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"5G mobile communication systems Security measures."
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5G cyber risks and mitigation
\"5G technology is the next step in the evolution of wireless communication. It offers faster speeds and more bandwidth than 4G. One of the biggest differences between 4G and 5G is that 5G will be used for a wider range of applications. This makes it ideal for applications such as autonomous vehicles, smart cities, and the internet of things. This means that there will be more devices connected to 5G networks, making them more vulnerable to cyber attacks. However, 5G also introduces new cyber risks that need to be addressed. In addition, 5 G networks are expected to be much more complex, making them harder to secure. 5G networks will use new technologies that could make them more vulnerable to attack. These technologies include massive MIMO (multiple input, multiple output), which uses more antennas than traditional cellular networks, and millimeter wave (mmWave), which uses higher frequencies than traditional cellular networks. These new technologies could make it easier for attackers to intercept data or disrupt service. To address these concerns, security measures must be implemented throughout the network. Security mechanisms must be included in the design of 5G networks and must be updated as new threats are identified. Moreover, to address these risks, 5 G security standards need to be developed and implemented. These standards should include measures to protect against Denial of Service (DoS) attacks, malware infections, and other threats. Fortunately, artificial intelligence can play a key role in mitigating these risks. With so many interconnected devices, it can be difficult to identify and isolate malicious traffic. AI can help by identifying patterns in data that would otherwise be undetectable to humans. 6G technology is still in the early developmental stages, but security experts are already voicing concerns about the potential challenges that could arise with this next generation of mobile connectivity. Experts are already working on a roadmap for 6G deployment, and they are confident that these and other challenges can be overcome\"-- Provided by publisher.
Book
A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet‐of‐Medical‐Things
The recent advancements of Internet of Things (IoT) embedded systems, wireless networks, and biosensors those have assisted in the rapid development of implanting wearable sensors are reviewed here. The applications of the internet of medical things (IoMT) that has gained major attention as an ecosystem of connected clinical systems, computing systems, and medical sensors geared towards improving the quality of healthcare services are also reviewed here. The 5G based AI technology can revolute the perception of healthcare and lifestyle. In light of the importance of IoT platforms and 5G networks, the purpose of this proposed research work is to identify threats that could undermine the integrity, privacy, and security of IoMT systems. Also, the novel blockchain‐based approaches that can help in improving the confidentiality of IoMT network. It has been discovered that IoMT is vulnerable to various types of attacks, including denial of service (DoS), malware, and eavesdropping attack. In addition, IoMT is exposed to various vulnerabilities, such as security, privacy, and confidentiality. Despite multiple security threats, there are novel cryptographic techniques, such as access control, identity authentication, and data encryption that can help in improving the security and reliability of IoMT devices.
Journal Article
NT-GNN: Network Traffic Graph for 5G Mobile IoT Android Malware Detection
IoT Android application is the most common implementation system in the mobile ecosystem. As assaults have increased over time, malware attacks will likely happen on 5G mobile IoT Android applications. The huge threat posed by malware to communication systems security has made it one of the main focuses of information security research. Therefore, this paper proposes a new graph neural network model based on a network traffic graph for Android malware detection (NT-GNN). While some current malware detection systems use network traffic data for detection, they ignore the complex structural relationships of network traffic, focusing exclusively on network traffic between pairs of endpoints. Additionally, our suggested network traffic graph neural network model (NT-GNN) considers the graph node and edge aspects, capturing the connection between various traffic flows and individual traffic attributes. We first extract the network traffic graph and then detect it using a novel graph neural network architecture. Finally, we experimented with the proposed NT-GNN model on the well-known Android malware CICAndMal2017 and AAGM datasets and achieved 97% accuracy. The results reflect the sophisticated nature of our methodology. Furthermore, we want to provide a new method for malicious code detection.
Journal Article
Cross-Layer Security for 5G/6G Network Slices: An SDN, NFV, and AI-Based Hybrid Framework
Within the dynamic landscape of fifth-generation (5G) and emerging sixth-generation (6G) wireless networks, the adoption of network slicing has revolutionized telecommunications by enabling flexible and efficient resource allocation. However, this advancement introduces new security challenges, as traditional protection mechanisms struggle to address the dynamic and complex nature of sliced network environments. This study proposes a Hybrid Security Framework Using Cross-Layer Integration, combining Software-Defined Networking (SDN), Network Function Virtualization (NFV), and AI-driven anomaly detection to strengthen network defenses. By integrating security mechanisms across multiple layers, the framework effectively mitigates threats, ensuring the integrity and confidentiality of network slices. An implementation was developed, focusing on the AI-based detection process using a representative 5G security dataset. The results demonstrate promising detection accuracy and real-time response capabilities. While full SDN/NFV integration remains under development, these findings lay the groundwork for scalable, intelligent security architectures tailored to the evolving needs of next-generation networks.
Journal Article
Transformative synergy: SSEHCET—bridging mobile edge computing and AI for enhanced eHealth security and efficiency
Blockchain technologies (BCT) are utilized in healthcare to facilitate a smart and secure transmission of patient data. BCT solutions, however, are unable to store data produced by IoT devices in smart healthcare applications because these applications need a quick consensus process, meticulous key management, and enhanced eprivacy standards. In this work, a smart and secure eHealth framework SSEHCET (Smart and Secure EHealth Framework using Cutting-edge Technologies) is proposed that leverages the potentials of modern cutting-edge technologies (IoT, 5G, mobile edge computing, and BCT), which comprises six layers: 1) The sensing layer-WBAN consists of medical sensors that normally are on or within the bodies of patients and communicate data to smartphones. 2) The edge layer consists of elements that are near IoT devices to collect data. 3) The Communication layer leverages the potential of 5G technology to transmit patients' data between multiple layers efficiently. 4) The storage layer consists of cloud servers or other powerful computers. 5) Security layer, which uses BCT to transmit and store patients' data securely. 6) The healthcare community layer includes healthcare professionals and institutions. For the processing of medical data and to guarantee dependable, safe, and private communication, a Smart Agent (SA) program was duplicated on all layers. The SA leverages the potential of BCT to protect patients' privacy when outsourcing data. The contribution is substantiated through a meticulous evaluation, encompassing security, ease of use, user satisfaction, and SSEHCET structure. Results from an in-depth case study with a prominent healthcare provider underscore SSEHCET's exceptional performance, showcasing its pivotal role in advancing the security, usability, and user satisfaction paradigm in modern eHealth landscapes.
Journal Article
Wireless Communications for Data Security: Efficiency Assessment of Cybersecurity Industry—A Promising Application for UAVs
The design of cooperative applications combining several unmanned aerial and aquatic vehicles is now possible thanks to the considerable advancements in wireless communication technology and the low production costs for small, unmanned vehicles. For example, the information delivered over the air instead of inside an optical fiber causes it to be far simpler for an eavesdropper to intercept and improperly change the information. This article thoroughly analyzes the cybersecurity industry’s efficiency in addressing the rapidly expanding requirement to incorporate compelling security features into wireless communication systems. In this research, we used a combination of DEA window analysis with the Malmquist index approach to assess the efficiency of the cybersecurity industry. We used input and output factors utilizing financial data from 2017–2020 sources from a US market. It was found that U1—Synopsys and U9—Fortinet exhibited the best performances when relating Malmquist and DEA window analysis. By evaluating ten big companies in the cybersecurity industry, we indicate that U2—Palo Alto Networks and U6—BlackBerry Ltd. companies needed significant improvements and that four other companies were generally more efficient. The findings of this study provide decision-makers a clear image and it will be the first study to evaluate and predict the performance of cyber security organizations, providing a valuable reference for future research.
Journal Article
Deep learning based enhanced secure emergency video streaming approach by leveraging blockchain technology for Vehicular AdHoc 5G Networks
VANET is a category of MANET that aims to provide wireless communication. It increases the safety of roads and passengers. Millions of people lose their precious lives in accidents yearly, millions are injured, and others incur disability daily. Emergency vehicles need clear roads to reach their destination faster to save lives. Video streaming can be more effective as compared to textual messages and warnings. To address this issue, we proposed a methodology to use visual sensors, cameras, and OBU to record emergency videos. Initially, the frames are detected. After re-recording, the frames detection algorithm detects the specific event from the video frames. Blockchain encrypts an emergency or specific event using hashing algorithms in the second layer of our proposed framework. In the third layer of the proposed methodology, encrypted video is broadcast with the help of 5G wireless technology to the connected nodes in the VANET. The dataset used in this research comprises up to 72 video sequences averaging about 120 seconds per video. All videos have different traffic conditions and vehicles. The ResNet-50 model is used for the feature extraction process of extracted frames. The model is trained using Tensorflow and Keras deep learning models. The Elbow method finds the optimal K number for the K Means model. This data is split into training and testing. 70% is reserved for training the support vector machine (SVM) model and test datasets, while 30%. 98% accuracy is achieved with 98% precision and 99% recall as results for the proposed methodology.
Journal Article
A machine learning approach for detecting WPA3 downgrade attacks in next-generation Wi-Fi systems
This paper presents a hybrid adaptive approach based on machine learning (ML) for classifying incoming traffic, feature selection and thresholding, aimed at enhancing downgrade attack detection in Wi-Fi Protected Access 3 (WPA3) networks. The fast proliferation of WPA3 is regarded critical for securing modern Wi-Fi systems, which have become integral to 5G and Beyond (5G&B) Radio Access Networks (RAN) architecture. However, the wireless communication channel remains inherently susceptible to downgrade attacks, where adversaries intentionally cause networks to revert from WPA3 to WPA2, with the malicious intent of exploiting known security flaws. Traditional Intrusion Detection Systems (IDS), which rely on fixed-threshold statistical methods, often fail to adapt to changing network environments and new, sophisticated attack strategies. To address this limitation, we introduce a novel ML-based Feature Selection and Thresholding for Downgrade Attacks Detection (MFST-DAD) approach, which comprises three stages: traffic data preprocessing, baseline adaptive feature selection, and real-time detection and prevention using ML algorithms. Experimental results on a specially generated dataset demonstrate that the proposed approach detects downgrade attacks in WPA3 networks, achieving 99.8% accuracy with a Naive Bayes classifier in both WPA3 personal and enterprise transition modes. These findings confirm the effectiveness of our proposed approach in securing next-generation Wi-Fi systems.
Journal Article
A Vulnerability Assessment of Open-Source Implementations of Fifth-Generation Core Network Functions
The paper presents an experimental security assessment within two widely used open-source 5G projects, namely Open5GS and OAI (Open-Air Interface). The examination concentrates on two network functions (NFs) that are externally exposed within the core network architecture, i.e., the Access and Mobility Management Function (AMF) and the Network Repository Function/Network Exposure Function (NRF/NEF) of the Service-Based Architecture (SBA). Focusing on the Service-Based Interface (SBI) of these exposed NFs, the analysis not only identifies potential security gaps but also underscores the crucial role of Mobile Network Operators (MNOs) in implementing robust security measures. Furthermore, given the shift towards Network Function Virtualization (NFV), this paper emphasizes the importance of secure development practices to enhance the integrity of 5G network functions. In essence, this paper underscores the significance of scrutinizing security vulnerabilities in open-source 5G projects, particularly within the core network’s SBI and externally exposed NFs. The research outcomes provide valuable insights for MNOs, enabling them to establish effective security measures and promote secure development practices to safeguard the integrity of 5G network functions. Additionally, the empirical investigation aids in identifying potential vulnerabilities in open-source 5G projects, paving the way for future enhancements and standard releases.
Journal Article
A brief review on security issues and counter measure techniques for future generation communication system (LTE/LTE-A)
The article addresses the issue of current and critical protections for applications for 4G and 5G mobile phones. The objective of the article is to provide an overview of the threats to 4G and 5G mobile communication networks and to analyze the variables used in conjunction with core and security methods. The research work uses a review of the literature to provide an overview of the threats to 4G and 5G mobile communication networks. The research also analyzed the variables used in conjunction with core and security methods. The research presents an overview of the threats to 4G and 5G mobile communication networks, including privacy attacks, behavioral attacks, space attacks, and authentication attacks. The authors also provide an overview of three types of cases, including road signs, officers, and road signs. The analysis of variables used in conjunction with core and security methods is unclear, often summarizing security. The authors conclude that there is a need for improved protections for applications for 4G and 5G mobile phones. They also suggest that further research is needed to better understand the variables used in conjunction with core and security methods.
Journal Article