAN), using a deep neural network pre-trained with a Deep Belief Network (DBN).”
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study (2020, background): “Kang et al. [23] proposed an intrusion detection system based on the deep neural network for vehicular networks.”
A survey of deep learning-based network anomaly detection (2017, background): “Yet, the issue is that since the augmented neural networks cannot be well trained by back-propagation that is a common algorithm of neural network learning, the generative pre-training scheme should be used for solving such issue [32].”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, background): “Each CAN packet includes 12 bits of arbitration field, 6 bits of control field, 0-8 bytes of data field, etc. Kang and Kang’s scheme utilizes the data field as the learning object.”
A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “Kang and Kang [154] developed a DL method to perform intrusion detection for in-vehicle network security.”