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"Ubiquitous computing Security measures."
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Privacy in mobile and pervasive computing
It is easy to imagine that a future populated with an ever-increasing number of mobile and pervasive devices that record our minute goings and doings will significantly expand the amount of information that will be collected, stored, processed, and shared about us by both corporations and governments. The vast majority of this data is likely to benefit us greatly--making our lives more convenient, efficient, and safer through custom-tailored and context-aware services that anticipate what we need, where we need it, and when we need it. But beneath all this convenience, efficiency, and safety lurks the risk of losing control and awareness of what is known about us in the many different contexts of our lives. Eventually, we may find ourselves in a situation where something we said or did will be misinterpreted and held against us, even if the activities were perfectly innocuous at the time. Even more concerning, privacy implications rarely manifest as an explicit, tangible harm. Instead, most privacy harms manifest as an absence of opportunity, which may go unnoticed even though it may substantially impact our lives. In this Synthesis Lecture, we dissect and discuss the privacy implications of mobile and pervasive computing technology. For this purpose, we not only look at how mobile and pervasive computing technology affects our expectations of--and ability to enjoy--privacy, but also look at what constitutes \"privacy\" in the first place, and why we should care about maintaining it. We describe key characteristics of mobile and pervasive computing technology and how those characteristics lead to privacy implications. We discuss seven approaches that can help support end-user privacy in the design of mobile and pervasive computing technologies, and set forward six challenges that will need to be addressed by future research. The prime target audience of this lecture is researchers and practitioners working in mobile and pervasive computing who want to better understand and account for the nuanced privacy implications of the technologies they are creating. Those new to either mobile and pervasive computing or privacy may also benefit from reading this book to gain an overview and deeper understanding of this highly interdisciplinary and dynamic field.
Book
Identity Management for Internet of Things
The Internet of Things is a wide-reaching network of devices, and these devices can intercommunicate and collaborate with each other to produce variety of services at any time, any place, and in any way. Maintaining access control, authentication and managing the identity of devices while they interact with other devices, services and people is an important challenge for identity management. The identity management presents significant challenges in the current Internet communication. These challenges are exacerbated in the internet of things by the unbound number of devices and expected limitations in constrained resources. Current identity management solutions are mainly concerned with identities that are used by end users, and services to identify themselves in the networked world. However, these identity management solutions are designed by considering that significant resources are available and applicability of these identity management solutions to the resource constrained internet of things needs a thorough analysis. Technical topics discussed in the book include: • Internet of Things; • Identity Management; • Identity models in Internet of Things; • Identity management and trust in the Internet of Things context; • Authentication and access control; Identitymanagement for Internet of Things contributes to the area of identity management for ubiquitous devices in the Internet of Things. It initially presents the motivational factors together with the identity management problems in the context of Internet of Things and proposes an identity management framework. Following this, it refers to the major challenges for Identitymanagement and presents different identity management models. This book also presents relationship between identity and trust, different approaches for trust management, authentication and access control.Key milestones identified for Identitymanagement are clustering with hierarchical addressing, trust management, mutual authentication and access control. Identitymanagement for Internet of Things is ideal forpersonnel in computer/communication industries as well as academic staff and master/research students in wireless communication, computer science, operational research, electrical engineering andtelecommunication systems Internet, and cloud computing.
eBook
Cross-Layer Federated Learning for Lightweight IoT Intrusion Detection Systems
With the proliferation of IoT devices, ensuring the security and privacy of these devices and their associated data has become a critical challenge. In this paper, we propose a federated sampling and lightweight intrusion-detection system for IoT networks that use K-meansfor sampling network traffic and identifying anomalies in a semi-supervised way. The system is designed to preserve data privacy by performing local clustering on each device and sharing only summary statistics with a central aggregator. The proposed system is particularly suitable for resource-constrained IoT devices such as sensors with limited computational and storage capabilities. We evaluate the system’s performance using the publicly available NSL-KDD dataset. Our experiments and simulations demonstrate the effectiveness and efficiency of the proposed intrusion-detection system, highlighting the trade-offs between precision and recall when sharing statistics between workers and the coordinator. Notably, our experiments show that the proposed federated IDS can increase the true-positive rate up to 10% when the workers and the coordinator collaborate.
Journal Article
The Internet in Everything
A compelling argument that the Internet of things threatens human rights and security and that suggests policy prescriptions to protect our future The Internet has leapt from human-facing display screens into the material objects all around us. In this so-called Internet of Things-connecting everything from cars to cardiac monitors to home appliances-there is no longer a meaningful distinction between physical and virtual worlds. Everything is connected. The social and economic benefits are tremendous, but there is a downside: an outage in cyberspace can result not only in a loss of communication but also potentially a loss of life. Control of this infrastructure has become a proxy for political power, since countries can easily reach across borders to disrupt real-world systems. Laura DeNardis argues that this diffusion of the Internet into the physical world radically escalates governance concerns around privacy, discrimination, human safety, democracy, and national security, and she offers new cyber-policy solutions. In her discussion, she makes visible the sinews of power already embedded in our technology and explores how hidden technical governance arrangements will become the constitution of our future.
eBook
Blockchain and 6G-Enabled IoT
Ubiquitous computing turns into a reality with the emergence of the Internet of Things (IoT) adopted to connect massive numbers of smart and autonomous devices for various applications. 6G-enabled IoT technology provides a platform for information collection and processing at high speed and with low latency. However, there are still issues that need to be addressed in an extended connectivity environment, particularly the security and privacy domain challenges. In addition, the traditional centralized architecture is often unable to address problems associated with access control management, interoperability of different devices, the possible existence of a single point of failure, and extensive computational overhead. Considering the evolution of decentralized access control mechanisms, it is necessary to provide robust security and privacy in various IoT-enabled industrial applications. The emergence of blockchain technology has changed the way information is shared. Blockchain can establish trust in a secure and distributed platform while eliminating the need for third-party authorities. We believe the coalition of 6G-enabled IoT and blockchain can potentially address many problems. This paper is dedicated to discussing the advantages, challenges, and future research directions of integrating 6G-enabled IoT and blockchain technology for various applications such as smart homes, smart cities, healthcare, supply chain, vehicle automation, etc.
Journal Article
Cryptography‐based deep artificial structure for secure communication using IoT‐enabled cyber‐physical system
Internet of things (IoTs) enabled cyber‐physical systems is a system that provides communication between physical devices and cyber environment. They run independently without any user interaction. Because the IoT devices are vulnerable to a variety of attacks, security is a noteworthy factor in the development process during communication. To improve secure communication with minimum time consumption, a novel technique called jackknife regressive Schmidt Samoa cryptography‐based deep artificial structure learning (JRSSC‐DASL) is introduced. Initially, the data is monitored by IoT devices and is collected from the dataset. The proposed deep artificial structure learning technique trains the gathered data with multiple layers. Then, the collected data is analysed in the first hidden layer with the help of the jackknife regression function by learning the feature and it classifies the data with higher accuracy. The classified data is sent to the next hidden layer where encryption is performed using Schmidt Samoa (SS) encryption algorithm. Then, the encrypted data is sent to the cloud server where the decryption is performed using the SS decryption algorithm. The cloud server obtains the original data and it is stored in their database for further processing. This process enhances the security of data communication and achieves high data confidentiality with less processing time. Experimental estimation is performed on the factors such as classification accuracy, confidentiality rate, processing time and memory usage to the number of data sensed from IoT device. Conferred results reveal that the proposed JRSSC‐DASL technique has high confidentiality rate and minimum processing time as well as memory usage when compared to state‐of‐the‐art methods.
Journal Article
Configuring a Trusted Cloud Service Model for Smart City Exploration Using Hybrid Intelligence
Emerging research concerns about the authenticated cloud service with high performance of security and assuring trust for distributed clients in a smart city. Cloud services are deployed by the third-party or web-based service providers. Thus, security and trust would be considered for every layer of cloud architecture. The principle objective of cloud service providers is to deliver better services with assurance of trust about clients' information. Cloud's users recurrently face different security challenges about the use of sharable resources. It is really difficult for Cloud Service Provider for adapting varieties of security policies to sustain their enterprises' goodwill. To make an optimistic decision that would be better suitable to provide a trusted cloud service for users' in smart city. Statistical method known as Multivariate Normal Distribution is used to select different attributes of different security entities for developing the proposed model. Finally, fuzzy multi objective decision making and Bio-Inspired Bat algorithm are applied to achieve the objective.
Journal Article
Intelligent reflecting surface backscatter-enabled physical layer security enhancement via deep reinforcement learning
This article introduces a novel strategy for wireless communication security utilizing intelligent reflecting surfaces (IRS). The IRS is strategically deployed to mitigate jamming attacks and eavesdropper threats while improving signal reception for legitimate users (LUs) by redirecting jamming signals toward desired communication signals leveraging physical layer security (PLS). By integrating the IRS into the backscatter communication system, we enhance the overall secrecy rate of LU, by dynamically adjusting IRS reflection coefficients and active beamforming at the base station (BS). A design problem is formulated to jointly optimize IRS reflecting beamforming and BS active beamforming, considering time-varying channel conditions and desired secrecy rate requirements. We propose a novel approach based on deep reinforcement learning (DRL) named Deep-PLS. This approach aims to determine an optimal beamforming policy capable of thwarting eavesdroppers in evolving environmental conditions. Extensive simulation studies validate the efficacy of our proposed strategy, demonstrating superior performance compared to traditional IRS approaches, IRS backscattering-based anti-eavesdropping methods, and other benchmark strategies in terms of secrecy performance.
Journal Article
Efficient secret key generation scheme of physical layer security communication in ubiquitous wireless networks
This paper focuses on high efficiency secret key generation mechanism of physical‐layer communication over fading channels in ubiquitous wireless networks. The secret key rate via traditional physical‐layer approach could be limited when the wireless propagation channels connecting two sensors change slowly. To generate a high‐rate secret key and improve the communication efficiency over quasi‐static block fading channels, a novel multi‐randomness device‐to‐device secret key generation strategy and a cooperative communication mechanism aided by relay nodes are proposed. In the proposed schemes, the legitimate members to send random signals rotationally in every coherent time T are set; thus, two legitimate ubiquitous wireless network members, Alice and Bob, can obtain the potential correlated information by exploiting the randomness and the reciprocity of the wireless propagation channels. Considering the reciprocity of wireless channels is variable while the forward channel gain and backward channel gain are correlated in coherent time, a modified secret key generation scheme is proposed via layered coding with theoretical secret key rates derived. The simulation results show that the proposed scheme outperforms traditional approaches with favourable application prospects in ubiquitous wireless communications networks and internet of things.
Journal Article
Identifying Smartphone Users Based on Activities in Daily Living Using Deep Neural Networks
Smartphones have become ubiquitous, allowing people to perform various tasks anytime and anywhere. As technology continues to advance, smartphones can now sense and connect to networks, providing context-awareness for different applications. Many individuals store sensitive data on their devices like financial credentials and personal information due to the convenience and accessibility. However, losing control of this data poses risks if the phone gets lost or stolen. While passwords, PINs, and pattern locks are common security methods, they can still be compromised through exploits like smudging residue from touching the screen. This research explored leveraging smartphone sensors to authenticate users based on behavioral patterns when operating the device. The proposed technique uses a deep learning model called DeepResNeXt, a type of deep residual network, to accurately identify smartphone owners through sensor data efficiently. Publicly available smartphone datasets were used to train the suggested model and other state-of-the-art networks to conduct user recognition. Multiple experiments validated the effectiveness of this framework, surpassing previous benchmark models in this area with a top F1-score of 98.96%.
Journal Article