Biblio

The domain of securing in-vehicle networks has attracted both academic and industrial researchers due to high danger of attacks on drivers and passengers. While securing wired and wireless interfaces is important to defend against these threats, detecting attacks is still the critical phase to construct a robust secure system. There are only a few results on securing communication inside vehicles using anomaly-detection techniques despite their efficiencies in systems that need real-time detection. Therefore, we propose an intrusion detection system (IDS) based on Multi-Layer Perceptron (MLP) neural network for Controller Area Networks (CAN) bus. This IDS divides data according to the ID field of CAN packets using K-means clustering algorithm, then it extracts suitable features and uses them to train and construct the neural network. The proposed IDS works for each ID separately and finally it combines their individual decisions to construct the final score and generates alert in the presence of attack. The strength of our intrusion detection method is that it works simultaneously for two types of attacks which will eliminate the use of several separate IDS and thus reduce the complexity and cost of implementation.

Internet of Things (IoT) era has gradually entered our life, with the rapid development of communication and embedded system, IoT technology has been widely used in many fields. Therefore, to maintain the security of the IoT system is becoming a priority of the successful deployment of IoT networks. This paper presents an intrusion detection model based on improved Deep Belief Network (DBN). Through multiple iterations of the genetic algorithm (GA), the optimal network structure is generated adaptively, so that the intrusion detection model based on DBN achieves a high detection rate. Finally, the KDDCUP data set was used to simulate and evaluate the model. Experimental results show that the improved intrusion detection model can effectively improve the detection rate of intrusion attacks.