Biblio
Unmanned Aerial Vehicles (UAVs) are drawing enormous attention in both commercial and military applications to facilitate dynamic wireless communications and deliver seamless connectivity due to their flexible deployment, inherent line-of-sight (LOS) air-to-ground (A2G) channels, and high mobility. These advantages, however, render UAV-enabled wireless communication systems susceptible to eavesdropping attempts. Hence, there is a strong need to protect the wireless channel through which most of the UAV-enabled applications share data with each other. There exist various error correction techniques such as Low Density Parity Check (LDPC), polar codes that provide safe and reliable data transmission by exploiting the physical layer but require high transmission power. Also, the security gap achieved by these error-correction techniques must be reduced to improve the security level. In this paper, we present deep learning (DL) enabled punctured LDPC codes to provide secure and reliable transmission of data for UAVs through the Additive White Gaussian Noise (AWGN) channel irrespective of the computational power and channel state information (CSI) of the Eavesdropper. Numerical result analysis shows that the proposed scheme reduces the Bit Error Rate (BER) at Bob effectively as compared to Eve and the Signal to Noise Ratio (SNR) per bit value of 3.5 dB is achieved at the maximum threshold value of BER. Also, the security gap is reduced by 47.22 % as compared to conventional LDPC codes.
Cyber ranges are proven to be effective towards the direction of cyber security training. Nevertheless, the existing literature in the area of cyber ranges does not cover, to our best knowledge, the field of 5G security training. 5G networks, though, reprise a significant field for modern cyber security, introducing a novel threat landscape. In parallel, the demand for skilled cyber security specialists is high and still rising. Therefore, it is of utmost importance to provide all means to experts aiming to increase their preparedness level in the case of an unwanted event. The EU funded SPIDER project proposes an innovative Cyber Range as a Service (CRaaS) platform for 5G cyber security testing and training. This paper aims to present the evaluation framework, followed by SPIDER, for the extraction of the user requirements. To validate the defined user requirements, SPIDER leveraged of questionnaires which included both closed and open format questions and were circulated among the personnel of telecommunication providers, vendors, security service providers, managers, engineers, cyber security personnel and researchers. Here, we demonstrate a selected set of the most critical questions and responses received. From the conducted analysis we reach to some important conclusions regarding 5G testing and training capabilities that should be offered by a cyber range, in addition to the analysis of the different perceptions between cyber security and 5G experts.
The advantages of measuring multiple wireless links simultaneously has been gaining attention due to the growing complexity of wireless communication systems. Analyzing vehicular communication systems presents a particular challenge due to their rapid time-varying nature. Therefore multi-node channel sounding is crucial for such endeavors. In this paper, we present the architecture and practical implementation of a scalable mobile multi-node channel sounder, optimized for use in vehicular scenarios. We perform a measurement campaign with three moving nodes, which includes a line of sight (LoS) connection on two links and non LoS(NLoS) conditions on the third link. We present the results on the obtained channel delay and Doppler characteristics, followed by the assessment of the degree of correlation of the analyzed channels and time-variant channel rates, hence investigating the suitability of the channel's physical attributes for relaying. The results show low cross-correlation between the transfer functions of the direct and the relaying link, while a higher rate is calculated for the relaying link.
This paper describes a novel distributed mobility management (DMM) scheme for the "named-object" information centric network (ICN) architecture in which the routers forward data based on unique identifiers which are dynamically mapped to the current network addresses of a device. The work proposes and evaluates two specific handover schemes namely, hard handoff with rebinding and soft handoff with multihoming intended to provide seamless data transfer with improved throughput during handovers. The evaluation of the proposed handover schemes using system simulation along with proof-of-concept implementation in ORBIT testbed is described. The proposed handoff and scheduling throughput gains are 12.5% and 44% respectively over multiple interfaces when compared to traditional IP network with equal share split scheme. The handover performance with respect to RTT and throughput demonstrate the benefits of clean slate network architecture for beyond 5G networks.