Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

Saha, Tanujay; Aaraj, Najwa; Jha, Niraj K.

doi:10.1109/tetc.2022.3147192

Cited by 22 publications

(17 citation statements)

References 26 publications

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“…Existing system has also witnessed the adoption of machine learning towards 5G network as seen in study of Saha et al [39]. The study contributes towards using machine learning for analyzing attack graphs over a virtualized environment of software de ned network in 5G.…”

Section: Review Of Literaturementioning

confidence: 99%

Novel Baseline Computational Predictive Model for Seamless Transmission in 5G

Archana

Shahabadkar²

2023

Preprint

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5G technologies is known for its beneficial characteristic of supporting largely connected network and high-speed data transmission. With increasing number of services and application meant for hosting over 5G network, there is also an increasing concern towards accomplishing better quality of service and quality of experience in global telecommunication sector. In this line of communication advancement, it is also noted that machine learning is one of the contributories and enabling technology towards boosting the performance of value-added services and applications running on 5G networks. Existing review of literature exhibited multiple variants of methodologies meant for performing predictive performance towards leveraging quality of data delivery services in 5G. However, there are quite many research challenges too that is directly associated with deploying a cost-effective learning scheme in 5G. Therefore, the proposed scheme contributes towards developing a novel and yet simplified baseline architecture which targets to accomplishing seamless and reliable data dissemination services in 5G. The proposed model constructs a novel deployment scenario where a user handheld device is considered as a mobile node with an agenda to considered routing in allocated multiple paths to reach its destination. Further, RFC 8822 is used for deployment 5G standard along with a specific mobility model sync with a real-time server via access point and gateway node in large deployment scenario. Finally, an enhanced Long Short-Term Memory is implemented towards performing identification of predictive routes that are shared to users directly over 5G network. The study outcome is benchmarked with existing learning schemes to exhibit that proposed scheme offers approximately 35% of reduced losses, 19% of higher throughput, 23% of reduced delay, 37% of reduced memory, and 41% of reduced processing time.

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Section: Review Of Literaturementioning

confidence: 99%

Novel Baseline Computational Predictive Model for Seamless Transmission in 5G

Archana

Shahabadkar²

2023

Preprint

View full text Add to dashboard Cite

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“…Furthermore, machine learning has impacted efficient implementations of various network protocols [19,24] and secure designs [18]. Such techniques [17] can also be employed in Mimiq to make it more bandwidth and power efficient.…”

Section: Future Workmentioning

confidence: 99%

Secure Address Allocation Mechanism in QUIC-based Protocols

Zhang¹

2023

Preprint

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<p>The emerging QUIC protocol provides efficient migration of IP addresses in a device without breaking the existing connection. This mechanism has been exploited in MIMIQ to provide user privacy and obstruct traffic analysis attacks. The frequent change of a device’s IP address thwarts traffic analysis attacks, and hinders the derivation of user behavioral patterns. This protocol can be useful for ISPs to provide privacy-preserving telecom services. In the original paper of MIMIQ, the authors demonstrate a proof-of-concept (POC) implementation on a single switch. This is a simplified example that is not scalable for real-world applications. In this project, we aim to implement MIMIQ on a user application. To make it easier to adapt, we implement it as a plug-in service on popular QUIC implementations. In the original POC, the address allocation scheme was naive and detailed analysis can reveal the user identity. In this article, we study the security properties of MIMIQ and propose an efficient, secure, and scalable address allocation mechanism.</p>

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“…Otherwise, any unsophisticated dictionary attack can compromise it. Security regulations from the domains of network security find relevance in this domain [19]. Other security features like ASLR, firewalls and isolation boundaries might also prove to be effective.…”

Section: Security Measuresmentioning

confidence: 99%

A Survey on the Real-world Cyberattacks on the Industrial Internet of Things

Lee¹

2023

Preprint

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<p>The Internet-of-Things, popularly referred to as IoT, has been gaining magnanimous attention since the last decade. More and more objects are becoming smart by the integration of sensors in them. All of these objects are interconnected by the internet to produce a variety of services. By 2025, the world is predicted to have over 27 billion devices connected by the Internet-of-Things. However, the internet was not built keeping the security of IoT in mind. This poses a multitude of security threats to IoT. With the expected expansion of the IoT network in the near future, the impact of security breaches will get more dangerous. In this survey, we analyze the biggest attacks that have impaired IoT networks in the past along with the methods which could have been employed to safeguard against such attacks. The aim of this survey is to provide comprehensive guidelines to IoT architects about the potential attack vectors that malicious attackers have exploited in the past and the various defenses that can be undertaken to nullify such attacks. This survey summarizes the lessons that real-world IoT security breaches have taught us over the years</p>

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Machine Learning Assisted Security Analysis of 5G-Network-Connected Systems

Cited by 22 publications

References 26 publications

Novel Baseline Computational Predictive Model for Seamless Transmission in 5G

Novel Baseline Computational Predictive Model for Seamless Transmission in 5G

Secure Address Allocation Mechanism in QUIC-based Protocols

A Survey on the Real-world Cyberattacks on the Industrial Internet of Things

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