Biblio

The concept of social networking is integrated into Internet of things (IoT) to socialize smart objects by mimicking human behaviors, leading to a new paradigm of Social Internet of Things (SIoT). A crucial problem that needs to be solved is how to establish reliable relationships autonomously among objects, i.e., building trust. This paper focuses on exploring an efficient context-aware trustworthiness inference framework to address this issue. Based on the sociological and psychological principles of trust generation between human beings, the proposed framework divides trust into two types: familiarity trust and similarity trust. The familiarity trust can be calculated by direct trust and recommendation trust, while the similarity trust can be calculated based on external similarity trust and internal similarity trust. We subsequently present concrete methods for the calculation of different trust elements. In particular, we design a kernel-based nonlinear multivariate grey prediction model to predict the direct trust of a specific object, which acts as the core module of the entire framework. Besides, considering the fuzziness and uncertainty in the concept of trust, we introduce the fuzzy logic method to synthesize these trust elements. The experimental results verify the validity of the core module and the resistance to attacks of this framework.

To increase security and to offer user experiences according to the requirements of a hyper-connected world, modern vehicles are integrating complex electronic systems, being transformed into systems of Cyber-Physical Systems (CPS). While a great diversity of heterogeneous hardware and software components must work together and control in real-time crucial functionalities, cybersecurity for the automotive sector is still in its infancy. This paper provides an analysis of the most common vulnerabilities and risks of connected vehicles, using a real example based on industrial and market-ready technologies. Several components have been implemented to inject and simulate multiple attacks, which enable security services and mitigation actions to be developed and validated.

The Internet of Things (IoT) is enabling a new generation of innovative services based on the seamless integration of smart objects into information systems. Such IoT devices generate an uninterrupted flow of information that can be transmitted through an untrusted network and stored on an untrusted infrastructure. The latter raises new security and privacy challenges that require novel cryptographic methods. Attribute-Based Encryption (ABE) is a new type of public-key encryption that enforces a fine-grained access control on encrypted data based on flexible access policies. The feasibility of ABE adoption in fully-fledged computing systems, i.e. smartphones or embedded systems, has been demonstrated in recent works. In this paper we assess the feasibility of the adoption of ABE in typical IoT constrained devices, characterized by limited capabilities in terms of computing, storage and power. Specifically, an implementation of three ABE schemes for ESP32, a low-cost popular platform to deploy IoT devices, is developed and evaluated in terms of encryption/decryption time and energy consumption. The performance evaluation shows that the adoption of ABE on constrained devices is feasible, although it has a cost that increases with the number of attributes. The analysis in particular highlights how ABE has a significant impact in the lifetime of battery-powered devices, which is impaired significantly when a high number of attributes is adopted.

Internet of things (IoT) is the smart network which connects smart objects over the Internet. The Internet is untrusted and unreliable network and thus IoT network is vulnerable to different kind of attacks. Conventional encryption and authentication techniques sometimes fail on IoT based network and intrusion may succeed to destroy the network. So, it is necessary to design intrusion detection system for such network. In our paper, we detect routing attacks such as sinkhole and selective forwarding. We have also tried to prevent our network from these attacks. We designed detection and prevention algorithm, i.e., KMA (Key Match Algorithm) and CBA (Cluster- Based Algorithm) in MatLab simulation environment. We gave two intrusion detection mechanisms and compared their results as well. True positive intrusion detection rate for our work is between 50% to 80% with KMA and 76% to 96% with CBA algorithm.

In the past decade, the revolution in miniaturization (microprocessors, batteries, cameras etc.) and manufacturing of new type of sensors resulted in a new regime of applications based on smart objects called IoT. Majority of such applications or services are to ease human life and/or to setup efficient processes in automated environments. However, this convenience is coming up with new challenges related to data security and human privacy. The objects in IoT are resource constrained devices and cannot implement a fool-proof security framework. These end devices work like eyes and ears to interact with the physical world and collect data for analytics to make expedient decisions. The storage and analysis of the collected data is done remotely using cloud computing. The transfer of data from IoT to the computing clouds can introduce privacy issues and network delays. Some applications need a real-time decision and cannot tolerate the delays and jitters in the network. Here, edge computing or fog computing plays its role to settle down the mentioned issues by providing cloud-like facilities near the end devices. In this paper, we discuss IoT, fog computing, the relationship between IoT and fog computing, their security issues and solutions by different researchers. We summarize attack surface related to each layer of this paradigm which will help to propose new security solutions to escalate it acceptability among end users. We also propose a risk-based trust management model for smart healthcare environment to cope with security and privacy-related issues in this highly un-predictable heterogeneous ecosystem.

Opportunistic Networks are delay-tolerant mobile networks with intermittent node contacts in which data is transferred with the store-carry-forward principle. Owners of smartphones and smart objects form such networks due to their social behaviour. Opportunistic Networking can be used in remote areas with no access to the Internet, to establish communication after disasters, in emergency situations or to bypass censorship, but also in parallel to familiar networking. In this work, we create a mobile network application that connects Android devices over Wi-Fi, offers identification and encryption, and gathers information for routing in the network. The network application is constructed in such a way that third party applications can use the network application as network layer to send and receive data packets. We create secure and reliable connections while maintaining a high transmission speed, and with the gathered information about the network we offer knowledge for state of the art routing protocols. We conduct tests on connectivity, transmission range and speed, battery life and encryption speed and show a proof of concept for routing in the network.

Smart objects are small devices with limited system resources, typically made to fulfill a single simple task. By connecting smart objects and thus forming an Internet of Things, the devices can interact with each other and their users and support a new range of applications. Due to the limitations of smart objects, common security mechanisms are not easily applicable. Small message sizes and the lack of processing power severely limit the devices' ability to perform cryptographic operations. This paper introduces a protocol for delegating client authentication and authorization in a constrained environment. The protocol describes how to establish a secure channel based on symmetric cryptography between resource-constrained nodes in a cross-domain setting. A resource-constrained node can use this protocol to delegate authentication of communication peers and management of authorization information to a trusted host with less severe limitations regarding processing power and memory.
 

This paper proposes a security architecture for an IoT transparent middleware. Focused on bringing real life objects to the virtual realm, the proposed architecture is deployable and comprises protection measures based on existent technologies for security such as AES, TLS and oAuth. This way, privacy, authenticity, integrity and confidentiality on data exchange services are integrated to provide security for generated smart objects and for involved users and services in a reliable and deployable manner.