Security in 5G-Enabled Internet of Things Communication: Issues: Challenges, and Future Research Roadmap

5G mobile communication systems promote the mobile network to not only interconnect people, but also interconnect and control the machine and other devices. 5G-enabled Internet of Things (IoT) communication environment supports a wide-variety of applications, such as remote surgery, self-driving car, virtual reality, flying IoT drones, security and surveillance and many more. These applications help and assist the routine works of the community. In such communication environment, all the devices and users communicate through the Internet. Therefore, this communication agonizes from different types of security and privacy issues. It is also vulnerable to different types of possible attacks (for example, replay, impersonation, password reckoning, physical device stealing, session key computation, privileged-insider, malware, man-in-the-middle, malicious routing, and so on). It is then very crucial to protect the infrastructure of 5G-enabled IoT communication environment against these attacks. This necessitates the researchers working in this domain to propose various types of security protocols under different types of categories, like key management, user authentication/device authentication, access control/user access control and intrusion detection. In this survey paper, the details of various system models (i.e., network model and threat model) required for 5G-enabled IoT communication environment are provided. The details of security requirements and attacks possible in this communication environment are further added. The different types of security protocols are also provided. The analysis and comparison of the existing security protocols in 5G-enabled IoT communication environment are conducted. Some of the future research challenges and directions in the security of 5G-enabled IoT environment are displayed. The motivation of this work is to bring the details of different types of security protocols in 5G-enabled IoT under one roof so that the future researchers will be benefited with the conducted work

Proceedings of the 2019 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

In 2019 again, the annual joint workshop of the Fraunhofer IOSB and the Vision and Fusion Laboratory of the Karlsruhe Institute of Technology took place. The doctoral students of both institutions presented extensive reports on the status of their research and discussed topics ranging from computer vision and optical metrology to network security, usage control and machine learning. The results and ideas presented at the workshop are collected in this book in the form of technical reports

Proceedings of the 2019 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

In 2019 fand wieder der jährliche Workshop des Fraunhofer IOSB und des Lehrstuhls für Interaktive Echtzeitsysteme des Karlsruher Insitut für Technologie statt. Die Doktoranden beider Institutionen präsentierten den Fortschritt ihrer Forschung in den Themen Maschinelles Lernen, Machine Vision, Messtechnik, Netzwerksicherheit und Usage Control. Die Ideen dieses Workshops sind in diesem Buch gesammelt in der Form technischer Berichte

Security in 5G-enabled Internet of Things communication: issues, challenges, and future research roadmap

5G mobile communication systems promote the mobile network to not only interconnect people, but also interconnect and control the machine and other devices. 5G-enabled Internet of Things (IoT) communication environment supports a wide-variety of applications, such as remote surgery, self-driving car, virtual reality, flying IoT drones, security and surveillance and many more. These applications help and assist the routine works of the community. In such communication environment, all the devices and users communicate through the Internet. Therefore, this communication agonizes from different types of security and privacy issues. It is also vulnerable to different types of possible attacks (for example, replay, impersonation, password reckoning, physical device stealing, session key computation, privileged-insider, malware, man-in-the-middle, malicious routing, and so on). It is then very crucial to protect the infrastructure of 5G-enabled IoT communication environment against these attacks. This necessitates the researchers working in this domain to propose various types of security protocols under different types of categories, like key management, user authentication/device authentication, access control/user access control and intrusion detection. In this survey paper, the details of various system models (i.e., network model and threat model) required for 5G-enabled IoT communication environment are provided. The details of security requirements and attacks possible in this communication environment are further added. The different types of security protocols are also provided. The analysis and comparison of the existing security protocols in 5G-enabled IoT communication environment are conducted. Some of the future research challenges and directions in the security of 5G-enabled IoT environment are displayed. The motivation of this work is to bring the details of different types of security protocols in 5G-enabled IoT under one roof so that the future researchers will be benefited with the conducted work

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Cybersecurity and the Digital Health: An Investigation on the State of the Art and the Position of the Actors

Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain

Security and Privacy in the Internet of Things

The Internet of Things (IoT) is an emerging paradigm that seamlessly integrates electronic devices with sensing and computing capability into the Internet to achieve intelligent processing and optimized controlling. In a connected world built through IoT, where interconnected devices are extending to every facet of our lives, including our homes, offices, utility infrastructures and even our bodies, we are able to do things in a way that we never before imagined. However, as IoT redefines the possibilities in environment, society and economy, creating tremendous benefits, significant security and privacy concerns arise such as personal information confidentiality, and secure communication and computation. Theoretically, when everything is connected, everything is at risk. The ubiquity of connected things gives adversaries more attack vectors and more possibilities, and thus more catastrophic consequences by cybercrimes. Therefore, it is very critical to move fast to address these rising security and privacy concerns in IoT systems before severe disasters happen. In this dissertation, we mainly address the challenges in two domains: (1) how to protect IoT devices against cyberattacks; (2) how to protect sensitive data during storage, dissemination and utilization for IoT applications. In the first part, we present how to leverage anonymous communication techniques, particularly Tor, to protect the security of IoT devices. We first propose two schemes to enhance the security of smart home by integrating Tor hidden services into IoT gateway for users with performance preference. Then, we propose a multipath-routing based architecture for Tor hidden services to enhance its resistance against traffic analysis attacks, and thus improving the protection for smart home users who desire very strong security but care less about performance. In the second part of this dissertation, we explore the solutions to protect the data for IoT applications. First, we present a reliable, searchable and privacy-preserving e-healthcare system, which takes advantage of emerging cloud storage and IoT infrastructure and enables healthcare service providers (HSPs) to realize remote patient monitoring in a secure and regulatory compliant manner. Then, we turn our attention to the data analysis in IoT applications, which is one of the core components of IoT applications. We propose a cloud-assisted, privacy-preserving machine learning classification scheme over encrypted data for IoT devices. Our scheme is based on a three-party model coupled with a two-stage decryption Paillier-based cryptosystem, which allows a cloud server to interact with machine learning service providers (MLSPs) and conduct computation intensive classification on behalf of the resourced-constrained IoT devices in a privacy-preserving manner. Finally, we explore the problem of privacy-preserving targeted broadcast in IoT, and propose two multi-cloud-based outsourced-ABE (attribute-based encryption) schemes. They enable the receivers to partially outsource the computationally expensive decryption operations to the clouds, while preventing attributes from being disclosed

Privacy-preserving data analytics in cloud computing

The evolution of digital content and rapid expansion of data sources has raised the need for streamlined monitoring, collection, storage and analysis of massive, heterogeneous data to extract useful knowledge and support decision-making mechanisms. In this context, cloud computing o↵ers extensive, cost-e↵ective and on demand computing resources that improve the quality of services for users and also help service providers (enterprises, governments and individuals). Service providers can avoid the expense of acquiring and maintaining IT resources while migrating data and remotely managing processes including aggregation, monitoring and analysis in cloud servers. However, privacy and security concerns of cloud computing services, especially in storing sensitive data (e.g. personal, healthcare and financial) are major challenges to the adoption of these services. To overcome such barriers, several privacy-preserving techniques have been developed to protect outsourced data in the cloud. Cryptography is a well-known mechanism that can ensure data confidentiality in the cloud. Traditional cryptography techniques have the ability to protect the data through encryption in cloud servers and data owners can retrieve and decrypt data for their processing purposes. However, in this case, cloud users can use the cloud resources for data storage but they cannot take full advantage of cloud-based processing services. This raises the need to develop advanced cryptosystems that can protect data privacy, both while in storage and in processing in the cloud. Homomorphic Encryption (HE) has gained attention recently because it can preserve the privacy of data while it is stored and processed in the cloud servers and data owners can retrieve and decrypt their processed data to their own secure side. Therefore, HE o↵ers an end-to-end security mechanism that is a preferable feature in cloud-based applications. In this thesis, we developed innovative privacy-preserving cloud-based models based on HE cryptosystems. This allowed us to build secure and advanced analytic models in various fields. We began by designing and implementing a secure analytic cloud-based model based on a lightweight HE cryptosystem. We used a private resident cloud entity, called ”privacy manager”, as an intermediate communication server between data owners and public cloud servers. The privacy manager handles analytical tasks that cannot be accomplished by the lightweight HE cryptosystem. This model is convenient for several application domains that require real-time responses. Data owners delegate their processing tasks to the privacy manager, which then helps to automate analysis tasks without the need to interact with data owners. We then developed a comprehensive, secure analytical model based on a Fully Homomorphic Encryption (FHE), that has more computational capability than the lightweight HE. Although FHE can automate analysis tasks and avoid the use of the privacy manager entity, it also leads to massive computational overhead. To overcome this issue, we took the advantage of the massive cloud resources by designing a MapReduce model that massively parallelises HE analytical tasks. Our parallelisation approach significantly speeds up the performance of analysis computations based on FHE. We then considered distributed analytic models where the data is generated from distributed heterogeneous sources such as healthcare and industrial sensors that are attached to people or installed in a distributed-based manner. We developed a secure distributed analytic model by re-designing several analytic algorithms (centroid-based and distribution-based clustering) to adapt them into a secure distributed-based models based on FHE. Our distributed analytic model was developed not only for distributed-based applications, but also it eliminates FHE overhead obstacle by achieving high efficiency in FHE computations. Furthermore, the distributed approach is scalable across three factors: analysis accuracy, execution time and the amount of resources used. This scalability feature enables users to consider the requirements of their analysis tasks based on these factors (e.g. users may have limited resources or time constrains to accomplish their analysis tasks). Finally, we designed and implemented two privacy-preserving real-time cloud-based applications to demonstrate the capabilities of HE cryptosystems, in terms of both efficiency and computational capabilities for applications that require timely and reliable delivery of services. First, we developed a secure cloud-based billing model for a sensor-enabled smart grid infrastructure by using lightweight HE. This model handled billing analysis tasks for individual users in a secure manner without the need to interact with any trusted parties. Second, we built a real-time secure health surveillance model for smarter health communities in the cloud. We developed a secure change detection model based on an exponential smoothing technique to predict future changes in health vital signs based on FHE. Moreover, we built an innovative technique to parallelise FHE computations which significantly reduces computational overhead

Security and Privacy Preservation in Mobile Crowdsensing

Mobile crowdsensing (MCS) is a compelling paradigm that enables a crowd of individuals to cooperatively collect and share data to measure phenomena or record events of common interest using their mobile devices. Pairing with inherent mobility and intelligence, mobile users can collect, produce and upload large amounts of data to service providers based on crowdsensing tasks released by customers, ranging from general information, such as temperature, air quality and traffic condition, to more specialized data, such as recommended places, health condition and voting intentions. Compared with traditional sensor networks, MCS can support large-scale sensing applications, improve sensing data trustworthiness and reduce the cost on deploying expensive hardware or software to acquire high-quality data. Despite the appealing benefits, however, MCS is also confronted with a variety of security and privacy threats, which would impede its rapid development. Due to their own incentives and vulnerabilities of service providers, data security and user privacy are being put at risk. The corruption of sensing reports may directly affect crowdsensing results, and thereby mislead customers to make irrational decisions. Moreover, the content of crowdsensing tasks may expose the intention of customers, and the sensing reports might inadvertently reveal sensitive information about mobile users. Data encryption and anonymization techniques can provide straightforward solutions for data security and user privacy, but there are several issues, which are of significantly importance to make MCS practical. First of all, to enhance data trustworthiness, service providers need to recruit mobile users based on their personal information, such as preferences, mobility pattern and reputation, resulting in the privacy exposure to service providers. Secondly, it is inevitable to have replicate data in crowdsensing reports, which may possess large communication bandwidth, but traditional data encryption makes replicate data detection and deletion challenging. Thirdly, crowdsensed data analysis is essential to generate crowdsensing reports in MCS, but the correctness of crowdsensing results in the absence of malicious mobile users and service providers become a huge concern for customers. Finally yet importantly, even if user privacy is preserved during task allocation and data collection, it may still be exposed during reward distribution. It further discourage mobile users from task participation. In this thesis, we explore the approaches to resolve these challenges in MCS. Based on the architecture of MCS, we conduct our research with the focus on security and privacy protection without sacrificing data quality and users' enthusiasm. Specifically, the main contributions are, i) to enable privacy preservation and task allocation, we propose SPOON, a strong privacy-preserving mobile crowdsensing scheme supporting accurate task allocation. In SPOON, the service provider recruits mobile users based on their locations, and selects proper sensing reports according to their trust levels without invading user privacy. By utilizing the blind signature, sensing tasks are protected and reports are anonymized. In addition, a privacy-preserving credit management mechanism is introduced to achieve decentralized trust management and secure credit proof for mobile users; ii) to improve communication efficiency while guaranteeing data confidentiality, we propose a fog-assisted secure data deduplication scheme, in which a BLS-oblivious pseudo-random function is developed to enable fog nodes to detect and delete replicate data in sensing reports without exposing the content of reports. Considering the privacy leakages of mobile users who report the same data, the blind signature is utilized to hide users' identities, and chameleon hash function is leveraged to achieve contribution claim and reward retrieval for anonymous greedy mobile users; iii) to achieve data statistics with privacy preservation, we propose a privacy-preserving data statistics scheme to achieve end-to-end security and integrity protection, while enabling the aggregation of the collected data from multiple sources. The correctness verification is supported to prevent the corruption of the aggregate results during data transmission based on the homomorphic authenticator and the proxy re-signature. A privacy-preserving verifiable linear statistics mechanism is developed to realize the linear aggregation of multiple crowdsensed data from a same device and the verification on the correctness of aggregate results; and iv) to encourage mobile users to participating in sensing tasks, we propose a dual-anonymous reward distribution scheme to offer the incentive for mobile users and privacy protection for both customers and mobile users in MCS. Based on the dividable cash, a new reward sharing incentive mechanism is developed to encourage mobile users to participating in sensing tasks, and the randomization technique is leveraged to protect the identities of customers and mobile users during reward claim, distribution and deposit