Search for: All records

This paper analyzes Google Home, Apple HomeKit, Samsung SmartThings, and Amazon Alexa platforms, focusing on their integration with the Matter protocol. Matter is a connectivity standard developed by the Connectivity Standards Alliance (CSA) for the smart-home industry. By examining key features and qualitative metrics, this study aims to provide valuable insights for consumers and industry professionals in making informed decisions about smart-home devices. We conducted (from May to August 2024) a comparative analysis to explore how Google Home Nest, Apple HomePod Mini, Samsung SmartThings station, and Amazon Echo Dot platforms leverage the power of Matter to provide seamless and integrated smart-home experiences. 

Roughly 6 million homes are sold each year in the United States alone.1 Before a home is sold, a building inspector often examines the integrity of the building and renders an opinion on its soundness— examining things like structural integrity, electrical safety, mold and mildew, and radon or other toxins. These inspectors have specialized tools, knowledge, and experience to make a more informed judgment than nonprofessionals are capable of making. 

Internet of Things (IoT) devices left behind when a home is sold create security and privacy concerns for both prior and new residents. We envision a specialized “building inspector for IoT” to help securely facilitate transfer of the home. 

As the integration of smart devices into our daily environment accelerates, the vision of a fully integrated smart home is becoming more achievable through standards such as the Matter protocol. In response, this research paper explores the use of Matter in addressing the heterogeneity and interoperability problems of smart homes. We built a testbed and introduce a network utility device, designed to sniff network traffic and provide a wireless access point within IoT networks. This paper also presents the experience of students using the testbed in an academic scenario. 

Project Connected Home over IP, known as Matter, a unifying standard for the smart home, will begin formal device certification in late 2022. The standard will prioritize connectivity using short-range wireless communication protocols such as Wi-Fi, Thread, and Ethernet. The standard will also include emerging technologies such as Blockchain for device certification and security. In this paper, we rely on the Matter protocol to solve the long-standing heterogeneity problem in smart homes. This work presents a hardware Testbed built using development kits, as there is currently very few devices supporting Matter protocol. In addition, it presents a network architecture that automates smart homes to cloud services. The work is a simple and cheap way of developing a Testbed for automating smart homes that uses Matter protocol. The architecture lays the foundation for exploring security and privacy issues, data collection analysis, and data provenance in a smart home ecosystem built on Matter protocol. 

With the increase in data transmissions and network traffic over the years, there has been an increase in concerns about protecting network data and information from snooping. With this concern, encryptions are incorporated into network protocols. From wireless protocols to web and phone applications, systems that handle the going and coming of data on the network have applied different kinds of encryptions to protect the confidentiality and integrity of their data transfers. The addition of encryptions poses a new question. What will be observed from encrypted traffic data? This work in progress research delivers an in-depth overview of the ZigBee protocol and analyzes encrypted ZigBee traffic on the ZigBee network. From our analysis, we developed possible strategies for ZigBee traffic analysis. Adopting the proposed strategy makes it possible to detect encrypted traffic activities and patterns of use on the ZigBee network. To the best of our knowledge, this is the first work that tries to understand encrypted ZigBee traffic. By understanding what can be gained from encrypted traffic, this work will benefit the security and privacy of the ZigBee protocol. 

Despite long-contested viability, numerous applications still rely upon Advance Encryption Standard (AES) in Counter mode (AES-CTR). Research supports that the vulnerabilities associated with CTR from a mathematical perspective, mainly forgery attempts, stem from misusing the nonce. When paired with cryptographic algorithms, assuming no nonce misuse increases the complexity of unraveling CTR. This paper examines the pairing of CTR with AES-128 (AES-CTR). It includes (1) full key recovery for a software implementation of AES-CTR utilizing a template attack (TA) and (2) enhancing the TA analysis's point of interest (POI) using first-order analysis and known key to identify leaky samples. 

Researchers are looking into solutions to support the enormous demand for wireless communication, which has been exponentially increasing along with the growth of technology. The sixth generation (6G) Network emerged as the leading solution for satisfying the requirements placed on the telecommunications system. 6G technology mainly depends on various machine learning and artificial intelligence techniques. The performance of these machine learning algorithms is high. Still, their security has been neglected for some reason, which leaves the door open to various vulnerabilities that attackers can exploit to compromise systems. Therefore, it is essential to evaluate the security of machine learning algorithms to prevent them from being spoofed by malicious hackers. Prior research has shown that the decision tree is one of the most popular algorithms used by 80% of researchers for classification problems. In this work, we collect the dataset from a laboratory testbed of over 100 Internet of things (IoT) devices. The devices include smart cameras, smart light bulbs, Alexa, and others. We evaluate classifiers using the original dataset during the experiment and record a 98% accuracy. We then use the label-flipping attack approach to poison our dataset and record the output. As a result, flipping 10%, 20%, 30%, 40%, and 50% of the poison data generated accuracies of 86%, 74%, 64%, 54%, and 50%, respectively.