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Digital devices are becoming increasingly ubiquitous and interconnected. Their evolution to intelligent parts of a digital ecosystem creates novel applications with so far unresolved security issues. A particular example is a vehicle. As vehicles evolve from simple means of transportation to smart entities with new sensing and communication capabilities, they become active members of a smart city. The Internet of Vehicles (IoV) consists of vehicles that communicate with each other and with public networks through V2V (vehicle-to-vehicle), V2I (vehicle-to-infrastructure) and V2P (vehicle-to-pedestrian) interactions, which enables both the collection and the real-time sharing of critical information about the condition on the road network. The Social Internet of Things (SIoT) introduces social relationships among objects, creating a social network where the participants are not humans, but intelligent objects. In this article, we explore the concept of the Social Internet of Vehicles (SIoV), a network that enables social interactions both among vehicles and among drivers. We discuss technologies and components of the SIoV, possible applications and issues of security, privacy and trust that are likely to arise.

Due to the advancement in technologies and excessive usability of smartphones in various domains (e.g., mobile banking), smartphones became more prone to malicious attacks.Typing on the soft keyboard of a smartphone produces different vibrations, which can be abused to recognize the keys being pressed, hence, facilitating side-channel attacks. In this work, we develop and evaluate AlphaLogger -an Android-based application that infers the alphabet keys being typed on a soft keyboard. AlphaLogger runs in the background and collects data at a frequency of 10Hz/sec from the smartphone hardware sensors (accelerometer, gyroscope and magnetometer ) to accurately infer the keystrokes being typed on the soft keyboard of all other applications running in the foreground. We show a performance analysis of the different combinations of sensors. A thorough evaluation demonstrates that keystrokes can be inferred with an accuracy of 90.2% using accelerometer, gyroscope, and magnetometer.