Machine learning for 5G security : architecture, recent advances, and challenges
- Authors: Afaq, Amir , Haider, Noman , Baig, Muhammad , Khan, Komal , Imran, Muhammad , Razzak, Imran
- Date: 2021
- Type: Text , Journal article
- Relation: Ad Hoc Networks Vol. 123, no. (2021), p.
- Full Text: false
- Reviewed:
- Description: The granularization of crucial network functions implementation using software-centric, and virtualized approaches in 5G networks have brought forth unprecedented security challenges in general and privacy concerns. Moreover, these software components’ premature deployment and compromised supply chain put the individual network components at risk and have a ripple effect for the rest of the network. Some of the novel threats to 5G assets include tampering in identity and access management, supply-chain poisoning, masquerade and bot attacks, loop-holes in source codes. Machine learning (ML) in this context can help to provide heavily dynamic and robust security mechanisms for the software-centric architecture of 5G Networks. ML models’ development and implementation also rely on programmable environments; hence, they can play a vital role in designing, modelling, and automating efficient security protocols. This article presents the threat landscape across 5G networks and discusses the feasibility and architecture of different ML-based models to counter these threats. Also, we present the architecture for automated threat intelligence using cooperative and coordinated ML to secure 5G assets and infrastructure. We also present the summary of closely related existing works along with future research challenges. © 2021 Elsevier B.V.
A cooperative crowdsensing system based on flying and ground vehicles to control respiratory viral disease outbreaks
- Authors: Sahraoui, Yesin , Kerrache, Chaker , Amadeo, Marica , Vegni, Anna , Korichi, Ahmed , Nebhen, Jamel , Imran, Muhammad
- Date: 2022
- Type: Text , Journal article
- Relation: Ad Hoc Networks Vol. 124, no. (2022), p.
- Full Text: false
- Reviewed:
- Description: The massive increase in population density in cities has led to several urban problems, such as an increment of air pollution, traffic congestion, and a faster spread of infectious diseases. With the rapid innovation in the intelligent sensors technology, and its integration into smart vehicles and Unmanned Aerial Vehicles (UAVs), a novel sensing paradigm has been promoted, namely vehicular crowdsensing, which leverages on-board sensors to capture information from the surrounding environment. Collected data are then analyzed to take proper countermeasures. In this paper, we present a smart coordination mechanism between UAVs and ground vehicles (GVs), which sense information like body temperature and breathing rate of people, in order to support a variety of monitoring applications, including discovering the presence of infectious diseases. In our framework, namely GUAVA, aerial and ground vehicles are equipped with GPS devices and thermal cameras to monitor specific geographic areas, detect humans’ vital parameters and, at the same time, discover duplicate data by identifying matching faces in thermal video sequences with the GaussianFace algorithm. The sensing tasks in hard-to-reach places are assigned to UAVs, with the ability to power up wirelessly from the nearest GV and offload the collected monitoring images to it. Simulation results have assessed our proposed framework, showing good performance in terms of distinct Quality of Service (QoS) metrics. © 2021