scholarly journals IoT based F-RAN Architecture using Cloud and Edge Detection System

2021 ◽  
Vol 3 (1) ◽  
pp. 31-39
Author(s):  
Subarna Shakya
Keyword(s):  
Edge Detection ◽  
Detection System ◽  
Access Network ◽  
Cell System ◽  
Free Access ◽  
Diagnostic Purpose ◽  
Central Processor ◽  
Probability Of Error ◽  
Radio Access ◽  
Iot Devices

A multi-cell Fog-Radio Access Network (F-RAN) architecture that takes into consideration the noisy interference from Internet of Things (IoT) devices and transmission takes place in the uplink with grant-free access. An edge node is used to connect the devices present in every cell and will hold a reasonable capacity in the central processor. The reading obtained from the IoT devices are used to determine the field of correlated Quality of Interests in every cell, transmitting using the Type-Based Multiple Access (TBMA) protocol. This is in contrast to the conventional protocols that are used for diagnostic purpose. In this proposed work, we have implemented the multi-cell F-RAN using cloud or edge detection in analysing the form of information-centric radio access. In a multi-cell system, cloud and edge detection are implemented and analysed. We have implemented model-based detectors and the probability of error for the asymptotic behavior in edge as well as cloud is determined. Similarly, cloud and edge detectors that are data driven are used when statistical models are not available.

scholarly journals SANS: Self-Sovereign Authentication for Network Slices

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xavier Salleras ◽  
Vanesa Daza
Keyword(s):  
Private Information ◽  
Access Network ◽  
Memory Consumption ◽  
Cryptographic Primitives ◽  
Specific Service ◽  
Network Slicing ◽  
Full Control ◽  
Radio Access ◽  
5G Network ◽  
Iot Devices

5G communications proposed significant improvements over 4G in terms of efficiency and security. Among these novelties, the 5G network slicing seems to have a prominent role: deploy multiple virtual network slices, each providing a different service with different needs and features. Like this, a Slice Operator (SO) ruling a specific slice may want to offer a service for users meeting some requirements. It is of paramount importance to provide a robust authentication protocol, able to ensure that users meet the requirements, providing at the same time a privacy-by-design architecture. This makes even more sense having a growing density of Internet of Things (IoT) devices exchanging private information over the network. In this paper, we improve the 5G network slicing authentication using a Self-Sovereign Identity (SSI) scheme: granting users full control over their data. We introduce an approach to allow a user to prove his right to access a specific service without leaking any information about him. Such an approach is SANS, a protocol that provides nonlinkable protection for any issued information, preventing an SO or an eavesdropper from tracking users’ activity and relating it to their real identities. Furthermore, our protocol is scalable and can be taken as a framework for improving related technologies in similar scenarios, like authentication in the 5G Radio Access Network (RAN) or other wireless networks and services. Such features can be achieved using cryptographic primitives called Zero-Knowledge Proofs (ZKPs). Upon implementing our solution using a state-of-the-art ZKP library and performing several experiments, we provide benchmarks demonstrating that our approach is affordable in speed and memory consumption.

scholarly journals IoT Botnet Anomaly Detection Using Unsupervised Deep Learning

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1876
Author(s):  
Ioana Apostol ◽  
Marius Preda ◽  
Constantin Nila ◽  
Ion Bica
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
False Positive Rate ◽  
Empirical Evaluation ◽  
Learning Approaches ◽  
Promising Alternative ◽  
Positive Rate ◽  
Unsupervised Deep Learning ◽  
Attack Surface ◽  
Iot Devices

The Internet of Things has become a cutting-edge technology that is continuously evolving in size, connectivity, and applicability. This ecosystem makes its presence felt in every aspect of our lives, along with all other emerging technologies. Unfortunately, despite the significant benefits brought by the IoT, the increased attack surface built upon it has become more critical than ever. Devices have limited resources and are not typically created with security features. Lately, a trend of botnet threats transitioning to the IoT environment has been observed, and an army of infected IoT devices can expand quickly and be used for effective attacks. Therefore, identifying proper solutions for securing IoT systems is currently an important and challenging research topic. Machine learning-based approaches are a promising alternative, allowing the identification of abnormal behaviors and the detection of attacks. This paper proposes an anomaly-based detection solution that uses unsupervised deep learning techniques to identify IoT botnet activities. An empirical evaluation of the proposed method is conducted on both balanced and unbalanced datasets to assess its threat detection capability. False-positive rate reduction and its impact on the detection system are also analyzed. Furthermore, a comparison with other unsupervised learning approaches is included. The experimental results reveal the performance of the proposed detection method.

scholarly journals Cooperation and information replication in wireless networks

2016 ◽  
Vol 374 (2062) ◽  
pp. 20150123 ◽  
Author(s):  
Konstantinos Poularakis ◽  
Leandros Tassiulas
Keyword(s):  
Network Performance ◽  
Access Network ◽  
Network Architectures ◽  
Backbone Networks ◽  
Network Bandwidth ◽  
Multicast Service ◽  
Radio Access ◽  
Maximum Benefit ◽  
Wireless Medium ◽  
Cell Tower

A significant portion of today's network traffic is due to recurring downloads of a few popular contents. It has been observed that replicating the latter in caches installed at network edges—close to users—can drastically reduce network bandwidth usage and improve content access delay. Such caching architectures are gaining increasing interest in recent years as a way of dealing with the explosive traffic growth, fuelled further by the downward slope in storage space price. In this work, we provide an overview of caching with a particular emphasis on emerging network architectures that enable caching at the radio access network. In this context, novel challenges arise due to the broadcast nature of the wireless medium, which allows simultaneously serving multiple users tuned into a multicast stream, and the mobility of the users who may be frequently handed off from one cell tower to another. Existing results indicate that caching at the wireless edge has a great potential in removing bottlenecks on the wired backbone networks. Taking into consideration the schedule of multicast service and mobility profiles is crucial to extract maximum benefit in network performance.

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