With the continuous maturity of automotive electronics and automation technologies, cars have provided drivers more driving entertainment by getting connected to smart phones, Bluetooth and the Internet, but it also brings hacker attacks, security vulnerability and other secure issues which cannot be ignored, these issues will seriously affect vehicle drives safety, personal privacy, and even endanger public safety. Automotive Ethernet have feature of high rate, so it can be applied to multimedia systems in cars. However, car bus system and the multimedia system are connected to each other through the Gateway system make the CAN (Controller Area Network), FlexRay and other vehicle bus network systems no longer independent and secure, so the security of the Automotive Ethernet must be guaranteed. In this paper ,we present a vehicle Ethernet network model, and introduces the AES-128 encryption algorithm and HMAC-SHA1 security authentication method and technology into the vehicle Ethernet system, that effectively preventing external intrusion, data hopping, and other possibility, further improved network security performance of automotive Ethernet and car bus networks.

Since the appearance of internet of vehicle (IoV), the in-vehicles network is no longer an isolated system. The OBD-II hardware, GPS, and the WiFi system could cause intrusions during communications. There are many unresolved network security problems yet in in-vehicle bus protocol, like controller area network (CAN), local area network (LIN), and FlexRay. In this paper, having considered the vulnerabilities and a hidden attacking of FlexRay, an authenticated encryption protocol is designed based on the Advanced Encryption Standard (AES)-128 and SHA-1 algorithm for FlexRay. We apply the protocol into a simulated FlexRay bus system which is build by CAN software environment. The protocol is also applied in a real Flexray network build by MC9S12XF512 evaluation board The both experimental results show that the security and reliability of in-vehicle bus system are enhanced.Index Terms-FlexRay, in-vehicle bus network, network reliability, security protocol.

Sentiment analysis systems can handle social media images by interpreting the embedded emotional responses in those images. This represents an interesting and challenging problem that tries to figure out the high-level content of large-scale visual data based on algorithms devised from computer vision. This paper presents a system to analyze social media images and visualize the implied emotions from each image as (Happy, Sad, and Neutral). The objective of this work is to introduce a system model with features extraction basis utilizing some adequate technique of machine learning. The applied methodology is pivoted on implementing the required system through several steps of processing. This involves social media image displaying and video frames grabbing, image features extraction, then embedded emotions patterns classification and recognition utilizing a proper convolutional neural network (CNN). Flickr and Twitter datasets were utilized while the pertinent algorithm was developed using "Matlab2017b" platform. This can help social media users visualizing their interests besides forming a better scope of visualization. It will further assist companies in envisaging the mood of users/costumers towards their stock prices in order to set competitive prices for both sides. We design a Deep Attention Network Mechanisms (DANM) to achieve a higher level of social media sentiment image analysis and classify them as (Highly positive mood and highly negative mood). The DANM produces features maps basis utilizing the adequate focusing technique of machine learning based on a proper convolutional neural network (CNN). The proposed CNN training system has proven better results with respect to accuracy and efficiency in comparison with some other similar works. When experimentations on both real and synthetic datasets were conducted, the system showed a percentile improvement of about 14.2%. This system is applicable to a broad horizon of applications such as studying the emotional response of humans on visual stimuli, visual sentiment analysis algorithms and modeling, building machine learning-based robust visual sentiment classifier, as well as in most online websites that involve visual data mining for business intelligence, e-commerce, stock market prediction, political vote forecasts, and video gaming. Communication Engineering analyze the facial actions in any case of context, culture, gender, and so on. The accomplishments in related areas such as psychological studies, human movement analysis, face detection, face tracking, and face recognition make the automatic facial expression analysis possible. It can be applied in many areas such as emotion and paralinguistic communication, clinical psychology, psychiatry, neurology, pain assessment, lie detection, intelligent environments, and multimodal human-computer interface (HCI).This paper aims to articulate the significant sides of implementing an efficient-accurate system for image sentiment analysis and visualization based on the concepts of utilizing Attention convol...