13,478 research outputs found

    I Know What You Did Last Summer: Your Smart Home Internet of Things and Your iPhone Forensically Ratting You Out

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    The adoption of smart home Internet of Things (IoT) devices continues to grow. What if your devices can snitch on you and let us know where you are at any given point in time? In this work we examined the forensic artifacts produced by Nest devices, and in specific, we examined the logical backup structure of an iPhone used to control a Nest thermostat, Nest Indoor Camera and a Nest Outdoor Camera. We also integrated the Google Home Mini as another method of controlling the studied Smart Home devices. Our work is the primary account for the examination of Nest artifacts produced by an iPhone, and is also the first open source research to produce a usable forensics tool we name the Forensic Evidence Acquisition and Analysis System (FEAAS). FEAAS consolidates evidentiary data into a readable report that can infer user events (like entering or leaving a home) and what triggered an event (whether it was the Google Assistant through a voice command, or the use of an iPhone application). Our results are important for the advancement of digital forensics, as there are cases starting to emerge in which smart home IoT devices have already been used as culpatory evidence

    Policing the smart home:The internet of things as ‘invisible witnesses'

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    In this paper, we develop the concept of smart home devices as ‘invisible witnesses’ in everyday life. We explore contemporary examples that highlight how smart devices have been used by the police and unpack the socio-technical implications of using these devices in criminal investigations. We draw on several sociological, computing and forensics concepts to develop our argument. We consider the challenges of obtaining and interpreting trace evidence from smart devices; unpack the ways in which these devices are designed to be ‘invisible in use’; and reflect on the processes by which they become domesticated into everyday life. We also analyse the differentiated levels of control occupants have over smart home devices, and the surveillance impacts of making everyday life visible to third parties, particularly the police

    Cyber security investigation for Raspberry Pi devices

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    Big Data on Cloud application is growing rapidly. When the cloud is attacked, the investigation relies on digital forensics evidence. This paper proposed the data collection via Raspberry Pi devices, in a healthcare situation. The significance of this work is that could be expanded into a digital device array that takes big data security issues into account. There are many potential impacts in health area. The field of Digital Forensics Science has been tagged as a reactive science by some who believe research and study in the field often arise as a result of the need to respond to event which brought about the needs for investigation; this work was carried as a proactive research that will add knowledge to the field of Digital Forensic Science. The Raspberry Pi is a cost-effective, pocket sized computer that has gained global recognition since its development in 2008; with the wide spread usage of the device for different computing purposes. Raspberry Pi can potentially be a cyber security device, which can relate with forensics investigation in the near future. This work has used a systematic approach to study the structure and operation of the device and has established security issues that the widespread usage of the device can pose, such as health or smart city. Furthermore, its evidential information applied in security will be useful in the event that the device becomes a subject of digital forensic investigation in the foreseeable future. In healthcare system, PII (personal identifiable information) is a very important issue. When Raspberry Pi plays a processor role, its security is vital; consequently, digital forensics investigation on the Raspberry Pies becomes necessary

    The not so smart, smart grid - potential security risks associated with the deployment of smart grid technologies

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    The electricity grid has been up until now a relatively stable artifice of modern industrialized nations. The power grids are the most widespread wired networks in the world. They are heavily regulated and standardized to protect the integrity, stability and reliability of supply. The grids have been essentially closed systems, this is now rapidly changing with the introduction of the network enabled smart meter. These meters are “web” accessible, connect and interact directly with electrical appliances in domiciles and businesses. This move now brings a range of extreme risks and complexities into these stable networks. This paper explores the security issues and potential problems associated with current moves to provide these smart meters to existing grid connections

    Smart Home Forensics: Identifying Ddos Attack Patterns on Iot Devices

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    Smart homes are becoming more common as more people integrate IoT devices into their home environment. As such, these devices have access to personal data on their homeowners’ networks. One of the advantages of IoT devices is that they are compact. However, this limits the incorporation of security measures in their hardware. Misconfigured IoT devices are commonly the target of malicious attacks. Additionally, distributed denial-of-service attacks are becoming more common due to applications and software that provides users with easy-to-use user interfaces. Since one vulnerable device is all an attacker needs to launch an attack on a network, in regards to IoT devices, there is a need for businesses and homeowners to find out methods of predicting incoming DDoS attacks. The earlier a DDoS attack is discovered, the earlier mitigation and prevention techniques can be applied. One way to predict incoming DDoS attacks is from emerging patterns. To discover these patterns, we constructed a home IoT environment and conducted LOIC and Slow Loris DDoS attacks against this environment. This setup led to the discovery of five distinct patterns that emerged when the IoT devices were being DDoS-ed. In this paper, we will discuss the DDoS attack used, home IoT environment, normal vs attacked traffic patterns, and make recommendations for future research
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