Blockchain-Powered Deep Learning for Internet of Things With Cloud-Assisted Secure Smart Home Networks

Internet of Things (IoT) devices and applications are growing for smart home use cases. Although various security risks have been identified in smart home devices, studies reveal that the majority of the smart home devices released in the market have multiple security vulnerabilities and, in many cases, no security mechanisms are implemented. These vulnerabilities of smart home networks are mostly attributed to user unawareness of security. Many users are not aware and do not have the technical skills or knowledge of what is happening on their devices in their smart home environment. When an intruder connects to their device or takes advantage of it, users are unaware of such a connection. This paper introduces a configuration tool that helps users to create device interaction rules that are added to an existing Network Intrusion Detection System (NIDS) in the smart home network. These rules generate alarms that alert the user if unwanted interaction takes place between their devices or from an external network, and in this way increases user awareness of the security of their smart home network.

This paper presents an analytical review about smart home network, their working and technologies from a security standpoint. It highlights various security flaws in a different smart home networks. It clearly explain, why a smart home is such an easy target for the attackers, it also discuss the role of interfaces in a smart home network by describing the scenario in which smart home network can be easily trapped. It also explains various types of home automation techniques along with the attacks which are quite more vulnerable to the smart home networks. As there of now there are many vulnerabilities which are exploiting the smart home network but no strong defensive mechanism has been developed till yet. It also indicates the basic components of a smart home network and the possible attacks which can be happen on a smart home networks.

Advances in electronics and connectivity have enabled a wide range of applications that can harness data collection for better decision making and an improved lifestyle. The Internet of Things (IoT) provides the communication infrastructure that allows devices with sensing and control capabilities to be connected within a home network. Smart home systems are considered one of the prominent applications in IoT, where it is possible to control home devices to achieve a better usage in terms of cost and comfort. However, smart home networks contain a wide range of devices and finding an optimal schedule for their working hours is an NP-hard problem. Hence, rather than using mathematical optimization to find optimal solutions, this paper proposes a modeling and simulation methods in order to provide good decisions and recommendations for devices’ scheduling. Discrete Event System Specification (DEVS) formalism is used to develop a model of a smart home network. The devices are categorized into two groups: monitoring devices and control devices. Monitoring devices include sensors that capture climate, energy, power, performance, and occupant’s behavioral data. Control devices send signals remotely for setting and controlling different devices in the smart home network. The behavior in terms of power usage and cost is simulated under different scenarios and settings. The simulation results show that less energy consumption can be achieved if users adopt a behavior where the schedule of three devices is changed every week. As a result, the proposed method can be utilized to make better decisions in setting devices parameters and evaluating the performance of the smart devices network under different conditions, scenarios, and settings.

The Internet of Things (IoT) application is visible in all aspects of humans’ day-to-day affairs. The demand for IoT is growing at an unprecedented rate, from wearable wristwatches to autopilot cars. The smart home has also seen significant advancements to improve the quality of lifestyle. However, the security and privacy of IoT devices have become primary concerns as data is shared among intelligent devices and over the internet in a smart home network. There are several attacks - node capturing attack, sniffing attack, malware attack, boot phase attack, etc., which are conducted by adversaries to breach the security of smart homes. The security breach has a negative impact on the tenants' privacy and prevents the availability of smart home services. This article presents smart homes' most common security attacks and mitigation techniques.

Technology has become a critical part of our daily activities and routine life in the present day scenario. The need of the hour is to devise a smart home that will be able to control the operation of electronic devices via a smartphone. This concept, while absurd to imagine, can be easily implemented efficiently using a package tracking software which includes IoT functions for control and simulation of a smart home network. IoT technology has had a huge boom in the tech industry in the recent past. IoT, better known as Internet of Things, can be applied to several real time scenarios, such as home automation, treatment, campus, office, etc. In this paper, we will be focusing on a safe home automation system that will be including modules such as: smoke detection, humiture monitoring, motion detection, temperature monitoring and solar panel controlling using smart battery. The aim of our research is to devise a simulation network of smart devices which will be in control of the end-user remotely and implement the concept of smart home automation. This project will make use of the Cisco Packet Tracer to monitor the IoT devices in the smart home network. Simulation results have shown that these smart devices can be connected in a network to the home portal and objects can be successfully monitored which proves that such a model can be used for real life implementation in the user’s home.

The growing number of Internet of Things (IoT) objects and the operational and security challenges in IoT systems are encouraging researchers to design suitable IoT architecture. Enormous data generated in the IoT environment face several kinds of security and privacy challenges. IoT system generally suffers from several issues like data storage, safety, privacy, integrity, transparency, trust, and single point of failure. IoT environment is emerging with several solutions to resolve these problems. The main objective of this paper is to design a cloud-blockchain-based secure IoT architecture that provides advanced and efficient storage and security solutions to IoT ecosystem. Blockchain technology appears to be a decent choice to resolve such kinds of problems. Blockchain technology uses a hash-based cryptographic technique for information security and integrity. Cloud computing provides advanced storage solutions with several remote services to store, compute and analyze the data. The proposed IoT architecture is based on the integration of cloud and blockchain services, which aim to provide transparent, decentralized, and trustworthy and secure storage solutions. In addition to the standard layers (perception layer, network layer, processing layer, and application layer) the proposed IoT architecture in the present paper includes a service layer, a security layer, and a parallel management and control layer, which focus on the security and management of the entire IoT infrastructure.

With the rapid growth of digital data and inter-connected devices, the integration of blockchain and Internet of Things technology offers a promising chance to revolutionize data management approaches. With today's global connectivity, the management and securing of ever-increasing amounts the amount of data produced by Internet of Things (IoT) devices is becoming an increasing concern. This study investigates a new solution to address such a concern by integrating blockchain technology into IoT systems. The aim is to develop a strong framework that takes advantage of the decentralized and secure character of blockchain and the wall-to-wall connectivity offered by the IoT. This integration is intended to address important needs such as data integrity, The integration aims to tackle crucial issues like data integrity, privacy, and unauthorized access. With the connectivity and sensing capabilities of the IoT, along with the blockchain technology of decentralized consensus and immutable ledgers, businesses are able to develop secure systems to retrieve, transfer, and store such data. In the experiments conducted on the Ethereum blockchain platform, it has been found that the blockchain and IoT technology solution not only secures data but also facilitates information management. The experiment results demonstrate that the proposed solution is able to protect against tampering, increase the transparency of transactions, and to minimize the chances of unauthorized access. The framework is scalable and highly adaptable, and it is therefore a promising solution for various applications. This framework employs an innovative approach to ensure secure and efficient data management.

One of the modern trends in the development of information and communication technologies that have evolved into the Internet of Things (IoT) paradigm is the progressive idea of using blockchain technology in IoT. This trend is described in a large number of modern literature sources, which point out the significant advantages of using blockchain, primarily related to decentralization, immutability, transparency and security. This allows us to conclude that the implementation of blockchain technology in IoT information and communication systems is highly relevant. However, the authors of different studies describe significant problems, including the problem of scalability of the blockchain itself, as well as the problems of energy efficiency and limited resources in IoT devices. Due to these problems, the implementation of blockchain technology in the Internet of Things is becoming a challenge and determines the relevance and feasibility of researching this topic and finding effective solutions related to the integration of two very powerful technologies in modern information and communication systems. The article presents an analytical overview of the issues of integrating blockchain technology into IoT, in particular, the use of the Proof-of-Work (PoW) consensus algorithm in IoT and the use of alternative blockchain protocols, including IOTA, Ethereum Light Client and Litecoin, which allow increasing the efficiency of performing tasks in IoT infrastructure, taking into account the limited resources of devices; existing problems and implementation methods, such as the use of light clients and proxy servers. The article also considers the feasibility of using a full blockchain node on IoT devices and ways to increase system efficiency by pre-processing data before sending it to the blockchain network. The article also highlights the problem of insufficient implementation of blockchain technology in various spheres of human life, as well as the search for the reasons for this problem. The results of the analytical review make it possible to substantiate the purpose and advantages of integrating blockchain technology into the Internet of Things, as well as to summarize the main issues and substantiate a number of tasks for relevant and progressive scientific research in the field of modern information and communication systems.

The development of Internet of Things (IoT) makes human life more intelligent, and the interconnection of all things has become a reality. However, the surge in the number of devices and centralized management brings severe challenges to IoT, such as single point of failure, poor security, privacy leakage, and low reliability. Due to the decentralization, verifiability and privacy protection of blockchain federated learning, some blockchain federated learning schemes have been proposed to solve these problems, but bring new challenges, such as device privacy leakage and heavy security computing load. In this paper, we propose a new decentralized, secure and verifiable consortium blockchain federated learning privacy protection scheme, named LPBFL, which realizes lightweight computing while ensuring the privacy of the local model and dataset of the device. To achieve lightweight privacy protection, LPBFL adopts the Paillier encryption and the newly designed lightweight digital signature and batch verification algorithm. Additionally, considering that devices upload invalid or even toxic local models intentionally or unintentionally, we design a device reputation selection mechanism to make blockchain federated learning more efficient. Finally, the theoretical analysis proves the security of LPBFL and verifies the unforgeability of the proposed digital signature. Comprehensive comparisons and extensive experiments demonstrate that our LPBFL has significant advantages in multiple aspects.

Over recent years industries have faced the problem of how to connect devices to ‘speak’ to each other with minimum wiring. Philips Semiconductors faced this problem when they needed to connect many ICs together. The Automotive Industry faced the same problem when it needed to connect tens of microcontrollers in each car. Recently, with smart homes, the problem has started to be part of each home. For instance, you may want to build your smart home with accessories from different manufacturers and you want the devices to ‘speak’ to each other. Added to that, you may want to control them from a central App or voice assist. Solutions for this problem started with the introduction of Inter Integrated Circuits (IIC) and Controller Area Networks (CAN). Both solutions are wired networks that allow ICs and microcontrollers to be connected in a network to communicate together. In smart home automation, a number of common smart home automation protocols that allow different devices to speak and communicate together have appeared during the last few decades. Some of the smart home protocols come under the umbrella of what is called the “Internet of Things (IoT)”. The proposed protocols can be grouped into wired networks e.g. X10, UPB; wireless or radio networks as ZigBee, Z-Wave, Bluetooth; or dual (wired and radio) such as Insteon. This book introduces to the reader some of the most popular Microcontroller and Smart home networks. The book covers in detail the following protocols: • I2C • I3C • CAN • ZigBee • ZigBee Pro • Z-Wave • Bluetooth Wi-Fi, WiMax and Insteon are part of our companion book “Serial Communication Protocols and Standards”. This book gives detailed comparisons between the various protocols. To complete the knowledge of the reader, the book gives in the last chapter a short summary on the protocols that we did not fully cover in this volume: Ethernet, Thread, Insteon, X10 and UPB.

The Internet of Things (IoT) is developing as a novel phenomenon that is applied in the growth of several crucial applications. However, these applications continue to function on a centralized storage structure, which leads to several major problems, such as security, privacy, and a single point of failure. In recent years, blockchain (BC) technology has become a pillar for the progression of IoT-based applications. The BC technique is utilized to resolve the security, privacy, and single point of failure (third-part dependency) issues encountered in IoT applications. Conversely, the distributed denial of service (DDoS) attacks on mining pools revealed the existence of vital fault lines amongst the BC-assisted IoT networks. Therefore, the current study designs a hybrid Harris Hawks with sine cosine and a deep learning-based intrusion detection system (H3SC-DLIDS) for a BC-supported IoT environment. The aim of the presented H3SC-DLIDS approach is to recognize the presence of DDoS attacks in the BC-assisted IoT environment. To enable secure communication in the IoT networks, BC technology is used. The proposed H3SC-DLIDS technique designs a H3SC technique by integrating the concepts of Harris Hawks optimization (HHO) and sine cosine algorithm (SCA) for feature selection. For the intrusion detection process, a long short-term memory auto-encoder (LSTM-AE) model is utilized in this study. Finally, the arithmetic optimization algorithm (AOA) is implemented for hyperparameter tuning of the LSTM-AE technique. The proposed H3SC-DLIDS method was experimentally validated using the BoT-IoT database, and the results indicate the superior performance of the proposed H3SC-DLIDS technique over other existing methods, with a maximum accuracy of 99.05%.

The IoT (Internet of Things) technology has been widely adopted in recent years and has profoundly changed the people's daily lives. However, in the meantime, such a fast-growing technology has also introduced new privacy issues, which need to be better understood and measured. In this work, we look into how private information can be leaked from network traffic generated in the smart home network. Although researchers have proposed techniques to infer IoT device types or user behaviors under clean experiment setup, the effectiveness of such approaches become questionable in the complex but realistic network environment, where common techniques like Network Address and Port Translation (NAPT) and Virtual Private Network (VPN) are enabled. To this aim, we propose a traffic analysis framework based on sequence-learning techniques like LSTM and leveraged the temporal relations between packets for the attack of device identification. We evaluated it under different environment settings (e.g., pure-IoT and noisy environment with multiple non-IoT devices). The results showed our framework was able to differentiate device types with a high accuracy. This result suggests IoT network communications pose prominent challenges to users' privacy, even when they are protected by encryption and morphed by the network gateway. As such, new privacy protection methods on IoT traffic need to be developed towards mitigating this new issue.

The digital transformation has already resulted in extensive change in our daily life, such as the evolution of smart cities, smart offices as well as smart homes. Smart homes suddenly become a more affordable option whereby home networking and other gadgets began to appear in a reasonable price and easy to find. In other words, smart homes have an increasing demand for IoT devices in home networking connections. More connections of devices may lead to network congestion. Smart home users often face service interruptions or connectivity issues. The project sets up a smart home network with multiple access points using Raspberry Pi. The devices are categorized according to the bandwidth usage to balance the network load and the network performance is measured. PRTG monitoring tools are used to capture the ping time for each connected device. Although overall results are considered poor due to the intermittent network access, there are a few time intervals that show significant improvement. In conclusion, the latency for each smart device in a smart home network has improved significantly when connecting to a multiple AP. More APs with smart channel assignment can be developed in the future and thorough experiments can be executed.

The Internet of Things (IoT) is a rapidly growing emerging topic of social, economic and technical significance. The spread of today’s wireless information and communication technologies has changed people’s lifestyle and social interactions - the next frontier is the smart home environments. A smart home consists of low capacity devices (sensors, for example) and wireless networks. These devices are networked together and they need to all work together as a secure system. The proposed scheme aims to offer a secure session key establishment scheme which can then be implemented in smart home networks. Every node (sensor and control unit) uses a small authentication token, along with a random number to establish a session key which is both secure and dynamic. A few popular attacks like denial-of-service and eavesdropping are prevented by the proposed scheme.

The European research project GHOST challenges the traditional cyber security solutions for the Internet of Things (IoT) sector by exploiting novel technologies, such as blockchain, to provide resilience and integrity of decision making on the communication exchange in a smart home context. When it comes to novel cyber security solutions for extremely heterogeneous environments like IoT and smart homes, the key focus is typically given to the understanding of network activities and elimination of suspicious traffic. The GHOST project adds an extra dimension to this approach by integrating blockchain technology at its core decision mechanism. On a daily basis, each GHOST installation is encountering malicious behaviour and suspicious IoT communications, where easy information sharing with other installations, as well as decentralised decision making, are mandatory features for the efficient protection of the end-user. GHOST's Smart Contracts (SC) are designed to tackle in an easy, yet productive way, the reporting on suspicious IP addresses which the IoT devices in a smart home are trying to communicate with. Two variations of blacklisting smart contracts are presented in this paper, covering a diverse spectrum of possible attack vectors while closely following the Privacy by Design (PbD) principles. A reputation scoring scheme for malicious IPs reporting is integrated in the SC, uncovering the implementation details on the penalisation of existing entries in case of malicious behaviour of reporting devices.