Future homes will employ potentially hundreds of Internet of Things (IoT) devices whose sensors may inadvertently leak sensitive information. A previous Communications Inside Risks column ("The Future of the Internet of Things," Feb. 2017) discusses how the expected scale of the IoT introduces threats that require considerations and mitigations.2 Future homes are an IoT hotspot that will be particularly at risk. Sensitive information such as passwords, identification, and financial transactions are abundant in the home--as are sensor systems such as digital assistants, smartphones, and interactive home appliances that may unintentionally capture this sensitive information. IoT device manufacturers should employ sensor sensor permissioning systems to limit applications access to only sensor data required for operation, reducing the risk that malicious applications may gain sensitive information. For example, a simple notepad application should not have microphone access.
Over the last decade, IoT platforms have been developed into a global giant that grabs every aspect of our daily lives by advancing human life with its unaccountable smart services. Because of easy accessibility and fast-growing demand for smart devices and network, IoT is now facing more security challenges than ever before. There are existing security measures that can be applied to protect IoT. However, traditional techniques are not as efficient with the advancement booms as well as different attack types and their severeness. Thus, a strong-dynamically enhanced and up to date security system is required for next-generation IoT system. A huge technological advancement has been noticed in Machine Learning (ML) which has opened many possible research windows to address ongoing and future challenges in IoT. In order to detect attacks and identify abnormal behaviors of smart devices and networks, ML is being utilized as a powerful technology to fulfill this purpose. In this survey paper, the architecture of IoT is discussed, following a comprehensive literature review on ML approaches the importance of security of IoT in terms of different types of possible attacks. Moreover, ML-based potential solutions for IoT security has been presented and future challenges are discussed.
Several lofty predictions about the number of connected devices in the IoT begin this year; many developments directly impacting its adoption rates will flourish in the coming 10 years, rendering it the premier expression of data management. A number of trends in edge computing, 5G, cyber security, Artificial Intelligence, and digital twins will significantly alter what the IoT means to enterprises. Opportunities for monetization will proliferate as, perhaps, will the potential for misuse. Exactly which of these trajectories will dominate remains to be seen. "The creators of IoT apps and the whole platform, including hardware and software, are coming up with ways we never envisioned for what IoT systems could do," observed Cybera President Cliff Duffey.
Nowadays, devices are equipped with advanced sensors with higher processing/computing capabilities. Further, widespread Internet availability enables communication among sensing devices. As a result, vast amounts of data are generated on edge devices to drive Internet-of-Things (IoT), crowdsourcing, and other emerging technologies. The collected extensive data can be pre-processed, scaled, classified, and finally, used for predicting future events using machine learning (ML) methods. In traditional ML approaches, data is sent to and processed in a central server, which encounters communication overhead, processing delay, privacy leakage, and security issues. To overcome these challenges, each client can be trained locally based on its available data and by learning from the global model. This decentralized learning structure is referred to as Federated Learning (FL). However, in large-scale networks, there may be clients with varying computational resource capabilities. This may lead to implementation and scalability challenges for FL techniques. In this paper, we first introduce some recently implemented real-life applications of FL. We then emphasize on the core challenges of implementing the FL algorithms from the perspective of resource limitations (e.g., memory, bandwidth, and energy budget) of client clients. We finally discuss open issues associated with FL and highlight future directions in the FL area concerning resource-constrained devices.
Smart home devices are designed to make our lives easier, but they also make it easier for hackers to infiltrate our lives. The FBI has sent out a warning that'hackers can use those innocent devices to do a virtual drive-by of your digital life.' The US intelligence agency urges users to regularly change passwords, check for firmware updates and never have two devices on the same network. Digital assistants, smart watches, fitness trackers, home security devices, thermostats, refrigerators, and even light bulbs are all on the list of devices that can be infiltrated by cybercriminals. And if these devices, among other smart home technology, are not properly protected, they can be used by hackers to'do a virtual drive-by of your digital life.' Samsung are developing an interactive kitchen that includes a fridge, oven and TV.
The Internet of Things (IoT) is actively shaping both the industrial and consumer worlds. Smart tech finds its way to every business and consumer domain there is -- from retail to healthcare, from finances to logistics -- and a missed opportunity strategically employed by a competitor can easily qualify as a long-term failure for companies who don't innovate . The year 2020 will hit all 4 components of IoT Model: Sensors, Networks (Communications), Analytics (Cloud), and Applications, with different degrees of impact. By 2020, the Internet of Things (IoT) is predicted to generate an additional $344B in revenues, as well as to drive $177B in cost reductions. IoT and smart devices are already increasing the performance metrics of major US-based factories.
The Social Internet of Things (SIoT), integration of Internet of Things and Social networks paradigms, has been introduced to build a network of smart nodes which are capable of establishing social links. In order to deal with misbehavioral service provider nodes, service requestor nodes must evaluate their trustworthiness levels. In this paper, we propose a novel trust management mechanism in the SIoT to predict the most reliable service provider for a service requestor, that leads to reduce the risk of exposing to malicious nodes. We model an SIoT with a flexible bipartite graph (containing two sets of nodes: service providers and requestors), then build the corresponding social network among service requestor nodes, using Hellinger distance. After that, we develop a social trust model, by using nodes' centrality and similarity measures, to extract behavioral trust between the network nodes. Finally, a matrix factorization technique is designed to extract latent features of SIoT nodes to mitigate the data sparsity and cold start problems. We analyze the effect of parameters in the proposed trust prediction mechanism on prediction accuracy. The results indicate that feedbacks from the neighboring nodes of a specific service requestor with high Hellinger similarity in our mechanism outperforms the best existing methods. We also show that utilizing social trust model, which only considers the similarity measure, significantly improves the accuracy of the prediction mechanism. Furthermore, we evaluate the effectiveness of the proposed trust management system through a real-world SIoT application. Our results demonstrate that the proposed mechanism is resilient to different types of network attacks and it can accurately find the proper service provider with high trustworthiness.
The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resource-constrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML- and DL-based IoT security.
The Internet of Things is a source of consistent upheaval for the technological and operational aspects of our societies. Wave upon wave of technological innovation has brought us to the point at which we currently find ourselves. When it comes to technological innovation, especially within the IoT, privacy and security are two of the most relevant and exciting fields of development currently being discussed. In this article, we'll be looking at ten security and privacy trends within the Internet of Things that are becoming increasingly widespread in various industrial and commercial sectors around the world. Some of the trends featured in this list will focus primarily on security aspects within the IoT, whereas others may be more concerned with the issues of privacy such technologies face.
The concept of social machines is increasingly being used to characterise various socio-cognitive spaces on the Web. Social machines are human collectives using networked digital technology which initiate real-world processes and activities including human communication, interactions and knowledge creation. As such, they continuously emerge and fade on the Web. The relationship between humans and machines is made more complex by the adoption of Internet of Things (IoT) sensors and devices. The scale, automation, continuous sensing, and actuation capabilities of these devices add an extra dimension to the relationship between humans and machines making it difficult to understand their evolution at either the systemic or the conceptual level. This article describes these new socio-technical systems, which we term Cyber-Physical Social Machines, through different exemplars, and considers the associated challenges of security and privacy.