Internet of Things

As the deployment of Internet of Things (IoT) is experiencing an exponential growth, it is no surprise that many recent cyber attacks are IoT-enabled: The attacker initially exploits some vulnerable IoT technology as a first step towards compromising a critical system that is connected, in some way, with the IoT. For some sectors, like industry, smart grids, transportation and medical services, the significance of such attacks is obvious, since IoT technologies are part of critical backend systems. However, in sectors where IoT is usually at the enduser side, like smart homes, such attacks can be underestimated, since not all possible attack paths are examined. In this paper we survey IoT-enabled cyber attacks, found in all application domains since 2010. For each sector, we emphasize on the latest, verified IoT-enabled attacks, based on known real-world incidents and published proof-of-concept attacks. We methodologically analyze representative attacks that demonstrate direct, indirect and subliminal attack paths against critical targets. Our goal is threefold: (i) To assess IoT-enabled cyber attacks in a risk-like approach, in order to demonstrate their current threat landscape; (ii) To identify hidden and subliminal IoT-enabled attack paths against critical infrastructures and services, and (iii) To examine mitigation strategies for all application domains.

In Industrial Internet of Things (IIoT) scenarios, where a plethora of IoT technologies coexist with consolidated industrial infrastructures, the integration of security mechanisms that provide protection against cyber-security attacks becomes a critical challenge. Due to the stealthy and persistent nature of some of these attacks, such as Advanced Persistent Threats, it is crucial to go beyond traditional Intrusion Detection Systems for the traceability of these attacks. In this sense, Opinion Dynamics poses a novel approach for the correlation of anomalies, which has been successfully applied to other network security domains. In this paper, we aim to analyze its applicability in the IIoT from a technical point of view, by studying its deployment over different IIoT architectures and defining a common framework for the acquisition of data considering the computational constraints involved. The result is a beneficial insight that demonstrates the feasibility of this approach when applied to upcoming IIoT infrastructures.

Crowdsourcing can be a powerful weapon against cyberattacks in 5G networks. In this paper we analyse this idea in detail, starting from the use cases in crowdsourcing focused on security, and highlighting those areas of a 5G ecosystem where crowdsourcing could be used to mitigate local and remote attacks, as well as to discourage criminal activities and cybercriminal behaviour. We pay particular attention to the capillary network, where an infinite number of IoT objects coexist. The analysis is made considering the different participants in a 5G IoT ecosystem.

The Internet of Things (IoT) and Edge Computing are starting to go hand in hand. By providing cloud services close to end-users, edge paradigms enhance the functionality of IoT deployments, and facilitate the creation of novel services such as augmented systems. Furthermore, the very nature of these paradigms also enables the creation of a proactive defense architecture, an immune system, which allows authorized immune cells (e.g., virtual machines) to traverse edge nodes and analyze the security and consistency of the underlying IoT infrastructure. In this article, we analyze the requirements for the development of an immune system for the IoT, and propose a security architecture that satisfies these requirements. We also describe how such a system can be instantiated in Edge Computing infrastructures using existing technologies. Finally, we explore the potential application of immune systems to other scenarios and purposes.

Recent news have raised concern regarding the security on the IoT field. Vulnerabilities in devices are arising and honeypots are an excellent way to cope with this problem. In this work, current solutions for honeypots in the IoT context, and other solutions adaptable to it are analyzed in order to set the basis for a methodology that allows deployment of IoT honeypot.

Edge Computing paradigms are expected to solve some major problems affecting current application scenarios that rely on Cloud computing resources to operate. These novel paradigms will bring computational resources closer to the users and by doing so they will not only reduce network latency and bandwidth utilization but will also introduce some attractive context-awareness features to these systems. In this paper we show how the enticing features introduced by Edge Computing paradigms can be exploited to improve security and privacy in the critical scenario of vehicular networks (VN), especially existing authentication and revocation issues. In particular, we analyze the security challenges in VN and describe three deployment models for vehicular edge computing, which refrain from using vehicular- to-vehicular communications. The result is that the burden imposed to vehicles is considerably reduced without sacrificing the security or functional features expected in vehicular scenarios.

The Internet of Things (IoT) is an environment of interconnected entities, that are identifiable, usable and controllable via the Internet. Trust is necessary in a system such as IoT as the entities involved should know the effect of interacting with other entities. Moreover, the entities must also be able to trust a system to reliably use it. An IoT system is composed of different entities from different vendors, each of them with a different purpose and a different lifecycle. So considering trust in the whole IoT system lifecycle is useful and necessary to guarantee a good service for the whole system. The heterogeneity and dynamicity of this field make it difficult to ensure trust in IoT. We propose a trust by design framework for including trust in the development of an IoT entity considering all the phases of the life-cycle. It is composed of the K-Model and transversal activities.

The Internet of Things (IoT) is an environment of interconnected entities, which are identifiable, usable and controllable via the Internet. Trust is useful for a system such as the IoT as the entities involved would like to know how the other entities they have to interact with are going to perform.
When developing an IoT entity, it will be desirable to guarantee trust during its whole life cycle. Trust domain is strongly dependent on other domains such as security and privacy.
To consider these domains as a whole and to elicit the right requirements since the first phases of the System Development Life Cycle (SDLC) is a key point when developing an IoT entity.
This paper presents a requirements elicitation method focusing on trust plus other domains such as security, privacy and usability that increase the trust level of the IoT entity developed. To help the developers to elicit the requirements, we propose a JavaScript Notation Object (JSON) template containing all the key elements that must be taken into consideration.
We emphasize on the importance of the concept of traceability. This property permits to connect all the elicited requirements guaranteeing more control on the whole requirements engineering process.

With the ever-increasing number of smart home devices, the issues related to these environments are also growing. With an ever-growing attack surface, there is no standard way to protect homes and their inhabitants from new threats. The inhabitants are rarely aware of the increased security threats that they are exposed to and how to manage them. To tackle this problem, we propose a solution based on segmented architectures similar to the ones used in industrial systems. In this approach, the smart home is segmented into various levels, which can broadly be categorised into an inner level and external level. The external level is protected by a firewall that checks the communication from/to the Internet to/from the external devices. The internal level is protected by an additional firewall that filters the information and the communications between the external and the internal devices. This segmentation guarantees a trusted environment between the entities belonging to the internal network. In this paper, we propose an adaptive trust model that checks the behaviour of the entities and, through this model, in case the entities violate trust rules they can be put in quarantine or banned from the network.

In recent times, smart home devices like Amazon Echo and Google Home have reached mainstream popularity. These devices are intrinsically intrusive, being able to access user’s personal information. There are growing concerns about indiscriminate data collection and invasion of user privacy in smart home devices. Improper trust assumptions and security controls can lead to unauthorized access of the devices, which can have severe consequences (i.e. safety risks). In this paper, we analysed the behaviour of smart home devices with respect to trust relationships. We set up a smart home environment to evaluate how trust is built and managed. Then, we performed a number of interaction tests with different types of users (i.e. owner, guests). As a result, we were able to assess the effectiveness of the provided security controls and identify some relevant security issues. To address them, we defined a trust model and proposed a solution based on it for securing smart home devices.

One of the biggest challenges in IoT-forensics is the analysis and correlation of heterogeneous digital evidence, to enable an effective understanding of complex scenarios. This paper defines a methodology for extracting unique objects (e.g., representing users or devices) from the files of a case, defining the context of the digital investigation and increasing the knowledge progressively, using additional files from the case (e.g. network captures). The solution includes external searches using open source intelligence (OSINT) sources when needed. In order to illustrate this approach, the proposed methodology is implemented in the JSON Users and Devices analysis (JUDAS) tool, which is able to generate the context from JSON files, complete it, and show the whole context using dynamic graphs. The approach is validated using the files in an IoT-Forensic digital investigation where an important set of potential digital evidence extracted from Amazon’s Alexa Cloud is analysed.

Personal devices contain electronic evidence associated with the behaviour of their owners and other devices in their environment, which can help clarify the facts of a cyber-crime scene. These devices are usually analysed as containers of proof. However, it is possible to harness the boom of personal devices to define the concept of digital witnesses, where personal devices are able to actively acquire, store, and transmit digital evidence to an authorised entity, reliably and securely. This article introduces this novel concept, providing a preliminary analysis on the management of digital evidence and the technologies that can be used to implement it with security guarantees in IoT environments. Moreover, the basic building blocks of a digital witness are defined.

IoT-Forensics is a novel paradigm for the acquisition of electronic evidence whose operation is conditioned by the peculiarities of the Internet of Things (IoT) context. As a branch of computer forensics, this discipline respects the most basic forensic principles of preservation, traceability, documentation, and authorization. The digital witness approach also promotes such principles in the context of the IoT while allowing personal devices to cooperate in digital investigations by voluntarily providing electronic evidence to the authorities. However, this solution is highly dependent on the willingness of citizens to collaborate and they may be reluctant to do so if the sensitive information within their personal devices is not sufficiently protected when shared with the investigators. In this paper, we provide the digital witness approach with a methodology that enables citizens to share their data with some privacy guarantees. We apply the PRoFIT methodology, originally defined for IoT-Forensics environments, to the digital witness approach in order to unleash its full potential. Finally, we show the feasibility of a PRoFIT-compliant digital witness with two use cases.

In the Internet of Things, services can be provisioned using centralized architectures, where central entities acquire, process, and provide information. Alternatively, distributed architectures, where entities at the edge of the network exchange information and collaborate with each other in a dynamic way, can also be used. In order to understand the applicability and viability of this distributed approach, it is necessary to know its advantages and disadvantages – not only in terms of features but also in terms of security and privacy challenges. The purpose of this paper is to show that the distributed approach has various challenges that need to be solved, but also various interesting properties and strengths.

Security must be a primary concern when engineering Future Internet (FI) systems and applications. In order to achieve secure solutions, we need to capture security requirements early in the Software Development Life Cycle (SDLC). Whereas the security community has traditionally focused on providing tools and mechanisms to capture and express hard security requirements (e.g. confidentiality), little attention has been paid to other important requirements such as trust and reputation. We argue that these soft security requirements can leverage security in open, distributed, heterogeneous systems and applications and that they must be included in an early phase as part of the development process. In this paper we propose a UML extension for specifying trust and reputation requirements, and we apply it to an eHealth case study.

If a wireless sensor network (WSN) is to be completely integrated into the Internet as part of the Internet of Things (IoT), it is necessary to consider various security challenges, such as the creation of a secure channel between an Internet host and a sensor node. In order to create such a channel, it is necessary to provide key management mechanisms that allow two remote devices to negotiate certain security credentials (e.g. secret keys) that will be used to protect the information flow. In this paper we will analyse not only the applicability of existing mechanisms such as public key cryptography and pre-shared keys for sensor nodes in the IoT context, but also the applicability of those link-layer oriented key management systems (KMS) whose original purpose is to provide shared keys for sensor nodes belonging to the same WSN.

In the Internet of Things (IoT) concept, devices communicate autonomously with applications in the Internet. A significant aspect of IoT that makes it stand apart from present-day networked devices and applications is a) the very large number of devices, produced by diverse makers and used by an even more diverse group of users; b) the applications residing and functioning in what were very private sanctums of life e.g. the car, home, and the people themselves. Since these diverse devices require high-level security, an operational model for an IoT system is required, which has built-in security. We have proposed the societal model as a simple operational model. The basic concept of the model is borrowed from human society – there will be infants, the weak and the handicapped who need to be protected by guardians. This natural security mechanism works very well for IoT networks which seem to have inherently weak security mechanisms. In this paper, we discuss the requirements of the societal model and examine its feasibility by doing a proof-of-concept implementation.

A personal network (PN) should enable the collaboration of user’s devices and services in a flexible, self-organizing and friendly manner. For such purpose, the PN must securely accommodate heterogeneous technologies with uneven computational and communication resources. In particular, personal RFID tags can enable seamless recognition of user’s context, provide user authentication and enable novel services enhancing the quality and quantity of data handled by the PN. However, the highly constrained features of common RFID tags and their passive role in the network highlights the need of an adequate secure communication model with personal tags which enables their participation as a member of the PN. In this paper, we present our concept of PN, with special emphasis on the role of RFID and sensor networks, and define a secure architecture for PNs including methods for the secure access to context-aware technologies from both local PN members and the Internet of Things. The PN architecture is designed to support differentiated security mechanisms to maximize the level of security for each type of personal device. Furthermore, we analyze which security solutions available in the literature can be adapted for our architecture, as well as the challenges and security mechanisms still necessary in the secure integration of personal tags.