Security, Trust and Privacy Models, and Architectures in IoT Environments

At present, IoT networks have penetrated almost all spheres of life in modern society. They have a fairly wide arsenal of various network devices and also have a fairly developed and branched structure. However, the high dynamics of the behavior of IoT networks, coupled with the large volumes of information processed in them and the transmitted traffic, cause certain difficulties in solving the problems of administration of computer networks. It is becoming increasingly difficult for an IoT network administrator to identify and resolve abnormal situations in a timely manner. It is possible to solve the problem of effective administration of a large and complex IoT networks if we introduce a specialized intelligent decision support system for the administrator into the arsenal of network administration tools. The paper discusses a variant of the implementation of the analytical block for intelligent decision support of IoT network administrators, built on the basis of artificial neural networks. The paper outlines the structure of a combined neural network, focused on solving the problem of assessing the state of computer network elements. Three training methods are considered: stochastic gradient descent, the adaptive learning rate method, and the adaptive inertia method. The experimental results have shown a sufficiently high accuracy of the proposed solution, good adaptability, and the possibility of its application in a wide range of network configurations.

The Internet of Things (IoT) exhibits vast applications to the healthcare industry. However, there are various security vulnerabilities associated with such kinds of data. In order to avoid such kinds of security problems, the design of a distributed model to provide resistant to security breached and vulnerabilities. This provides a trust-less solution to the healthcare practitioner and their patients. One of the proposed solutions for adopting blockchain capabilities is in an Internet of Things (IoT) infrastructure. Blockchains provide tamper-proof and non-repudiations features to data. Moreover, the consensus mechanism of the blockchain protocol provides reliability and trust to the user to agree upon a certain transaction or updating, it’s also called proof of concept (PoC). In this research we have used hyper-ledger fabric as a tool for consortium blockchain design. We implemented the proposed model through chaincode, and we used cipher for threat and security check. The results are compared with the benchmark models, and it’s evaluated that the proposed model provides better privacy preservation.

In the Internet of Things, smart objects can build multidimensional and context-sensitive network infrastructures potentially rich of social interactions. Smart objects can be associated with software agents to boost social interactions and realizing complex and sophisticated forms of collaboration of objects with both other objects and people. In such a scenario, there exists the possibility to interact with unreliable partners exposing agents to the risks deriving by malicious behaviors. To mitigate these risks, Trust and Reputation Systems can be adopted to provide each agent with appropriate trustworthiness measures about the potential counterparts in order to select the best ones. In this context, our contribution consists of (i) a method to preliminarily identify the best candidates as malicious in order to consider them as pre-untrusted entities and (ii) a novel effective reputation model able to detect collusive malicious agents without introducing collateral effects with respect to the reputation scores of honest agents.

Common arguments related to delay-tolerant networks in IoT environments are often focused on routing performances, energy consumption or quality of service. Despite privacy-related issues are generally considered critical, they do not arouse the same interest as the other reported aspects. Nevertheless, most of the protocols in use seem to not care about that, assuming that this task should be in charge by others. In this chapter, we propose an innovative routing protocol, which is able to take care of sensitive information, with delivery performances comparable with the most used protocols currently known, taking hint by a feature borrowed from human nature, the vocal timbre.

The chapter encompasses issues of modeling and detection of Denial-of-Sleep attacks in wireless sensor networks. Such attacks are applicable to IoT devices functioning autonomously and switching between two operating modes, namely, the normal and energy-efficient sleep mode, depending on the business rules of the devices. While being generally quite effective, such attacks are quite stealthy and can significantly reduce and even completely deplete a device’s battery life, thereby leading the device to a disabled state. In this work, source data is analyzed, and artificial intelligence methods are applied to detect Denial-of-Sleep attack. The detection model is constructed with the use of particular machine learning methods. The model is validated on real data collected on a testbed with ZigBee wireless modules presenting both normal wireless sensor nodes and attacking one. The detection quality indicators prove the effectiveness and applicability of the proposed ML-based detection techniques in practice.

Malware is a serious threat in a world where IoT devices are becoming more and more pervasive; indeed, every day new and more sophisticated malware can rely on an attack surface that grows together with the number of new devices coming to the market. There is a constant competition between malware detection systems that have to adapt their knowledge base and heuristics day by day and malware writers that have to find new techniques to evade these systems. In this scenario, machine learning methods are the best candidate to face the continuous evolution of malware; this justifies the increasing interest in such approaches to build antimalware systems able to learn and adapt themselves. However, a still open question is how robust machine learning-based systems are against obfuscation techniques: methods that base their effectiveness on what they are able to learn from a training set are potentially vulnerable to modifications of the code that alter the probabilistic distribution of the features observed during the training phase. In this paper we propose a comparison of seven different methods trained to classify malware, paying specific attention to the recent image-based approaches. The comparison has been conducted using one of the largest dataset of malware publicly released until now, i.e., the SOREL-20M, composed of more than 20 million of samples divided in 11 families of malware. In the proposed analysis, we have considered four basic obfuscation techniques based on the addition of a sequence of bytes at the end of the executable; they are very easy to implement for a malware writer. All the tested methods achieved a very high accuracy on unmodified test samples, but only few of them have demonstrated to be able to withstand the considered obfuscation techniques.

The Internet of Things (IoT) promises to change many aspects of our daily lives thanks to the opportunity of interconnecting a massive numbers of smart objects with increasing computational, storage, communication, and power capabilities, in such a way making “smart” and “interactive” most of the world around us. In other words, smart objects and humans will be involved together in pervasive, proactive, and collaborative activities to orchestrate and execute increasingly complex and sophisticated tasks. In such a scenario, thanks also to a greater ecological awareness of people, a promising field of application for IoT technology is the monitoring of natural habitats in automatic or semiautomatic mode. A potentially effective and efficient solution is to form composite teams joining human operators and IoT devices. In such teams, not only the kind of team members will be different, i.e., humans and IoT devices, but also the IoT devices will be heterogeneous among them in terms of characteristics and performance. However, a basic requirement for a good team is the existence of high levels of mutual trust among its members. In other words, for the formation of a good team, it becomes of primary relevance to know and adequately represent the trustworthiness of the individual team members. To this end, our contribution can be summarized as (i) introducing a trust measure that takes into account both the reputation of devices and the accuracy of their measures; (ii) designing a framework that based on the proposed trust measure forms temporary teams for environmental ecosystems made of humans and IoT devices; and (iii) testing the proposed framework by simulating a collaborative environmental monitoring activity. The simulation results confirmed the advantages of the proposed approach in terms of performance and appreciation of the composite temporary teams that have been formed in this way.

IoT devices and applications are becoming a pervasive technology which involves many aspects of people’s everyday life. As such, their use raises critical issues related to the safety, privacy, and security of the data they operate on. In this setting, a very important concern regards the accountability of IoT devices with respect to the processing activities they execute while interacting with other systems, e.g., during the enactment of complex, cross-organization business processes. Two key issues toward the achievement of accountable interactions are (i) the ability to reliably authenticate and identify the involved parties and (ii) the traceability of the executed operations. Such aspects can be dealt with by means of the integration of physically unclonable functions (PUFs) technologies, exploited for the digital identification of IoT devices, with a DLT-based solution, devised to track interactions among heterogeneous systems. We describe the architecture of the proposed infrastructure which relies on state-of-the-art blockchain technologies which are the basis of Decentralized Autonomous Organizations (DAO).

Tokenized assets can provide relevant advantages in terms of accessibility and market size when compared with the original assets capabilities. Furthermore, when tokens are based on the public blockchain, they natively gain peer-to-peer notarization ability, which is the required feature to grant peer-to-peer market accessibility. We attribute these motivations as the main reason of the recent boost of the token adoption. With this work we first analyze some relevant aspects of the main available tokenization approaches, such as the IoT-based, and then we propose a new concept architecture that aim to combine the best side of each approach into a new hybrid solution that use both on-chain and off-chain transactions to being suitable for any kind of physical asset.

Home automation is not a new concept and has been around since the 1970s. Home automation systems (HAS) adoption is increasing since the users want to benefit from such features that allow them to control their home appliances from a single place in their daily lives. However, with the HAS adaptation increase, security issues are also becoming a concern. In today’s world, hackers do not need to travel to target homes and can easily target homes through virtual attacks. Security is a significant factor that users consider when adopting a smart home. This chapter reviews the literature on HAS security issues, solutions, and future challenges.