Biblio

Chaos is an interesting phenomenon for nonlinear systems that emerges due to its complex and unpredictable behavior. With the escalated use of low-powered edge-compute devices, data security at the edge develops the need for security in communication. The characteristic that Chaos synchronizes over time for two different chaotic systems with their own unique initial conditions, is the base for chaos implementation in communication. This paper proposes an encryption architecture suitable for communication of on-chip sensors to provide a POC (proof of concept) with security encrypted on the same chip using different chaotic equations. In communication, encryption is achieved with the help of microcontrollers or software implementations that use more power and have complex hardware implementation. The small IoT devices are expected to be operated on low power and constrained with size. At the same time, these devices are highly vulnerable to security threats, which elevates the need to have low power/size hardware-based security. Since the discovery of chaotic equations, they have been used in various encryption applications. The goal of this research is to take the chaotic implementation to the CMOS level with the sensors on the same chip. The hardware co-simulation is demonstrated on an FPGA board for Chua encryption/decryption architecture. The hardware utilization for Lorenz, SprottD, and Chua on FPGA is achieved with Xilinx System Generation (XSG) toolbox which reveals that Lorenz’s utilization is 9% lesser than Chua’s.

Worldwide, societies are rapidly becoming more connected, owing primarily to the growing number of intelligent things and smart applications (e.g, smart automobiles, smart wearable devices, etc.) These have occurred in tandem with the Internet Of Things, a new method of connecting the physical and virtual worlds. It is a new promising paradigm whereby every ‘thing’ can connect to anything via the Internet. However, with IoT systems being deployed even on large-scale, security concerns arise amongst other challenges. Hence the need to allocate appropriate protection of resources. The realization of secure IoT systems could only be accomplished with a comprehensive understanding of the particular needs of a specific system. How-ever, this paradigm lacks a proper and exhaustive classification of security requirements. This paper presents an approach towards understanding and classifying the security requirements of IoT devices. This effort is expected to play a role in designing cost-efficient and purposefully secured future IoT systems. During the coming up with and the classification of the requirements, We present a variety of set-ups and define possible attacks and threats within the scope of IoT. Considering the nature of IoT and security weaknesses as manifestations of unrealized security requirements, We put together possible attacks and threats in categories, assessed the existent IoT security requirements as seen in literature, added more in accordance with the applied domain of the IoT and then classified the security requirements. An IoT system can be secure, scalable, and flexible by following the proposed security requirement classification.

Fitbit Versa is the most popular of its predecessors and successors in the Fitbit faction. Increasingly data stored on these smart fitness devices, their linked applications and cloud datacenters are being used for criminal convictions. There is limited research for investigators on wearable devices and specifically exploring evidence identification and methods of extraction. In this paper we present our analysis of Fitbit Versa using Cellebrite UFED and MSAB XRY. We present a clear scope for investigation and data significance based on the findings from our experiments. The data recovery will include logical and physical extractions using devices running Android 9 and iOS 12, comparing between Cellebrite and XRY capabilities. This paper discusses databases and datatypes that can be recovered using different extraction and analysis techniques, providing a robust outlook of data availability. We also discuss the accuracy of recorded data compared to planned test instances, verifying the accuracy of individual data types. The verifiable accuracy of some datatypes could prove useful if such data was required during the evidentiary processes of a forensic investigation.

Wearable devices are emerging as effective modalities for the collection of individuals’ data. While this data can be leveraged for use in several areas ranging from health-care to crime investigation, storing and securely accessing such information while preserving privacy and detecting any tampering attempts are significant challenges. This paper describes a decentralized system that ensures an individual’s privacy, maintains an immutable log of any data access, and provides decentralized access control management. Our proposed framework uses a custom permissioned blockchain protocol to securely log data transactions from wearable devices in the blockchain ledger. We have implemented a proof-of-concept for our framework, and our preliminary evaluation is summarized to demonstrate our proposed framework’s capabilities. We have also discussed various application scenarios of our privacy-preserving model using blockchain and proof-of-authority. Our research aims to detect data tampering attempts in data sharing scenarios using a thorough transaction log model.

We analyze partner health apps compatible with the Fitbit fitness tracker, and record what third parties they are talking to. We focus on the ten partner Android applications that have more than 50,000 downloads and are fitness-related. Our results show that most of the them contact “unexpected” third parties. Such third parties include social networks; analytics and advertisement services; weather APIs. We also investigate what information is shared by the partner apps with these unexpected entities. Our findings suggest that in many cases personal information of users might be shared, including the phone model; location and SIM carrier; email and connection history.

Purpose: Smart apps and wearables devices are an increasingly used way in healthcare to monitor a range of functions associated with certain health conditions. Even if in the present there are some devices and applications developed, there is no sufficient evidence of the use of such wearables devices in the detection of some disorders such as depression. Thus, through this paper, we want to address this need and present a quantitative research to determine the user requirements for developing a smart device that can detect depression. Material and Methods: To determine the user requirements for developing a system to detect depression we developed a questionnaire which was applied to 205 participants. Results and conclusions: Such a system addressed to detect depression is of interest among the respondents. The most essential parameters to be monitored refer to sleep quality, level of stress, circadian rhythm, and heart rate. Also, the developed system should prioritize reliability, privacy, security, and ease of use.

The use of wearable devices (e.g., smartwatches) in two factor authentication (2FA) is fast emerging, as wearables promise better usability compared to smartphones. Still, the current deployments of wearable 2FA have significant usability and security issues. Specifically, one-time PIN-based wearable 2FA (PIN-2FA) requires noticeable user effort to open the app and copy random PINs from the wearable to the login terminal's (desktop/laptop) browser. An alternative approach, based on one-tap approvals via push notifications (Tap-2FA), relies upon user decision making to thwart attacks and is prone to skip-through. Both approaches are also vulnerable to traditional phishing attacks. To address this security-usability tension, we introduce a fundamentally different design of wearable 2FA, called SG-2FA, involving wrist-movement “seamless gestures” captured near transparently by the second factor wearable device while the user types a very short special sequence on the browser during the login process. The typing of the special sequence creates a wrist gesture that when identified correctly uniquely associates the login attempt with the device's owner. The special sequence can be fixed (e.g., “\$@\$@\$\$”), does not need to be a secret, and does not need to be memorized (could be simply displayed on the browser). This design improves usability over PIN-2FA since only this short sequence has to be typed as part of the login process (no interaction with or diversion of attention to the wearable and copying of random PINs is needed). It also greatly improves security compared to Tap-2FA since the attacker can not succeed in login unless the user's wrist is undergoing the exact same gesture at the exact same time. Moreover, the approach is phishing-resistant and privacy-preserving (unlike behavioral biometrics). Our results show that SG-2FA incurs only minimal errors in both benign and adversarial settings based on appropriate parameterizations.

An important demand of a wearable health observance system is to soundly exchange the Employees' health data and preventing improper use of black devices. In this project we tend to measure planning wearable sensors device sight abnormal and/or unforeseen things by observance physiological parameters alongside different symptoms. Therefore, necessary facilitate is provided in times of urgent would like. To minimize the health hazards and improving the well-being of employees is to be a major critical role in an organization. As per the report by the Indian Labour Organization, the organization spends an average of 3.94% for GDP on employee treatment. The same study revealed that almost 2.78% million deaths occurs every year and 3.74% million occur non-fatal injuries every year at work. So, the organizations are making towards mitigating the facilities to decimating various IoT technologies and the IoT technology are embedded with modern smart systems, it is easy to monitor every employee in an organization, and also it collects and gather the data and send any critical information by the employees.

Recent years have seen an increase in medical big data, which can be attributed to a paradigm shift experienced in medical data sharing induced by the growth of medical technology and the Internet of Things. The evidence of this potential has been proved during the recent covid-19 pandemic, which was characterised by the use of medical wearable devices to help with the medical data exchange between the healthcare providers and patients in a bid to contain the pandemic. However, the use of these technologies has also raised questions and concerns about security and privacy risks. To assist in resolving this issue, this paper proposes a blockchain-based access control framework for managing access to users’ medical data. This is facilitated by using a smart contract on the blockchain, which allows for delegated access control and secure user authentication. This solution leverages blockchain technology’s inherent autonomy and immutability to solve the existing access control challenges. We have presented the solution in the form of a medical wearable sensor prototype and a mobile app that uses the Ethereum blockchain in a real data sharing control scenario. Based on the empirical results, the proposed solution has proven effective. It has the potential to facilitate reliable data exchange while also protecting sensitive health information against potential threats. When subjected to security analysis and evaluation, the system exhibits performance improvements in data privacy levels, high security and lightweight access control design compared to the current centralised access control models.

In this paper, we propose a functional model for the implementation of devices that use the Internet of Things (IoT). In recent years, the number of devices connected to the internet per person has increased from 0.08 in 2003 to a total of 6.58 in 2020, suggesting an increase of 8,225% in 7 years. The proposal includes a functional IoT model of a cybersecurity architecture by including components to ensure compliance with the proposed controls within a cybersecurity framework to detect cyber threats in IoT-based wearable devices. The proposal focuses on reducing the number of vulnerabilities present in IoT devices since, on average, 57% of these devices are vulnerable to attacks. The model has a 3-layer structure: business, applications, and technology, where components such as policies, services and nodes are described accordingly. The validation was done through a simulated environment of a system for the control and monitoring of pregnant women using wearable devices. The results show reductions of the probability index and the impact of risks by 14.95% and 6.81% respectively.

Recently, the frequent security incidents of the Internet of things make the wearable IOT health monitoring systems (WIHMS) face serious security threats. Aiming at the security requirements of WIHMS identity authentication, Q. Jiang proposed a lightweight device mutual identity authentication solution in 2019. The scheme has good security performance. However, we find that in Jiang’s scheme, in the authentication phase, the server CS needs at least 3 queries and 1 update of the database operation, which affects the overall performance of the system. For this reason, we propose a new device mutual authentication and key agreement protocol. In our protocol, the authentication server only needs to query the server database twice.

Nowadays, ubiquitous healthcare monitoring applications are becoming a necessity. In a pervasive smart healthcare system, the user's location information is always transmitted periodically to healthcare providers to increase the quality of the service provided to the user. However, revealing the user's location will affect the user's privacy. This paper presents a novel cloud-based secure location privacy-preserving mobile healthcare framework with decision-making capabilities. A user's vital signs are sensed possibly through a wearable healthcare device and transmitted to a cloud server for securely storing user's data, processing, and decision making. The proposed framework integrates a number of features such as machine learning (ML) for classifying a user's health state, and crowdsensing for collecting information about a person's privacy preferences for possible locations and applying such information to a user who did not set his privacy preferences. In addition to location privacy preservation methods (LPPM) such as obfuscation, perturbation and encryption to protect the location of the user and provide a secure monitoring framework. The proposed framework detects clear emergency cases and quickly decides about sending a help message to a healthcare provider before sending data to the cloud server. To validate the efficiency of the proposed framework, a prototype is developed and tested. The obtained results from the proposed prototype prove its feasibility and utility. Compared to the state of art, the proposed framework offers an adaptive context-based decision for location sharing privacy and controlling the trade-off between location privacy and service utility.

Tunnel approach to the security and privacy aspects of communication networks has been an issue since the inception of networking technologies. Neither the technology nor the regulatory and legal frame works proactively play a significant role towards addressing the ever escalating security challenges. As we have move to ubiquitous computing paradigm where information secrecy and privacy is coupled with new challenges of human to machine and machine to machine interfaces, a transformational model for security should be visited. This research is attempted to highlight the peripheral view of IoT based miniature device security paradigm with focus on standardization, regulations, user adaptation, software and applications, low computing resources and power consumption, human to machine interface and privacy.

In today's world privacy is paramount in everyone's life. Alongside the growth of IoT (Internet of things), wearable devices are becoming widely popular for real-time user monitoring and wise service support. However, in contrast with the traditional short-range communications, these resource-scanty devices face various vulnerabilities and security threats during the course of interactions. Hence, designing a security solution for these devices while dealing with the limited communication and computation capabilities is a challenging task. In this work, PUF (Physical Unclonable Function) and lightweight cryptographic parameters are used together for performing two-way authentication between wearable devices and smartphone, while the simultaneous verification is performed by providing yoking-proofs to the Cloud Server. At the end, it is shown that the proposed scheme satisfies many security aspects and is flexible as well as lightweight.

The developments made in IoT applications have made wearable devices a popular choice for collecting user data to monitor this information and provide intelligent service support. Since wearable devices are continuously collecting and transporting a user's sensitive data over the network, there exist increased security challenges. Moreover, wearable devices lack the computation capabilities in comparison to traditional short-range communication devices. In this paper, authors propounded a Yoking Proof based remote Authentication scheme for Cloud-aided Wearable devices (YPACW) which takes PUF and cryptographic functions and joins them to achieve mutual authentication between the wearable devices and smartphone via a cloud server, by performing the simultaneous verification of these devices, using the established yoking-proofs. Relative to Liu et al.'s scheme, YPACW provides better results with the reduction of communication and processing cost significantly.

Security has become a major concern for women, children and even elders in every walk of their life. Women are getting assaulted and molested, children are getting kidnapped, elder citizens are also facing many problems like robbery, etc. In this paper, a smart security solution called smart wearable device system is implemented using the Raspberry Pi3 for enhancing the safety and security of women/children. It works as an alert as well as a security system. It provides a buzzer alert alert to the people who are nearby to the user (wearing the smart device). The system uses Global Positioning System (GPS) to locate the user, sends the location of the user through SMS to the emergency contact and police using the Global System for Mobile Communications (GSM) / General Radio Packet Service (GPRS) technology. The device also captures the image of the assault and surroundings of the user or victim using USB Web Camera interfaced to the device and sends it as an E-mail alert to the emergency contact soon after the user presses the panic button present on Smart wearable device system.

Biometric authentication is the preferred authentication scheme in modern computing systems. While it offers enhanced usability, it also requires cautious handling of sensitive users' biometric templates. In this paper, a distributed scheme that eliminates the requirement for a central node that holds users' biometric templates is presented. This is replaced by an Ethereum/IPFS combination to which the templates of the users are stored in a homomorphically encrypted form. The scheme enables the biometric authentication of the users by any third party service, while the actual biometric templates of the user never leave his device in non encrypted form. Secure authentication of users in enabled, while sensitive biometric data are not exposed to anyone. Experiments show that the scheme can be applied as an authentication mechanism with minimal time overhead.

Wearable smart sensing devices presently become more and more popular in people's daily life, which also brings serious problems related to personal data privacy. In order to provide users better experiences, wearable smart sensing devices are collecting users' personal data all the time and uploading the data to service provider to get computing services, which objectively let service provider master each user's condition and cause a lot of problems such as spam, harassing call, etc. This paper designs a blockchain based scheme to solve such problems by cutting off the association between user identifier and its sensing data from perspective of shielding service providers and adversaries. Firstly, privacy requirements and situations in smart sensing area are reviewed. Then, three key technologies are introduced in the scheme including its theories, purposes and usage. Next, the designed protocol is shown and analyzed in detail. Finally, security analysis and engineering feasibility of the scheme are given. This scheme will give user better experience from privacy protection perspective in smart sensing area.

Wearable devices can be potentially captured or accessed in an unauthorized manner because of their physical nature. In such cases, they are in white-box attack contexts, where the adversary may have total visibility on the implementation of the built-in cryptosystem, with full control over its execution platform. Dealing with white-box attacks on wearable devices is undoubtedly a challenge. To serve as a countermeasure against threats in such contexts, we propose a lightweight encryption scheme to protect the confidentiality of data against white-box attacks. We constructed the scheme's encryption and decryption algorithms on a substitution-permutation network that consisted of random secret components. Moreover, the encryption algorithm uses random padding that does not need to be correctly decrypted as part of the input. This feature enables non-bijective linear transformations to be used in each encryption round to achieve strong security. The required storage for static data is relatively small and the algorithms perform well on various devices, which indicates that the proposed scheme satisfies the requirements of wearable computing in terms of limited memory and low computational power.

Smartwatches are expected to become the world's best-selling electronic product after smartphones. Various smart-watches have been released to the private consumer market, but the data on smartwatches is not well protected. In this paper, we show for the first time that photoplethysmography (PPG)signals influenced by hand gestures can be used to authenticate users on smartwatches. The insight is that muscle and tendon movements caused by hand gestures compress the arterial geometry with different degrees, which has a significant impact on the blood flow. Based on this insight, novel approaches are proposed to detect the starting point and ending point of the hand gesture from raw PPG signals and determine if these PPG signals are from a normal user or an attacker. Different from existing solutions, our approach leverages the PPG sensors that are available on most smartwatches and does not need to collect training data from attackers. Also, our system can be used in more general scenarios wherever users can perform hand gestures and is robust against shoulder surfing attacks. We conduct various experiments to evaluate the performance of our system and show that our system achieves an average authentication accuracy of 96.31 % and an average true rejection rate of at least 91.64% against two types of attacks.

Mobile payment has drawn considerable attention due to its convenience of paying via personal mobile devices at anytime and anywhere, and passcodes (i.e., PINs or patterns) are the first choice of most consumers to authorize the payment. This paper demonstrates a serious security breach and aims to raise the awareness of the public that the passcodes for authorizing transactions in mobile payments can be leaked by exploiting the embedded sensors in wearable devices (e.g., smartwatches). We present a passcode inference system, WristSpy, which examines to what extent the user's PIN/pattern during the mobile payment could be revealed from a single wrist-worn wearable device under different passcode input scenarios involving either two hands or a single hand. In particular, WristSpy has the capability to accurately reconstruct fine-grained hand movement trajectories and infer PINs/patterns when mobile and wearable devices are on two hands through building a Euclidean distance-based model and developing a training-free parallel PIN/pattern inference algorithm. When both devices are on the same single hand, a highly challenging case, WristSpy extracts multi-dimensional features by capturing the dynamics of minute hand vibrations and performs machine-learning based classification to identify PIN entries. Extensive experiments with 15 volunteers and 1600 passcode inputs demonstrate that an adversary is able to recover a user's PIN/pattern with up to 92% success rate within 5 tries under various input scenarios.

Wearable technology has been on an exponential rise and shows no signs of slowing down. One category of wearable technology is Fitness bands, which have the potential to show a user's activity levels and location data. Such information stored in fitness bands is just the beginning of a long trail of evidence fitness bands can store, which represents a huge opportunity to digital forensic practitioners. On the surface of recent work and research in this area, there does not appear to be any similar work that has already taken place on fitness bands and particularly, the devices in this study, a Garmin Forerunner 110, a Fitbit Charge HR and a Generic low-cost HETP fitness tracker. In this paper, we present our analysis of these devices for any possible digital evidence in a forensically sound manner, identifying files of interest and location data on the device. Data accuracy and validity of the evidence is shown, as a test run scenario wearing all of the devices allowed for data comparison analysis.

Wearables are now ubiquitous items equipped with a multitude of sensors such as GPS, accelerometer, or Bluetooth. The raw data from this sensors are typically used in a health context. However, we can also use it for security purposes. In this paper, we present a solution that aims at using data from the sensors of a wearable device to identify the password a user is typing on a keyboard by using machine learning algorithms. Hence, the purpose is to determine whether a malicious third party application could extract sensitive data through the raw data that it has access to.

We propose WristUnlock, a novel technique that uses a wrist wearable to unlock a smartphone in a secure and usable fashion. WristUnlock explores both the physical proximity and secure Bluetooth connection between the smartphone and wrist wearable. There are two modes in WristUnlock with different security and usability features. In the WristRaise mode, the user raises his smartphone in his natural way with the same arm carrying the wrist wearable; the smartphone gets unlocked if the acceleration data on the smartphone and wrist wearable satisfy an anticipated relationship specific to the user himself. In the WristTouch mode, the wrist wearable sends a random number to the smartphone through both the Bluetooth channel and a touch-based physical channel; the smartphone gets unlocked if the numbers received from both channels are equal. We thoroughly analyze the security of WristUnlock and confirm its high efficacy through detailed experiments.

With the emergence of IoT, wearable devices are drawing attention and becoming part of our daily life. These wearable devices collect private information about their wearers. Mostly, a secure authentication process is used to verify a legitimate user that relies on the mobile terminal. Similarly, remote cloud services are used for verification and authentication of both wearable devices and wearers. Security is necessary to preserve the privacy of users. Some traditional authentication protocols are proposed which have vulnerabilities and are prone to different attacks like forgery, de-synchronization, and un-traceability issues. To address these vulnerabilities, recently, Wu et al. (2017) proposed a cloud-assisted authentication scheme which is costly in terms of computations required. Therefore this paper proposed an improved, lightweight and computationally efficient authentication scheme for wearable devices. The proposed scheme provides similar level of security as compared to Wu's (2017) scheme but requires 41.2% lesser computations.