With the continuous development of mobile communication technologies, Third Generation Partnership Project (3GPP) has proposed related standards with the fifth generation mobile communication technology (5G), which marks the official start of the evolution from the current Long Term Evolution (LTE) system to the next generation mobile communication system (5GS). This paper makes a large number of contributions to the security aspects of 3GPP 5G networks. Firstly, we present an overview of the network architecture and security functionality of the 3GPP 5G networks. Subsequently, we focus on the new features and techniques including the support of massive Internet of Things (IoT) devices, Device to Device (D2D) communication, Vehicle to Everything (V2X) communication, and network slice, which incur the huge challenges for the security aspects in 3GPP 5G networks. Finally, we discuss in detail the security features, security requirements or security vulnerabilities, existing security solutions and some open research issues about the new features and techniques in 3GPP 5G network.

Machine learning (ML) technology has shown its unique advantages in many fields and has excellent performance in many applications, such as image recognition, speech recognition, recommendation systems, and natural language processing. Recently, the applicability of ML in wireless sensor networks (WSNs) has attracted much attention. As resources are limited in WSNs, identifying how to improve resource utilization and achieve power-efficient load balancing is becoming a critical issue in WSNs. Traditional green routing algorithms aim to achieve this by reducing energy consumption and prolonging network lifetime through optimized routing schemes in WSNs. However, there are usually problems such as poor flexibility, a single consideration factor, and a reliance on accurate mathematical models. ML techniques can quickly adapt to environmental changes and integrate multiple factors for routing decisions, which provides new ideas for intelligent energy-efficient routing algorithms in WSNs. In this paper, we survey and propose a theoretical hypothetic model formulation of ML as an effective method for creating a power-efficient green routing model that can overcome the limitations of traditional green routing methods. In addition, the study also provides an overview of past, present, and future progress in green routing schemes in WSNs. The contents of this paper will appeal to a wide range of audiences interested in ML-based WSNs.

It is essential for Vehicular Ad-hoc networks (VANETs) to have reliable vehicles for communication with vehicles. VANET is dynamical network where the vehicles frequently alter their place. Safe routing is of great essence at the time of routing process to fit in shared trust/belief involving these nodes. Occasionally, the malicious node transmits the counterfeit data amid other nodes. To find out trust/belief is deemed to be a difficult task when the malicious nodes try to distort the discovery of route or transmission of data within the network. Researchers have worked extensively to ensure a safe routing process with trust-oriented applications. We develop the framework based on trust with a fresh mechanism to determine DDoS attacks in VANET. The major trust elements in the evaluation of trust are frequency value statistics, trust hypothesis statistics, residual energy, trust policy, and data factor. Based on the trust elements, the generation of trust evaluation matrix takes place. We develop the suggested trust mechanism in an innovative manner to offer the security in a better manner by avoiding the trespassers in the network. The deterrence design by trust evaluation mechanism in combination with a clustering method is proficiently made use for the identification of the attacker and reduction of the price concerning detection method. The suggested system optimizes the utilization of a bandwidth without compromising the security of the nodes in the network.

The emergence of Narrowband Internet of Things (NB-IoT) has brought hope for the popularization and application of mobile Internet standards. Nowadays, NB-IoT technology has been introduced into the 3GPP standards, where low-overhead, low-data-transmission IoT devices can securely access the fifth Generation (5G) core network through the 3GPP access network. However, according to the current 3GPP standard, these NB-IoT devices still employ the traditional authentication process of normal User Equipment (UE) to implement mutual authentication between NB-IoT devices and 5G core networks, which brings a lot of communication and storage overhead, and it will be more serious when lots of NB-IoT devices are activated simultaneously. In this paper, we propose a fast mutual authentication and data transfer scheme for massive NB-IoT devices,which integrates the access authentication and secure data transmission process and achieves the authentications and data transmissions of a group of NB-IoT devices at the same time. Our scheme can not only greatly simplify the authentication process and alleviate the load of the networks, but also ensure robust security protection including user anonymity and non-repudiation. The performance analysis results show that the proposed scheme can withstand a variety of security attacks with ideal efficiency.

Gait recognition enables many potential applications requiring identification. Wi-Fi based gait recognition is predominant because of its non-invasive and ubiquitous advantages. However, since the gait information changes with the walking direction, the existing Wi-Fi based gait recognition systems require the subject to walk along a predetermined path. This direction dependence restriction impedes Wi-Fi based gait recognition from being widely used. In order to address this issue, a direction-independent gait recognition system, called WiDIGR is proposed. WiDIGR can recognize a subject through the gait no matter what straight line walking path it is. This relaxes the strict constraint of the other Wi-Fi based gait recognition. Specifically, based on the Fresnel model, a series of signal processing techniques are proposed to eliminate the differences among induced signals caused by walking in different directions and generate a high quality direction-independent signal spectrogram. Furthermore, effective features are extracted both manually and automatically from the direction-independent spectrogram. The experiment results in typical indoor environment demonstrate the superior performance of WiDIGR, with mean accuracy ranging from 78.28% for a group of six subjects to 92.83% for a group of three.