Attack Detection and Mitigation in MEC-Enabled 5G Networks for AIoT

ABSTRACT6G technology is expected to transform communications by allowing the Internet of Everything, representing a huge advance in 2030. While B5G has not yet been established, various nations are actively working on 5G, but certain research groups are presently devoting their attention to the creation of 6G technologies. The upcoming 6G networks promise higher quality of service (QoS) features, including virtual reality and holographic communications. Multi‐Access Edge Computing (MEC) and, in particular, the offloading idea are key components of 6G innovation that enable resource‐intensive application design. If the wireless link used for computational offloading is inefficient, MEC's true potential can be impeded. Intelligent beamforming and MEC have garnered attention recently. Systematically optimizing the wireless communication environment, these developments improve connectivity between user equipment (UE) and base station (BS). By increasing the electrical charge of the reflectable signal, intelligent beamforming increases the range and efficacy of Back Communication. Particularly, this study assesses how well the MEC structure performs in urban microcellular settings when intelligent beamforming is used in communications. It has been demonstrated that the use of Intelligent Reflecting Surfaces (IRS) greatly reduces spectrum and energy usage. This study's results are implemented in Python software. Our suggested strategy shows better latency compared to other optimization methods and shorter task completion times than traditional methods. When compared to conventional techniques, the suggested MEC‐enabled network showcasing lower latency (4.9 ms) and efficient network congestion management and task completion time (30 ms) demonstrates a significant performance. These results highlight how intelligent beamforming and MEC have a lot of potential to shape the architecture of 6G networks in the future.

Multi-access Edge Computing (MEC) can provide the intelligent platform or environment for services with the requirements of lower latency, higher bandwidth, and higher computation complexity, thereby attracting more attention in the future network. Intuitively, MEC can be applied to 4G and 5G mobile networks respectively, however the corresponding MEC architectures will be entirely different because of the difference between 4G and 5G network architectures. Therefore, this paper firstly presents the 4G MEC architecture and deeply discusses the existing problems in field trials and future deployment, e.g. charging, lawful interception, etc. Considering the above problems, the 5G MEC integrated architecture is proposed in this paper, which takes the network and service functions into account simultaneously. Meanwhile, the network function, especially for the traffic offloading mechanism, is emphatically analyzed and discussed. In addition, some suggestions are also presented for promotion of 5G MEC.

This study focuses on the security of 5G mobile network major technology called Multi-Access Edge Computing (MEC) and its susceptibility to distributed denial of service (DDoS) attacks. The goal of the research is to address the effects of DDoS attacks and implement effective mitigation techniques. Several supervised Machine Learning (ML) techniques, which include Random Forest (RF), Decision Tree, Naïve Bayes, K-Nearest Neighbour, Logistics Regression, and Blending/Stack Model, are evaluated using multiple performance metrics such as accuracy, detection/recall, F1-Measure, Matthew’s correlation coefficient, Receiver Operating Characteristic, and Area Under Receiver Operating Characteristic. According to literature, ML algorithms achieve the best performance in mitigating DDoS attacks, therefore, they can be optimized to enhance their effectiveness. The research provides an overview of the existing mitigation schemes in the MEC and proposes a DDoS mitigation scheme. The findings show that hybrid models outperformed traditional ML models. Among the mitigation techniques evaluated, RF proved to be the most effective in mitigating DDoS attacks in MEC.

Multi-Access Edge Computing (MEC) is a fundamental component for 5G networks to overcome the latency limitations of traditional cloud computing. However, bringing resources closer to users exposes edge nodes to significant security threats, particularly volumetric Denial of Service (DoS) attacks. Current defenses often depend on static thresholds or computationally expensive deep learning, which can exhaust the limited resources of MEC nodes. To address these limitations, this paper proposes a resource-optimized edge-centric security management logic that integrates Software Defined Network (SDN) with lightweight supervised learning (C5.0, Bagging-CART, and Random Forest). Unlike standard system integrations, we introduce a dynamic non-permanent blocking algorithm designed to balance detection accuracy with control plane stability. Experimental results demonstrate that the proposed C5.0 model, operating at a specific 0.20% sFlow sampling point, achieves 100% detection accuracy with under 100 ms mitigation latency. The system successfully reduces volumetric attack loads from 445 Mbps to 95 Mbps (a 78% reduction) at the node level. These findings confirm that the proposed framework achieves higher computational efficiency than complex alternatives, making it a highly stable solution for constrained 5G MEC environments.

According to the World Health Organization, about 15% of the world's population has some form of disability. Assistive Technology, in this context, contributes directly to the overcoming of difficulties encountered by people with disabilities in their daily lives, allowing them to receive education and become part of the labor market and society in a worthy manner. Assistive Technology has made great advances in its integration with Artificial Intelligence of Things (AIoT) devices. AIoT processes and analyzes the large amount of data generated by Internet of Things (IoT) devices and applies Artificial Intelligence models, specifically, machine learning, to discover patterns for generating insights and assisting in decision making. Based on a systematic literature review, this article aims to identify the machine-learning models used across different research on Artificial Intelligence of Things applied to Assistive Technology. The survey of the topics approached in this article also highlights the context of such research, their application, the IoT devices used, and gaps and opportunities for further development. The survey results show that 50% of the analyzed research address visual impairment, and, for this reason, most of the topics cover issues related to computational vision. Portable devices, wearables, and smartphones constitute the majority of IoT devices. Deep neural networks represent 81% of the machine-learning models applied in the reviewed research.

Multi access edge computing (MEC) is an extension of 5G network on the edge side. Its network characteristics of high bandwidth and low delay provide necessary guarantee for the extreme user experience of VR (virtual reality). At the same time, the distributed architecture of 5g MEC network and the fast data exchange capability between nodes provide the hardware foundation for VR application marginalization deployment and equipment mobility access. Considering the cost and experience of VR, this paper first analyzes the advantages and disadvantages of VR based on traditional hardware architecture, and then describes the benefits of MEC on VR cost and experience. Finally, taking VR live video scene as an example, the key technologies of VR based on MEC are described.

The complexity of the Internet and the volume of network traffic have dramatically increased in the last few years, making it more challenging to design scalable Network Traffic Monitoring and Analysis (NTMA) systems. Critical NTMA applications such as the detection of network attacks and anomalies require fast mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. The high-dimensionality of network data provided by current network monitoring systems opens the door to the massive application of machine learning approaches to improve the detection and classification of network attacks and anomalies, but this higher dimensionality comes with an extra data processing overhead. In this paper we present Big-DAMA, a big data analytics framework (BDAF) for NTMA applications. Big-DAMA is a flexible BDAF, capable to analyze and store big amounts of both structured and unstructured heterogeneous data sources, with both stream and batch processing capabilities. Big-DAMA uses off-the-shelf big data storage and processing engines to offer both stream data processing and batch processing capabilities, decomposing separate engines for stream, batch and query, following a Data Stream Warehouse (DSW) paradigm. Big-DAMA implements several algorithms for anomaly detection and network security using supervised and unsupervised machine learning (ML) models, using off-the-shelf ML libraries. We apply Big-DAMA to the detection of different types of network attacks and anomalies, benchmarking multiple supervised ML models. Evaluations are conducted on top of real network measurements collected at the WIDE backbone network, using the well-known MAWILab dataset for attacks labeling. Big-DAMA can speed up computations by a factor of 10 when compared to a standard Apache Spark cluster, and can be easily deployed in cloud environments, using hardware virtualization technology.

Optimal resource management for multi-access edge computing without using cross-layer communication

Empowered by promising artificial intelligence, the traditional Internet of Things is evolving into the Artificial Intelligence of Things (AIoT), which is an important enabling technology for Industry 4.0. Collaborative learning is a key technology for AIoT to build machine learning (ML) models on distributed datasets. However, there are two critical concerns of collaborative learning for AIoT: privacy leakage of sensitive data and dishonest computation. Specifically, data contains sensitive information of users, which cannot be openly shared for model learning. Furthermore, to protect the privacy of data or other selfish purposes, participants of collaborative learning may behave dishonestly, submitting dummy data or incorrect model computation. Therefore, it is important to guarantee privacy preservation of data and honest computation on collaborative learning. Our work tackles the two concerns wherein a model demander can securely train ML models with sensitive data and can regulate the computation of participants. To this end, we propose a secure and trusted collaborative learning framework called TrusCL. The framework guarantees privacy preservation via a delicate combination of homomorphic encryption (HE) and differential privacy (DP), achieving the trade-off between efficiency and accuracy. Furthermore, based on blockchain, in our design, the key steps of secure collaborative learning are recorded on blockchain so that malicious behaviors can be effectively tracked and choked in a timely manner to facilitate trusted computation. Experimental results validate the trade-off performance of Trus-CL between model training efficiency and trained model accuracy.

Today, smart supply chains are one of the main and growing applications of evolving technologies such as the Internet of Things (IoT), artificial intelligence (AI), and blockchain, which bring many benefits. IoT and blockchain security is a subset of cybersecurity that protects against IoT-related threats. Therefore, the simultaneous combination of these technologies and the creation of a supply chain based on the artificial intelligence of things (AIoT) and blockchain, in addition to increasing the performance of activities in supply chain processes, can also bring many security challenges to this smart supply chain. Because the data collected by IoT devices and stored in the blockchain-based systems that manage them are much more sensitive than traditional network security strategies.Due to the importance of this issue, in this study, an attempt has been made to analyse these security indicators in the AIoT and blockchain-based intelligent supply chain using a combined decision-making method in a fuzzy neutrosophic environment. Understanding these security measures will definitely help to implement more secure and powerful smart supply chains. The results showed that customer privacy criteria have the highest priority. The following priorities are authentication, Security challenges based on infrastructure, and network integrity.

The support of stringent requirements such as ultra-low latency and ultra-reliability of the forthcoming 5G services poses several challenges to telecommunications infrastructure providers. Network Function Virtualization, multi-access edge computing (MEC), and network slicing capabilities can help the support of such requirements. However, a trade-off between the cost of resource deployment and the support of service requirements needs to be taken into account in the design of NFV-based 5G networks. In this paper, we investigate the MEC location problem, which aims at selecting locations to place MECs hosting protected slices. We propose a MEC location problem enhanced with 1: 1 and 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> protection schemes for the provisioning of protected slices. In the 1: 1 scheme, protection is assured by reserving a backup slice for each tenant, whereas in the 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> scheme, a backup slice is shared among <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> tenants. The problem is modeled as a multi-criteria optimization problem and solved by the employment of a multi-objective evolutionary non-dominated sorting genetic algorithm. A comparison between the 1: 1 and 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> protection schemes is carried out in the context of 5G network slicing. Results show that the protection scheme 1: 1 can reduce the response time, at a higher deployment cost when compared to the 1: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> scheme.

Ultra-low end-to-end latency is one of the most important requirements in 5G networks and beyond to support latency-critical applications. Cloud radio access networks and multi-access edge computing (MEC) are considered as the key driving technology that can help reduce end-to-end latency. However, the use of MEC nodes poses radical changes to the access network architecture. As it brings the processing and networking services closer to the edge, it often requires network functions [for example, the centralized unit/distributed unit (CU/DU) stack and application processing] to be distributed across different MEC sites. Therefore, a transport mechanism is needed to efficiently coordinate and connect network functions across MEC nodes. To address this challenge, we propose a two-tier virtualized passive optical network (PON) transport method with scheduler coordination over a virtualized and sliced MESH-PON architecture. While a MESH-PON architecture enables direct communication between MEC nodes hosting CUs/DUs and/or the application processing, our method provides a two-tier virtualized PON transport scheme with coordinated schedulers. This approach greatly reduces latency incurred in transporting signals across the different PON tiers, while maintaining the flexibility of multi-tier methods. We show that our proposed scheme can achieve end-to-end application-level latency below 1 or 2 ms, depending on the network configurations.

Telehealth and remote patient monitoring (RPM) have been critical components that have received substantial attention and gained hold since the pandemic's beginning. Telehealth and RPM allow easy access to patient data and help provide high-quality care to patients at a low cost. This article proposes an Intelligent Remote Patient Activity Tracking System system that can monitor patient activities and vitals during those activities based on the attached sensors. An Internet of Things- (IoT-) enabled health monitoring device is designed using machine learning models to track patient's activities such as running, sleeping, walking, and exercising, the vitals during those activities such as body temperature and heart rate, and the patient's breathing pattern during such activities. Machine learning models are used to identify different activities of the patient and analyze the patient's respiratory health during various activities. Currently, the machine learning models are used to detect cough and healthy breathing only. A web application is also designed to track the data uploaded by the proposed devices.

Multi-access edge computing (MEC) is a new 5G enabling technology proposed to reduce latency by bringing cloud computing capability closer to IoT and mobile device users. MEC may be prone to unreliable communication as a result of deadlock during resource provisioning. Deadlock may occur due to a huge number of devices contending for a limited amount of resources if adequate measures are not put in place. It is crucial to eradicate deadlock while scheduling and provisioning of resources on MEC to achieve highly reliable and available system. In this paper, a deadlock avoidance resource provisioning algorithm is proposed for Industrial IoT devices using MEC platforms to ensure the higher reliability of network interactions. The proposed scheme incorporates a banker’s resource-request algorithm using software-defined networking to reduce communication overhead. Simulation Results have shown that system deadlock can be prevented by applying the proposed algorithm which ultimately leads to a more reliable network interaction between mobile stations and MEC platforms.

Aims: This research aims to secure and support mobile devices and IoTs enabled in WLAN and 5G Multi-Access Edge Computing (MEC) infrastructures. Background: Currently, wireless network access gains increasing potential in today's networks. At the same time, such ongoing wireless network access also raises the risk of network attacks. The 5G technology is expected to empower people inter-communications by integrating wireless networking technologies in all kinds of networks. Additionally, the emerging 5G technologies put forward many new requirements for RF characteristics and techniques regarding bandwidth and power issues, in addition to supporting high connectivity capacity for the emerging IP-based IoT applications. Objective: The objective is to provide a hybrid architecture of the proposed framework for a 5G network with cloudlet computing. The purpose that we are focusing on it is to support and monitor wireless networks under attack. Methods: The solution is done through a hybrid architecture. In this architecture, the cloudlet and Wi-Fi access points (AP) are integrated for providing an integrated Wi-Fi-enabled cloudlet. On the other hand, we have specified a solution for detecting unauthorized APs by using authentication and authorization techniques for securing communications among endpoint devices such as IoT devices or mobile devices, cloudlet, and the main cloud. A framework of this architecture has been developed to face the issue of securing IoT devices and mobile wireless devices. Results: The traffic sniffing and traffic filtering of the endpoints are done. Therefore, the required actions that define the organization's policy are applied through the permitted access list. In addition to a wireless network location, the management components use more than one authorized attribute value to generate the authorized list, such as SSID, MAC, BSSID …etc. Finally, Traffic analysis provides the user to get reports, statistics, and analysis charts of secure endpoints activities. Conclusion: We are focusing on one of the critical security issues of wireless networks. Namely, the issue of unauthorized access becomes more critical due to getting access to a network without subscriber permission. Moreover, the proposed solution allows managing access control permissions to allow or block access to resources for the user of a mobile device.