118 research outputs found
A Survey of Physical Layer Security Techniques for 5G Wireless Networks and Challenges Ahead
Physical layer security which safeguards data confidentiality based on the
information-theoretic approaches has received significant research interest
recently. The key idea behind physical layer security is to utilize the
intrinsic randomness of the transmission channel to guarantee the security in
physical layer. The evolution towards 5G wireless communications poses new
challenges for physical layer security research. This paper provides a latest
survey of the physical layer security research on various promising 5G
technologies, including physical layer security coding, massive multiple-input
multiple-output, millimeter wave communications, heterogeneous networks,
non-orthogonal multiple access, full duplex technology, etc. Technical
challenges which remain unresolved at the time of writing are summarized and
the future trends of physical layer security in 5G and beyond are discussed.Comment: To appear in IEEE Journal on Selected Areas in Communication
A Comprehensive Survey On Client Selections in Federated Learning
Federated Learning (FL) is a rapidly growing field in machine learning that
allows data to be trained across multiple decentralized devices. The selection
of clients to participate in the training process is a critical factor for the
performance of the overall system. In this survey, we provide a comprehensive
overview of the state-of-the-art client selection techniques in FL, including
their strengths and limitations, as well as the challenges and open issues that
need to be addressed. We cover conventional selection techniques such as random
selection where all or partial random of clients is used for the trained. We
also cover performance-aware selections and as well as resource-aware
selections for resource-constrained networks and heterogeneous networks. We
also discuss the usage of client selection in model security enhancement.
Lastly, we discuss open issues and challenges related to clients selection in
dynamic constrained, and heterogeneous networks
How Physicality Enables Trust: A New Era of Trust-Centered Cyberphysical Systems
Multi-agent cyberphysical systems enable new capabilities in efficiency,
resilience, and security. The unique characteristics of these systems prompt a
reevaluation of their security concepts, including their vulnerabilities, and
mechanisms to mitigate these vulnerabilities. This survey paper examines how
advancement in wireless networking, coupled with the sensing and computing in
cyberphysical systems, can foster novel security capabilities. This study
delves into three main themes related to securing multi-agent cyberphysical
systems. First, we discuss the threats that are particularly relevant to
multi-agent cyberphysical systems given the potential lack of trust between
agents. Second, we present prospects for sensing, contextual awareness, and
authentication, enabling the inference and measurement of ``inter-agent trust"
for these systems. Third, we elaborate on the application of quantifiable trust
notions to enable ``resilient coordination," where ``resilient" signifies
sustained functionality amid attacks on multiagent cyberphysical systems. We
refer to the capability of cyberphysical systems to self-organize, and
coordinate to achieve a task as autonomy. This survey unveils the cyberphysical
character of future interconnected systems as a pivotal catalyst for realizing
robust, trust-centered autonomy in tomorrow's world
The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey
As smart grids (SG) increasingly rely on advanced technologies like sensors
and communication systems for efficient energy generation, distribution, and
consumption, they become enticing targets for sophisticated cyberattacks. These
evolving threats demand robust security measures to maintain the stability and
resilience of modern energy systems. While extensive research has been
conducted, a comprehensive exploration of proactive cyber defense strategies
utilizing Deep Learning (DL) in {SG} remains scarce in the literature. This
survey bridges this gap, studying the latest DL techniques for proactive cyber
defense. The survey begins with an overview of related works and our distinct
contributions, followed by an examination of SG infrastructure. Next, we
classify various cyber defense techniques into reactive and proactive
categories. A significant focus is placed on DL-enabled proactive defenses,
where we provide a comprehensive taxonomy of DL approaches, highlighting their
roles and relevance in the proactive security of SG. Subsequently, we analyze
the most significant DL-based methods currently in use. Further, we explore
Moving Target Defense, a proactive defense strategy, and its interactions with
DL methodologies. We then provide an overview of benchmark datasets used in
this domain to substantiate the discourse.{ This is followed by a critical
discussion on their practical implications and broader impact on cybersecurity
in Smart Grids.} The survey finally lists the challenges associated with
deploying DL-based security systems within SG, followed by an outlook on future
developments in this key field.Comment: To appear in the IEEE internet of Things journa
A Survey on Security and Privacy of 5G Technologies: Potential Solutions, Recent Advancements, and Future Directions
Security has become the primary concern in many telecommunications industries today as risks can have high consequences. Especially, as the core and enable technologies will be associated with 5G network, the confidential information will move at all layers in future wireless systems. Several incidents revealed that the hazard encountered by an infected wireless network, not only affects the security and privacy concerns, but also impedes the complex dynamics of the communications ecosystem. Consequently, the complexity and strength of security attacks have increased in the recent past making the detection or prevention of sabotage a global challenge. From the security and privacy perspectives, this paper presents a comprehensive detail on the core and enabling technologies, which are used to build the 5G security model; network softwarization security, PHY (Physical) layer security and 5G privacy concerns, among others. Additionally, the paper includes discussion on security monitoring and management of 5G networks. This paper also evaluates the related security measures and standards of core 5G technologies by resorting to different standardization bodies and provide a brief overview of 5G standardization security forces. Furthermore, the key projects of international significance, in line with the security concerns of 5G and beyond are also presented. Finally, a future directions and open challenges section has included to encourage future research.European CommissionNational Research Tomsk Polytechnic UniversityUpdate citation details during checkdate report - A
Securing Cognitive Radio Networks using blockchains
Due to the increase in industrial applications of Internet of Things (IoT), number of internet connected devices have been increased accordingly. This has resulted in big challenges in terms of accessibility, scalability, connectivity and adaptability. IoT is capable of creating connections between devices on wireless medium but the utilization of scarce spectrum in efficient manner for the establishment of these connections is the biggest concern. To accommodate spectrum allocation problem different radio technologies are being utilized. One of the most efficient technique being used is cognitive radio, which dynamically allocate the unlicensed spectrum for IoT applications. Spectrum sensing being the fundamental component of Cognitive Radio Network (CRN) is threatened by security attacks. Process of spectrum sensing is disturbed by the malicious user (MU) which attacks the primary signal detection and affects the accuracy of sensing outcome. The presence of such MU in system, sending false sensing data can degrade the performance of cognitive radios. Therefore, in this article a blockchain based method is proposed for the MU detection in network. By using this method an MU can easily be discriminated from a reliable user through cryptographic keys. The efficiency of the proposed mechanism is analyzed through proper simulations using MATLAB. Consequently, this mechanism can be deployed for the validation of participating users in the process of spectrum sensing in CRN for IoTs.publishe
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