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Industrial applications generate big data with redundant information that is transmitted over heterogeneous networks. The transmission of big data with redundant information not only increases the overall end-to-end delay but also increases the computational load on servers which affects the performance of industrial applications. To address these challenges, we propose an intelligent framework named Reliable and Secure multi-level Edge Computing (RaSEC), which operates in three phases. In the first phase, level-one edge devices apply a lightweight aggregation technique on the generated data. This technique not only reduces the size of the generated data but also helps in preserving the privacy of data sources. In the second phase, a multistep process is used to register level-two edge devices (LTEDs) with high-level edge devices (HLEDs). Due to the registration process, only legitimate LTEDs can forward data to the HLEDs, and as a result, the computational load on HLEDs decreases. In the third phase, the HLEDs use a convolutional neural network to detect the presence of moving objects in the data forwarded by LTEDs. If a movement is detected, the data is uploaded to the cloud servers for further analysis; otherwise, the data is discarded to minimize the use of computational resources on cloud computing platforms. The proposed framework reduces the response time by forwarding useful information to the cloud servers and can be utilized by various industrial applications. Our theoretical and experimental results confirm the resiliency of our framework with respect to security and privacy threats. [ABSTRACT FROM AUTHOR]

This paper presents an approach for tolerant control and compensation of cyber attacks on the inputs and outputs of a cyber-physical system of rotary gantry type. The proposed control schemes are designed based on classic–intelligent control strategies for trajectory tracking and vibration control of a networked control system, which are developed for tip angular position control, while the system is prone to cyber attacks. The malicious attacks are assumed to be of denial of service (DoS) kind and cause packet loss with high probability in the two signals; control input and sensor output. In this paper, several classic and intelligent control strategies are studied in terms of robustness and effectiveness to attacks. Based on the results, a new hybrid control scheme is designed using linear quadratic regulation, sliding mode control, and artificial radial basis function neural network to alleviate the effect of DoS attacks and maintain the performance of the cyber-rotary gantry system in tracking applications. The neural network controller is trained during the control process. Its learning algorithm is based on the minimization of a cost function which contains the sliding surface. The hybrid control system is analyzed from the stability perspective. Moreover, the efficiency of the proposed scheme is validated by simulation on MATLAB Simulink platform. [ABSTRACT FROM AUTHOR]

Due to the rapid developments in intelligent transportation systems, modern vehicles have turned into intelligent transportation means which are able to exchange data through various communication protocols. Today's vehicles portray best example of a cyber-physical system because of their integration of computational components and physical systems. As the IoT and data remain intrinsically linked together, the evolving nature of the transportation network comes with a risk of virtual vehicle hijacking. In this paper, we propose a combination of machine learning techniques to mitigate the relay attacks on Passive Keyless Entry and Start (PKES) systems. The proposed algorithm uses a set of key fob features that accurately profiles the PKES system and a set of driving features to identify the driver. First relay attack detection is performed, and if a relay attack is not detected, the vehicle is unlocked and algorithm proceeds to gain the driving features and use neural networks to identify whether the current driver is whom he/she claims to be. To assess the machine learning model, we compared the decision tree, SVM, and KNN method using a three-month log of a PKES system. Our test results confirm the effectiveness of the proposed method in recognizing relayed messages. The proposed methods achieve 99.8% accuracy rate. We used a Long Short-Term Memory recurrent neural network for driver identification based on the real-world driving data, which are collected from a driver who drives the vehicles on several routes in real-world traffic conditions. [ABSTRACT FROM AUTHOR]