7,108 research outputs found
A novel distributed authentication of blockchain technology integration in IoT services
Internet of Things (IoT) is currently playing a major role in how intelligent devices are interconnected and deployed to automate services in transport and smart living sectors. However, IoT is facing challenges in terms of data protection and authentication due to the heterogeneous nature of IoT devices that do not exhibit a central authority. It is crucial to provide secure and trustworthy solutions for the increasing demands of decentralized IoT environments. To this end, this research proposes a novel integration of blockchain-technologies in IoT services to enhance security, data integrity, users privacy, system scalability and interoperability of devices. This is done by leveraging smart contracts to enforce authentication, access control and data exchange mechanisms for IoT devices. The proposed approach is verified by the construction and deployment of a smart contract over the Polygon blockchain network in a simulated real-world IoT scenario. The obtained results show that the proposed approach ensures fast and secure authentication in IoT networks by decreasing the risk of unauthorized access and data tampering
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Securing mobile edge computing using hybrid deep learning method
In recent years, Mobile Edge Computing (MEC) has revolutionized the landscape of the telecommunication industry by offering low-latency, high-bandwidth, and real-time processing. With this advancement comes a broad range of security challenges, the most prominent of which is Distributed Denial of Service (DDoS) attacks, which threaten the availability and performance of MECâs services. In most cases, Intrusion Detection Systems (IDSs), a security tool that monitors networks and systems for suspicious activity and notify administrators in real time of potential cyber threats, have relied on shallow Machine Learning (ML) models that are limited in their abilities to identify and mitigate DDoS attacks. This article highlights the drawbacks of current IDS solutions, primarily their reliance on shallow ML techniques, and proposes a novel hybrid AutoencoderâMulti-Layer Perceptron (AEâMLP) model for intrusion detection as a solution against DDoS attacks in the MEC environment. The proposed hybrid AEâMLP model leverages autoencodersâ feature extraction capabilities to capture intricate patterns and anomalies within network traffic data. This extracted knowledge is then fed into a Multi-Layer Perceptron (MLP) network, enabling deep learning techniques to further analyze and classify potential threats. By integrating both AE and MLP, the hybrid model achieves higher accuracy and robustness in identifying DDoS attacks while minimizing false positives. As a result of extensive experiments using the recently released NF-UQ-NIDS-V2 dataset, which contains a wide range of DDoS attacks, our results demonstrate that the proposed hybrid AEâMLP model achieves a high accuracy of 99.98%. Based on the results, the hybrid approach performs better than several similar techniques
AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
The next generation of Internet services, such as Metaverse, rely on mixed
reality (MR) technology to provide immersive user experiences. However, the
limited computation power of MR headset-mounted devices (HMDs) hinders the
deployment of such services. Therefore, we propose an efficient information
sharing scheme based on full-duplex device-to-device (D2D) semantic
communications to address this issue. Our approach enables users to avoid heavy
and repetitive computational tasks, such as artificial intelligence-generated
content (AIGC) in the view images of all MR users. Specifically, a user can
transmit the generated content and semantic information extracted from their
view image to nearby users, who can then use this information to obtain the
spatial matching of computation results under their view images. We analyze the
performance of full-duplex D2D communications, including the achievable rate
and bit error probability, by using generalized small-scale fading models. To
facilitate semantic information sharing among users, we design a contract
theoretic AI-generated incentive mechanism. The proposed diffusion model
generates the optimal contract design, outperforming two deep reinforcement
learning algorithms, i.e., proximal policy optimization and soft actor-critic
algorithms. Our numerical analysis experiment proves the effectiveness of our
proposed methods. The code for this paper is available at
https://github.com/HongyangDu/SemSharingComment: Accepted by IEEE JSA
LATEST ADVANCES ON SECURITY ARCHITECTURE FOR 5G TECHNOLOGY AND SERVICES
The roll out of the deployment of the 5G technology has been ongoing globally. The
deployment of the technologies associated with 5G has seen mixed reaction as regards its
prospects to improve communication services in all spares of life amid its security concerns. The
security concerns of 5G network lies in its architecture and other technologies that optimize the
performance of its architecture. There are many fractions of 5G security architecture in the
literature, a holistic security architectural structure will go a long way in tackling the security
challenges. In this paper, the review of the security challenges of the 5G technology based on its
architecture is presented along with their proposed solutions. This review was carried out with
some keywords relating to 5G securities and architecture; this was used to retrieve appropriate
literature for fitness of purpose. The 5G security architectures are mojorly centered around the
seven network security layers; thereby making each of the layers a source of security concern on
the 5G network. Many of the 5G security challenges are related to authentication and authorization
such as denial-of-service attacks, man in the middle attack and eavesdropping. Different methods
both hardware (Unmanned Aerial Vehicles, field programmable logic arrays) and software (Artificial
intelligence, Machine learning, Blockchain, Statistical Process Control) has been proposed for
mitigating the threats. Other technologies applicable to 5G security concerns includes: Multi-radio
access technology, smart-grid network and light fidelity. The implementation of these solutions
should be reviewed on a timely basis because of the dynamic nature of threats which will greatly
reduce the occurrence of security attacks on the 5G network
A survey on reconfigurable intelligent surfaces: wireless communication perspective
Using reconfigurable intelligent surfaces (RISs) to improve the coverage and the data rate of future wireless networks is a viable option. These surfaces are constituted of a significant number of passive and nearly passive components that interact with incident signals in a smart way, such as by reflecting them, to increase the wireless system's performance as a result of which the notion of a smart radio environment comes to fruition. In this survey, a study review of RIS-assisted wireless communication is supplied starting with the principles of RIS which include the hardware architecture, the control mechanisms, and the discussions of previously held views about the channel model and pathloss; then the performance analysis considering different performance parameters, analytical approaches and metrics are presented to describe the RIS-assisted wireless network performance improvements. Despite its enormous promise, RIS confronts new hurdles in integrating into wireless networks efficiently due to its passive nature. Consequently, the channel estimation for, both full and nearly passive RIS and the RIS deployments are compared under various wireless communication models and for single and multi-users. Lastly, the challenges and potential future study areas for the RIS aided wireless communication systems are proposed
A Generalized Look at Federated Learning: Survey and Perspectives
Federated learning (FL) refers to a distributed machine learning framework
involving learning from several decentralized edge clients without sharing
local dataset. This distributed strategy prevents data leakage and enables
on-device training as it updates the global model based on the local model
updates. Despite offering several advantages, including data privacy and
scalability, FL poses challenges such as statistical and system heterogeneity
of data in federated networks, communication bottlenecks, privacy and security
issues. This survey contains a systematic summarization of previous work,
studies, and experiments on FL and presents a list of possibilities for FL
across a range of applications and use cases. Other than that, various
challenges of implementing FL and promising directions revolving around the
corresponding challenges are provided.Comment: 9 pages, 2 figure
Artificial Intelligence Based Deep Bayesian Neural Network (DBNN) Toward Personalized Treatment of Leukemia with Stem Cells
The dynamic development of computer and software technology in recent years was accompanied by the expansion and widespread implementation of artificial intelligence (AI) based methods in many aspects of human life. A prominent field where rapid progress was observed are highâthroughput methods in biology that generate big amounts of data that need to be processed and analyzed. Therefore, AI methods are more and more applied in the biomedical field, among others for RNAâprotein binding sites prediction, DNA sequence function prediction, proteinâprotein interaction prediction, or biomedical image classification. Stem cells are widely used in biomedical research, e.g., leukemia or other disease studies. Our proposed approach of Deep Bayesian Neural Network (DBNN) for the personalized treatment of leukemia cancer has shown a significant tested accuracy for the model. DBNNs used in this study was able to classify images with accuracy exceeding 98.73%. This study depicts that the DBNN can classify cell cultures only based on unstained light microscope images which allow their further use. Therefore, building a bayesianâbased model to great help during commercial cell culturing, and possibly a first step in the process of creating an automated/semiautomated neural networkâbased model for classification of good and bad quality cultures when images of such will be available
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