# Internal Interface Diversification as a Security Measure in Sensor Networks +
## 1 Introduction  
The Internet of Things (IoT) is a network composed of physical devices and other objects [1]. These \"things\" are incorporated with software, electronics, sensors and actuators. By using network connectivity, the devices collect and exchange information with each other. Today, IoT devices are used by various public and private sectors from health care to industrial applications. The whole physical infrastructure has become closely connected by information technology with the help of sensors, as interconnected embedded devices transmit measurement results and instructions over the network. The IoT forms a global infrastructure and covers several applications areas, such as smart cities, smart grids and power plants, with the aim of making everyday life more effortless and comfortable. In a wireless sensor network (WSN), several nodes are equipped with sensors that detect physical or environmental conditions, such as the temperature, light or pressure. With the rapid development of sensors, WSNs can be considered a key technology in the IoT environment. More and more new devices and sensors are connected to the Internet daily, and the IoT keeps growing. Gartner estimates over 6 billion \"things\" are in use now, and there will be about 20 billion by 2020. Another prediction is that more than half of major new business processes and systems will incorporate an IoT component by 2020 [2]. The enormous growth in the number of IoT devices also means there are serious implications for software security. The International Telecommunication Union (ITU) states in its recommendation that the \"IoT makes full use of things to offer services to all kinds of applications, whilst ensuring that security and privacy requirements are fulfilled.\" [1]. In practice, however, security and privacy are still a major challenge for the burgeoning IoT environment. The mismatch between the growing significance and the poor security of the IoT is obvious. HP reported that at least 70 percent of IoT devices are vulnerable to exploits that may be harmful to users [3]. Not surprisingly, adversaries have already started taking advantage of this issue, as was clearly shown with Mirai, a piece of malware found in August 2016. Mirai launched large-scale network attacks by using a botnet comprising of hundreds of thousands of IoT devices [4]. There is a clear incentive to improve software security in the IoT environment. Cybersecurity vendors and service providers have noticed the problem and are already devising new approaches and architectures for IoT security. However, novel security solutions that can effectively protect devices while taking into account the very limited resources available in the IoT environment, as well as sensor and actuator networks, are still needed. In our view, interface diversification is a security scheme that shows a lot of promise in the IoT environment. In the current era of _software monoculture_, not many execution platforms exist with large differences. Instead, there are huge numbers of copies of the same execution platform with the same internal structure. Because of this, one piece of malware can potentially infect millions of devices. Malware uses knowledge about the system's internal interfaces to achieve the attacker's objectives. If the internal interfaces were different in each IoT device, malware that relies on the information about the well-known identical interfaces would not be able to run in a system [5,6]. The idea of _internal interface diversification_ follows this line of reasoning. Unique diversification applied to the target node's internal interfaces makes its software much more resistant to potential cyber attacks. In a system that has been diversified, a malicious piece of software cannot communicate in the secret \"language\" used in that system and, therefore, is prevented from accessing the the system's resources. In this paper, we propose interface diversification as a security measure for devices in sensor networks. The contributions of this paper are as follows.