Biblio

Recent years have seen significant growth in the adoption of smart homes devices. These devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. There have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. The generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.

Internet of Things (IoT) now has extremely wide applications in many areas of life such as urban management, environmental management, smart shopping, and smart home. Because of the wide range of application fields, the IoT infrastructures are built differently. To make an IoT system indoor with high efficiency and more convenience, a case study for smart home security using Bluetooth Mesh approach is introduced. By using Bluetooth Mesh technology in home security, the user can open the door everywhere inside their house. The system work in a flexible way since it can extend the working range of network. In addition, the system can monitor the state of both the lock and any node in network by using a gateway to transfer data to cloud and enable a website-based interface.

Smart Security Solutions are in high demand with the ever-increasing vulnerabilities within the IT domain. Adjusting to a Work-From-Home (WFH) culture has become mandatory by maintaining required core security principles. Therefore, implementing and maintaining a secure Smart Home System has become even more challenging. ARGUS provides an overall network security coverage for both incoming and outgoing traffic, a firewall and an adaptive bandwidth management system and a sophisticated CCTV surveillance capability. ARGUS is such a system that is implemented into an existing router incorporating cloud and Machine Learning (ML) technology to ensure seamless connectivity across multiple devices, including IoT devices at a low migration cost for the customer. The aggregation of the above features makes ARGUS an ideal solution for existing Smart Home System service providers and users where hardware and infrastructure is also allocated. ARGUS was tested on a small-scale smart home environment with a Raspberry Pi 4 Model B controller. Its intrusion detection system identified an intrusion with 96% accuracy while the physical surveillance system predicts the user with 81% accuracy.

As a current disruptive and transformative technology, artificial intelligence is constantly infiltrating all aspects of production and life. However, with the in-depth development and application of artificial intelligence, the security challenges it faces have become more and more prominent. In the real world, attacks against intelligent systems such as the Internet of Things, smart homes, and driverless cars are constantly appearing, and incidents of artificial intelligence being used in cyber-attacks and cybercrimes frequently occur. This article aims to discuss artificial intelligence security issues and propose some countermeasures.

We present an intelligent system that focus on how to ensure the stability of ZigBee network automatically. First, we discussed on the character of ZigBee compared with WIFI. Pointed out advantage of ZigBee resides in security, stability, low power consumption and better expandability. Second, figuring out the shortcomings of ZigBee on application is that physical limitation of the frequency band and weak ability on diffraction, especially coming across a wall or a door in the actual environment of home. The third, to put forward a method which can be used to ensure the strength of ZigBee signal. The method is to detect the strength of ZigBee relay in advance. And then, to compare it with the threshold value which had been defined in previous. The threshold value of strength of ZigBee is the minimal and tolerable value which can ensure stable transmission of ZigBee. If the detected value is out of the range of threshold, system will prompt up warning message which can be used to hint user to add ZigBee reply between the original ZigBee node and ZigBee gateway.

The Internet of Things (IoT) will connect not only computers and mobile devices, but it will also interconnect smart buildings, houses, and cities, as well as electrical grids, gas plants, and water networks, automobiles, airplanes, etc. IoT will lead to the development of a wide range of advanced information services that are pervasive, cost-effective, and can be accessed from anywhere and at any time. However, due to the exponential number of interconnected devices, cyber-security in the IoT is a major challenge. It heavily relies on the digital identity concept to build security mechanisms such as authentication and authorization. Current centralized identity management systems are built around third party identity providers, which raise privacy concerns and present a single point of failure. In addition, IoT unconventional characteristics such as scalability, heterogeneity and mobility require new identity management systems to operate in distributed and trustless environments, and uniquely identify a particular device based on its intrinsic digital properties and its relation to its human owner. In order to deal with these challenges, we present a Blockchain-based Identity Framework for IoT (BIFIT). We show how to apply our BIFIT to IoT smart homes to achieve identity self-management by end users. In the context of smart home, the framework autonomously extracts appliances signatures and creates blockchain-based identifies for their appliance owners. It also correlates appliances signatures (low level identities) and owners identifies in order to use them in authentication credentials and to make sure that any IoT entity is behaving normally.

The growth of IoT devices during the last decade has led to the development of smart ecosystems, such as smart homes, prone to cyberattacks. Traditional security methodologies support to some extend the requirement for preserving privacy and security of such deployments, but their centralized nature in conjunction with low computational capabilities of smart home gateways make such approaches not efficient. Last achievements on blockchain technologies allowed the use of such decentralized architectures to support cybersecurity defence mechanisms. In this work, a blockchain framework is presented to support the cybersecurity mechanisms of smart homes installations, focusing on the immutability of users and devices that constitute such environments. The proposed methodology provides also the appropriate smart contracts support for ensuring the integrity of the smart home gateway and IoT devices, as well as the dynamic and immutable management of blocked malicious IPs. The framework has been deployed on a real smart home environment demonstrating its applicability and efficiency.

The technological evolution brought by the Internet of things (IoT) comes with new forms of cyber-attacks exploiting the complexity and heterogeneity of IoT networks, as well as, the existence of many vulnerabilities in IoT devices. The detection of compromised devices, as well as the collection and preservation of evidence regarding alleged malicious behavior in IoT networks, emerge as areas of high priority. This paper presents a blockchain-based solution, which is designed for the smart home domain, dealing with the collection and preservation of digital forensic evidence. The system utilizes a private forensic evidence database, where the captured evidence is stored, along with a permissioned blockchain that allows providing security services like integrity, authentication, and non-repudiation, so that the evidence can be used in a court of law. The blockchain stores evidences' metadata, which are critical for providing the aforementioned services, and interacts via smart contracts with the different entities involved in an investigation process, including Internet service providers, law enforcement agencies and prosecutors. A high-level architecture of the blockchain-based solution is presented that allows tackling the unique challenges posed by the need for digitally handling forensic evidence collected from IoT networks.

Our vision in this paper is that agency, as the individual ability to intervene and tailor the system, is a crucial element in building trust in IoT technologies. Following up on this vision, we will first address the issue of agency, namely the individual capability to adopt free decisions, as a relevant driver in building trusted human-IoT relations, and how agency should be embedded in digital systems. Then we present the main challenges posed by existing approaches to implement this vision. We show then our proposal for a model-based approach that realizes the agency concept, including a prototype implementation.

Internet of things is a network formed between two or more devices through internet which helps in sharing data and resources. IoT is present everywhere and lot of applications in our day-to-day life such as smart homes, smart grid system which helps in reducing energy consumption, smart garbage collection to make cities clean, smart cities etc. It has some limitations too such as concerns of security of the network and the cost of installations of the devices. There have been many researches proposed various method in improving the IoT systems. In this paper, we have discussed about the scope and limitations of IoT in various fields and we have also proposed a technique to secure offline architecture of IoT.

A Distributed Denial of Service (DDoS) attack is an attack that compromised the bandwidth of the whole network by choking down all the available network resources which are publically available, thus makes access to that resource unavailable. The DDoS attack is more vulnerable than a normal DoS attack because here the sources of attack origin are more than one, so users cannot even estimate how to detect and where to take actions so that attacks can be dissolved. This paper proposed a unique approach for DDoS detection using the shortest path algorithm. This Paper suggests that the remedy that must be taken in order to counter-affect the DDoS attack in a smart home network.

Internet of Thing (IoT) devices are being widely used in smart homes and organizations. An IoT device has some intended purposes, but may also have hidden functionalities. Typically, the device is installed in a home or an organization and the network traffic associated with the device is captured and analyzed to infer high-level functionality to the extent possible. However, such analysis is dynamic in nature, and requires the installation of the device and access to network data which is often hard to get for privacy and confidentiality reasons. We propose an alternative static approach which can infer the functionality of a device from vendor materials using Natural Language Processing (NLP) techniques. Information about IoT device functionality can be used in various applications, one of which is ensuring security in a smart home. We demonstrate how security policies associated with device functionality in a smart home can be formally represented using the NIST Next Generation Access Control (NGAC) model and automatically analyzed using Alloy, which is a formal verification tool. This will provide assurance to the consumer that these devices will be compliant to the home or organizational policy even before they have been purchased.

Recent advances in pervasive computing have caused a rapid growth of the Smart Home market, where a number of otherwise mundane pieces of technology are capable of connecting to the Internet and interacting with other similar devices. However, with the lack of a commonly adopted set of guidelines, several IT companies are producing smart devices with their own proprietary standards, leading to highly heterogeneous Smart Home systems in which the interoperability of the present elements is not always implemented in the most straightforward manner. As such, understanding the cyber risk of these cyber-physical systems beyond the individual devices has become an almost intractable problem. This paper tackles this issue by introducing a Smart Home reference architecture which facilitates security analysis. Being composed by three viewpoints, it gives a high-level description of the various functions and components needed in a domestic IoT device and network. Furthermore, this document demonstrates how the architecture can be used to determine the various attack surfaces of a home automation system from which its key vulnerabilities can be determined.

We are living in a society where technology is present everywhere we go. We are striving towards smart homes, smart cities, Internet of Things, Internet of Everything. Not so long ago, a password was all you needed for secure authentication. Nowadays, even the most complicated passwords are not considered enough. Multi-factor authentication is gaining more and more terrain. Complex system may also require more than one solution for real, strong security. The present paper proposes a framework based with MFA as a basis for access control and data analytics. Events within a cyber-physical system are processed and analyzed in an attempt to detect, prevent and mitigate possible attacks.

Wireless Sensor networks can be composed of smart buildings, smart homes, smart grids, and smart mobility, and they can even interconnect all these fields into a large-scale smart city network. Software-Defined Networking is an ideal technology to realize Internet-of-Things (IoT) Network and WSN network requirements and to efficiently enhance the security of these networks. Software defines Networking (SDN) is used to support IoT and WSN related networking elements, additional security concerns rise, due to the elevated vulnerability of such deployments to specific types of attacks and the necessity of inter-cloud communication any IoT application would require. This work is a study of different security mechanisms available in SDN for IoT and WSN network secure communication. This work also formulates the problems when existing methods are implemented with different networks parameters.

Cities are becoming increasingly smart as the Internet of Things (IoT) proliferates. With IoT devices interconnected, smart cities can offer novel and ubiquitous services as well as automate many of our daily lives (e.g., smart health, smart home). The abundance in the number of IoT devices leads to divergent types of security threats as well. One of such important attacks is the Distributed Denial of Service attack(DDoS). DDoS attacks have become increasingly common in the internet of things because of the rapid growth of insecure devices. These attacks slow down legitimate network requests. Although DDoS attacks were first reported in 1996, the sophistication of these attacks has increased significantly. In mid-August 2020, a 2 Terabytes per second(TBps) attack targeting critical infrastructure, such as finance, was reported. In the next two years, it is predicted that this number will double to 15 million attacks. Blockchain technology, whose development dates back to the advent of the internet, has become one of the most important advancements to come along since that time. Several applications can use this technology to secure exchanges. Using blockchain to mitigate DDoS attacks is discussed in this survey paper in diverse domains to date. Its purpose is to expose the strengths, weaknesses, and limitations of the different approaches to DDoS mitigation. As a research and development platform for DDoS mitigation, this paper will act as a central hub for a more comprehensive understanding of these approaches.

While Explanatory AI (XAI) is attracting increasing interest from academic research, most AI-based solutions still rely on black box methods. This is unsuitable for certain domains, such as smart homes, where transparency is key to gaining user trust and solution adoption. Moreover, smart homes are challenging environments for XAI, as they are decentralized systems that undergo runtime changes. We aim to develop an XAI solution for addressing problems that an autonomic management system either could not resolve or resolved in a surprising manner. This implies situations where the current state of affairs is not what the user expected, hence requiring an explanation. The objective is to solve the apparent conflict between expectation and observation through understandable logical steps, thus generating an argumentative dialogue. While focusing on the smart home domain, our approach is intended to be generic and transferable to other cyber-physical systems offering similar challenges. This position paper focuses on proposing a decentralized algorithm, called D-CAN, and its corresponding generic decentralized architecture. This approach is particularly suited for SISSY systems, as it enables XAI functions to be extended and updated when devices join and leave the managed system dynamically. We illustrate our proposal via several representative case studies from the smart home domain.

Deep Neural Networks (DNN) has gained great success in solving several challenging problems in recent years. It is well known that training a DNN model from scratch requires a lot of data and computational resources. However, using a pre-trained model directly or using it to initialize weights cost less time and often gets better results. Therefore, well pre-trained DNN models are valuable intellectual property that we should protect. In this work, we propose DeepTrace, a framework for model owners to secretly fingerprinting the target DNN model using a special trigger set and verifying from outputs. An embedded fingerprint can be extracted to uniquely identify the information of model owner and authorized users. Our framework benefits from both white-box and black-box verification, which makes it useful whether we know the model details or not. We evaluate the performance of DeepTrace on two different datasets, with different DNN architectures. Our experiment shows that, with the advantages of combining white-box and black-box verification, our framework has very little effect on model accuracy, and is robust against different model modifications. It also consumes very little computing resources when extracting fingerprint.

With the popularization and application of Internet of Things technology, the degree of intelligence of the home system is getting higher and higher. As an important part of the smart home, the security system plays an important role in protecting against accidents such as flammable gas leakage, fire, and burglary that may occur in the home environment. This design focuses on sensor signal acquisition and processing, wireless access, and cloud applications, and integrates Cypress’s new generation of PSoC 6 MCU, CYW4343W Wi-Fi and Bluetooth dual-module chips, and Amazon’s AWS cloud into smart home security System designing. First, through the designed air conditioning and refrigeration module, fire warning processing module, lighting control module, ventilation fan control module, combustible gas and smoke detection and warning module, important parameter information in the home environment is obtained. Then, the hardware system is connected to the AWS cloud platform through Wi-Fi; finally, a WEB interface is built in the AWS cloud to realize remote monitoring of the smart home environment. This design has a good reference for the design of future smart home security systems.

Home automation is an intelligent, functional as a unit system that facilitates home processes without unnecessarily complicating the user's life. Devices can be connected, which in turn connect and talk through a centralized control unit, which are accessible via mobile phones. These devices include lights, appliances, security systems, alarms and many other sensors and devices. This paper presents the design and implementation of a Bluetooth based smart home automation system which uses a Peripheral interface controller (PIC) microcontroller (16F1937) as the main processer and the appliances are connected to the peripheral ports of the microcontroller via relays. The circuit in the project was designed in Diptrace software. The PCB layout design was completed. The fully functional smart home prototype was built and demonstrated to functional.

This research work consists in to design a system of occupancy simulation in smart homes based on IoT, in order to create configurations within a home that make look like the daily behavior of home inhabitants. Due to the high rate of burglary in uninhabited places, reaching an 9% in average in 2019 in the Chilean case, technologies have been involved with greater emphasis on improving security systems, where the implementation of the Internet of Things will allow rapid action against the intruder detection in those places. The proposed IoT system is based on a motion sensor, actuators as relays and lights, Arduino platform to control system, and a Amazon Echo virtual assistant to interface with inhabitants. The main contribution of this prototype security system is the integration of different IoT (Adafruit, IFTTT) and control platforms (Arduino uno and NodeMCU), virtual assistant (Alexa) and actuators, which has features that can be replicated in larger processes and with a larger number of devices. The results demonstrate that security system create an environment occupied by owners without to be inside home, through sensors and actuators.

The use of the Internet of Things (IoT) devices has surged in recent years. However, due to the lack of substantial security, IoT devices are vulnerable to cyber-attacks like Denial-of-Service (DoS) attacks. Most of the current security solutions are either computationally expensive or unscalable as they require known attack signatures or full packet inspection. In this paper, we introduce a novel Graph-based Outlier Detection in Internet of Things (GODIT) approach that (i) represents smart home IoT traffic as a real-time graph stream, (ii) efficiently processes graph data, and (iii) detects DoS attack in real-time. The experimental results on real-world data collected from IoT-equipped smart home show that GODIT is more effective than the traditional machine learning approaches, and is able to outperform current graph-stream anomaly detection approaches.

Smart home assistants are becoming a norm due to their ease-of-use. They employ spoken language as an interface, facilitating easy interaction with their users. Even with their obvious advantages, natural-language based interfaces are not prevalent outside the domain of home assistants. It is hard to adopt them for computer-controlled systems due to the numerous complexities involved with their implementation in varying fields. The main challenge is the grounding of natural language base terms into the underlying system's primitives. The existing systems that do use natural language interfaces are specific to one problem domain only.In this paper, a domain-agnostic framework that creates natural language interfaces for computer-controlled systems has been developed by creating a customizable mapping between the language constructs and the system primitives. The framework employs ontologies built using OWL (Web Ontology Language) for knowledge representation and machine learning models for language processing tasks.

With the rise of Internet of Things (IoT) devices, home network management and security are becoming complex. There is an urgent requirement to make smart home network management more efficient. This work proposes an SDN-based architecture to secure smart home networks through K-Nearest Neighbor (KNN) based device classifications and malicious traffic detection. The efficiency is enhanced by offloading the computation-intensive KNN model to a Field Programmable Gate Arrays (FPGA). Furthermore, we propose a custom KNN solution that exhibits the best performance on an FPGA compared with four alternative KNN instances (i.e., 78% faster than a parallel Bubble Sort-based implementation and 99% faster than three other sorting algorithms). Moreover, with 36,225 training samples, the proposed KNN solution classifies a test query with 95% accuracy in approximately 4 ms on an FPGA compared to 57 seconds on a CPU platform. This highlights the promise of FPGA-based platforms for edge computing applications in the smart home.

The Internet of Things (IoT) is enabling smart houses, where multiple users with complex social relationships interact with smart devices. This requires sophisticated access control specification and enforcement models, that are currently lacking. In this paper, we introduce the extended generalized role based access control (EGRBAC) model for smart home IoT. We provide a formal definition for EGRBAC and illustrate its features with a use case. A proof-of-concept demonstration utilizing AWS-IoT Greengrass is discussed in the appendix. EGRBAC is a first step in developing a comprehensive family of access control models for smart home IoT.