In an Internet of Things (IoT) environment, a large volume of potentially confidential data might be leaked from sensors installed everywhere. To ensure the authenticity of such sensitive data, it is important to initially verify the source of data and its identity. Practically, IoT device identification is the primary step toward a secure IoT system. An appropriate device identification approach can counteract malicious activities such as sending false data that trigger irreparable security issues in vital or emergency situations. Recent research indicates that primary identity metrics such as Internet Protocol (IP) or Media Access Control (MAC) addresses are insufficient due to their instability or easy accessibility. Thus, to identify an IoT device, analysis of the header information of packets by the sensors is of imperative consideration. This paper proposes a combination of sensor measurement and statistical feature sets in addition to a header feature set using a classification-based device identification framework. Various machine Learning algorithms have been adopted to identify different combinations of these feature sets to provide enhanced security in IoT devices. The proposed method has been evaluated through normal and under-attack circumstances by collecting real-time data from IoT devices connected in a lab setting to show the system robustness.
IoT systems may provide information from different sensors that may reveal potentially confidential data, such as a person's presence or not. The primary question to address is how we can identify the sensors and other devices in a reliable way before receiving data from them and using or sharing it. In other words, we need to verify the identity of sensors and devices. A malicious device could claim that it is the legitimate sensor and trigger security problems. For instance, it might send false data about the environment, harmfully affecting the outputs and behavior of the system. For this purpose, using only primary identity values such as IP address, MAC address, and even the public-key cryptography key pair is not enough since IPs can be dynamic, MACs can be spoofed, and cryptography key pairs can be stolen. Therefore, the server requires supplementary security considerations such as contextual features to verify the device identity. This paper presents a measurement-based method to detect and alert false data reports during the reception process by means of sensor behavior. As a proof of concept, we develop a classification-based methodology for device identification, which can be implemented in a real IoT scenario.
The use of new-generation information technologies in industry and manufacturing is increasing rapidly. However, although information about products is generated and consumed during their entire lifecycle, current research on Product Lifecycle Management (PLM) tends to focus mainly on the physical products themselves rather than on the related information. The Digital Twin (DT) concept aims to connect the physical world with the virtual one by making all the information about physical objects accessible from a single place, even though that information might be distributed over many information systems. This paper presents and analyses new Product Lifecycle Information Management (PLIM) with DT for managing the lifecycle of smart products in the IoT environment. A real-world use case that is a recently finished main building of the Aalto University campus is presented to demonstrate the proposed approach.
Internet of Things (IoT) is a computing infrastructure underlying powerful systems and applications, enabling autonomous interconnection of people, vehicles, devices, and information systems. Many IoT sectors such as smart grid or smart mobility will benefit from the recent evolutions of the smart city initiatives for building more advanced IoT services, from the collection of human-and machine-generated data to their storage and analysis. It is therefore of utmost importance to manage the volume, velocity, and variety of the data, in particular at the IoT gateways level, where data are published and consumed. This paper proposes an access time improvement framework to optimize the publication and consumption steps, the storage and retrieval of data at the gateways level to be more precise. This new distributed framework relies on a consistent hashing mechanism and modular characteristics of microservices to ensure a flexible and scalable solution. Applied and assessed on a real case study, experimental results show how the proposed framework improves data access time for standardized IoT gateways.
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