Biblio

The evolution of smart automobiles and vehicles within the Internet of Things (IoT) - particularly as that evolution leads toward a proliferation of completely autonomous vehicles - has sparked considerable interest in the subject of vehicle/automotive security. While the attack surface is wide, there are patterns of exploitable vulnerabilities. In this study we reviewed, classified according to their attack surface and evaluated some of the common vehicle and infrastructure attack vectors identified in the literature. To remediate these attack vectors, specific technical recommendations have been provided as a way towards secure deployments of smart automobiles and transportation infrastructures.

The fifth-generation and beyond mobile networks should support extremely high and diversified requirements from a wide variety of emerging applications. It is envisioned that more advanced radio transmission, resource allocation, and networking techniques are required to be developed. Fulfilling these tasks is challenging since network infrastructure becomes increasingly complicated and heterogeneous. One promising solution is to leverage the great potential of Artificial Intelligence (AI) technology, which has been explored to provide solutions ranging from channel prediction to autonomous network management, as well as network security. As of today, however, the state of the art of integrating AI into wireless networks is mainly limited to use a dedicated AI algorithm to tackle a specific problem. A unified framework that can make full use of AI capability to solve a wide variety of network problems is still an open issue. Hence, this paper will present the concept of intelligence slicing where an AI module is instantiated and deployed on demand. Intelligence slices are applied to conduct different intelligent tasks with the flexibility of accommodating arbitrary AI algorithms. Two example slices, i.e., neural network based channel prediction and anomaly detection based industrial network security, are illustrated to demonstrate this framework.

Children and teenagers that have been a victim of bullying can possibly suffer its psychological effects for a lifetime. With the increase of online social media, cyberbullying incidents have been increased as well. In this paper we discuss how we can detect cyberbullying with AI techniques, using term frequency-inverse document frequency. We label messages as benign or bully. We want our method of cyberbullying detection to be privacy-preserving, such that the subscribers' benign messages should not be revealed to the operator. Moreover, the operator labels subscribers as normal, bully and victim. The operator utilizes policy control in 5G networks, to protect victims of cyberbullying from harmful traffic.

Artificial Intelligence (AI) can and does use individual's data to make predictions about their wants, their needs, their influences on them and predict what they could do. The use of individual's data naturally raises privacy concerns. This article focuses on AI, the privacy issue against the backdrop of the endless growth of the Digital Universe where Big Data, AI, Data Analytics and 5G Technology live and grow in The Internet of Things (IoT).

Internet of Things (IoT) revolution in 5th Generation (5G) will dynamically support all user, devices and customer worldwide where these devices, mechanical and digital machines will be connected and are able to communicate and transfer data over the network. In industries, the evolution of these technologies, known as Industrial IoT (IIoT) will enable machines to be connected and communicate where else, Internet of Everything (IoE) makes the connection more relevant between all smart devices, machines and also people with a huge data, high speed and high security. The growth of these technologies has made Radio Frequency (RF) spectrum resources for wireless communication to be more saturated. In order to solve this problem, new wireless communication technologies are proposed to meet the demand and also to enhance the performance of the system and overcome the existing bandwidth limitations. Studies done shows that Light-Fidelity (Li-Fi), based on Visible Light Communications (VLC) is one of the most promising technology in future which is based on optical wireless communication. Initial study on the Li-Fi concept has focuses on achieving speed, bi-directional transmission concept and supports multiuser access. In this paper we propose a frame work focuses on the handover process for indoor environment by using the steerable Access Point (AP) and compare the output result with fix Access Point.

The fifth generation of mobile technology (5G) is positioned to address the demands and business contexts of 2020 and beyond. Therefore, in 5G, there is a need to push the envelope of performance to provide, where needed, for example, much greater throughput, much lower latency, ultra-high reliability, much higher connectivity density, and higher mobility range. A crucial point in the effective provisioning of 5G Next Generation Mobile Networks (NGMN) lies in the efficient error control and in more details in the utilization of Forward Error Correction (FEC) codes on the application layer. FEC is a method for error control of data transmission adopted in several mobile multicast standards. FEC is a feedback free error recovery method where the sender introduces redundant data in advance with the source data enabling the recipient to recover from different arbitrary packet losses. Recently, the adoption of FEC error control method has been boosted by the introduction of powerful Application Layer FEC (AL-FEC) codes. Furthermore, several works have emerged aiming to address the efficient application of AL-FEC protection introducing deterministic or randomized online algorithms. In this work we propose a novel AL-FEC scheme based on online algorithms forced by the well stated AL-FEC policy online problem. We present an algorithm which exploits feedback capabilities of the mobile users regarding the outcome of a transmission, and adapts the introduced protection respectively. Moreover, we provide an extensive analysis of the proposed AL-FEC algorithm accompanied by a performance evaluation against common error protection schemes.

In the wake of diversity of service requirements and increasing push for extreme efficiency, adaptability propelled by machine learning (ML) a.k.a self organizing networks (SON) is emerging as an inevitable design feature for future mobile 5G networks. The implementation of SON with ML as a foundation requires significant amounts of real labeled sample data for the networks to train on, with high correlation between the amount of sample data and the effectiveness of the SON algorithm. As generally real labeled data is scarce therefore it can become bottleneck for ML empowered SON for unleashing their true potential. In this work, we propose a method of expanding these sample data sets using Generative Adversarial Networks (GANs), which are based on two interconnected deep artificial neural networks. This method is an alternative to taking more data to expand the sample set, preferred in cases where taking more data is not simple, feasible, or efficient. We demonstrate how the method can generate large amounts of realistic synthetic data, utilizing the GAN's ability of generation and discrimination, able to be easily added to the sample set. This method is, as an example, implemented with Call Data Records (CDRs) containing the start hour of a call and the duration of the call, in minutes taken from a real mobile operator. Results show that the method can be used with a relatively small sample set and little information about the statistics of the true CDRs and still make accurate synthetic ones.

5G mobile networks promise universal communication environment and aims at providing higher bandwidth, increased communication and networking capabilities, and extensive signal coverage by using multiple communication technologies including Device-to-Device (D-to-D). This paradigm, will allow scalable and ubiquitous connectivity for large-scale mobile networks where a huge number of heterogeneous devices with limited resources will cooperate to enhance communication efficiency in terms of link reliability, spectral efficiency, system capacity, and transmission range. However, owing to its decentralized nature, cooperative D-to-D communication could be vulnerable to attacks initiated on relay nodes. Consequently, a source node has the interest to select the more protected relay to ensure the security of its traffic. Nevertheless, an improvement in the protection level has a counterpart cost that must be sustained by the device. To address this trade-off as well as the interaction between the attacker and the source device, we propose a dynamic game theoretic based approach to model and analyze this problem as a cost model. The utility function of the proposed non-cooperative game is based on the concepts of return on protection and return on attack which illustrate the gain of selecting a relay for transmitting a data packet by a source node and the reward of the attacker to perform an attack to compromise the transmitted data. Moreover, we discuss and analyze Nash equilibrium convergence of this attack-defense model and we propose an heuristic algorithm that can determine the equilibrium state in a limited number of running stages. Finally, we perform simulation work to show the effectiveness of the game model in assessing the behavior of the source node and the attacker and its ability to reach equilibrium within a finite number of steps.

The EU SoftFIRE project has built an experimentation platform for NFV and SDN experiments, tailored for testing and evaluating 5G network applications and solutions. The platform is a fully orchestrated virtualisation testbed consisting of multiple component testbeds across Europe. Users of the platform can deploy their virtualisation experiments via the platform's Middleware. This paper introduces the SoftFIRE testbed and its Middleware, and presents a set of KPI results for evaluation of experiment deployment performance.

The concept of network slicing in 5G mobile networks introduces new challenges for security management: Given the combination of Infrastructure-as-a-Service cloud providers, mobile network operators as Software-as-a-Service providers, and the various verticals as customers, multi-layer and multi-tenancy-capable management architectures are required. This paper addresses the challenges for correlation of security events in such 5G scenarios with a focus on event processing at telecommunication service providers. After an analysis of the specific demand for network-slice-centric security event correlation in 5G networks, ongoing standardization efforts, and related research, we propose a multi-tenancy-capable event correlation architecture along with a scalable information model. The event processing, alerting, and correlation workflow is discussed and has been implemented in a network and security management system prototype, leading to a demonstration of first results acquired in a lab setup.

Serving heterogeneous traffic demand requires efficient resource utilization to deliver the promises of 5G wireless network towards enhanced mobile broadband, massive machine type communication and ultra-reliable low-latency communication. In this paper, an integrated user application-specific demand characteristics as well as network characteristics evaluation based online slice allocation model for 5G wireless network is proposed. Such characteristics include, available bandwidth, power, quality of service demand, service priority, security sensitivity, network load, predictive load etc. A degree of intra-slice resource sharing elasticity has been considered based on their availability. The availability has been assessed based on the current availability as well as forecasted availability. On the basis of application characteristics, an admission control strategy has been proposed. An interactive AMF (Access and Mobility Function)- RAN (Radio Access Network) information exchange has been assumed. A cost function has been derived to quantify resource allocation decision metric that is valid for both static and dynamic nature of user and network characteristics. A dynamic intra-slice decision boundary estimation model has been proposed. A set of analytical comparative results have been attained in comparison to the results available in the literature. The results suggest the proposed resource allocation framework performance is superior to the existing results in the context of network utility, mean delay and network grade of service, while providing similar throughput. The superiority reported is due to soft nature of the decision metric while reconfiguring slice resource block-size and boundaries.

QoS-based connectivity coordinated by the 5GEx Multi-domain Orchestrator exploiting novel stateful BRPC is demonstrated for the first time over a multi-operator multi-technology transport network within the European 5GEx Sandbox, including Segment Routing and optical domains.

To address cybersecurity, this author proposed recently the approach of formulating it in symmetric dual-space and dual-system. This paper further explains this concept, beginning with symmetric Maxwell Equation (ME) and Fourier Transform (FT). The approach appears to be a powerful solution, with wide applications ranging from Electronic Warfare (EW) to 5G Mobile, etc.

Upon the new paradigm of Cellular Internet of Things, through the usage of technologies such as Narrowband IoT (NB-IoT), a massive amount of IoT devices will be able to use the mobile network infrastructure to perform their communications. However, it would be beneficial for these devices to use the same security mechanisms that are present in the cellular network architecture, so that their connections to the application layer could see an increase on security. As a way to approach this, an identity management and provisioning mechanism, as well as an identity federation between an IoT platform and the cellular network is proposed as a way to make an IoT device deemed worthy of using the cellular network and perform its actions.

Internet of Things (IoT) and 5G are emerging technologies that prompt a mobile service platform capable of provisioning billions of communication devices which enable ubiquitous computing and ambient intelligence. These novel approaches are guaranteeing gigabit-level bandwidth, ultra-low latency and ultra-high storage capacity for their subscribers. To achieve these limitations, ETSI has introduced the paradigm of Multi-Access Edge Computing (MEC) for creating efficient data processing architecture extending the cloud computing capabilities in the Radio Access Network (RAN). Despite the gained enhancements to the mobile network, MEC is subjected to security challenges raised from the heterogeneity of IoT services, intricacies in integrating virtualization technologies, and maintaining the performance guarantees of the mobile networks (i.e. 5G). In this paper, we are identifying the probable threat vectors in a typical MEC deployment scenario that comply with the ETSI standards. We analyse the identified threat vectors and propose solutions to mitigate them.

Caching methods are developed since 50 years for paging in CPU and database systems, and since 25 years for web caching as main application areas among others. Pages of unique size are usual in CPU caches, whereas web caches are storing data chunks of different size in a widely varying range. We study the impact of different object sizes on the performance and the overhead of web caching. This entails different caching goals, starting from the byte and object hit ratio to a generalized value hit ratio for optimized costs and benefits of caching regarding traffic engineering (TE), reduced delays and other QoS measures. The selection of the cache contents turns out to be crucial for the web cache efficiency with awareness of the size and other properties in a score for each object. We introduce a new class of rank exchange caching methods and show how their performance compares to other strategies with extensions needed to include the size and scores for QoS and TE caching goals. Finally, we derive bounds on the object, byte and value hit ratio for the independent request model (IRM) based on optimum knapsack solutions of the cache content.

Fractional hedonic games (FHGs) are extensively studied in game theory and explain the formation of coalitions among individuals in a group. This paper investigates the coalition generation problem, namely, finding a coalition structure whose social welfare, i.e., the sum of the players' payoffs, is maximized. We focus on agent-based methods which set the decision rules for each player in the game. Through repeated interactions the players arrive at a coalition structure. In particular, we propose CFSV, namely, coalition formation with Shapley value-based welfare distribution scheme. To evaluate CFSV, we theoretically demonstrate that this algorithm achieves optimal coalition structure over certain standard graph classes and empirically compare the algorithm against other existing benchmarks on real-world and synthetic graphs. The results show that CFSV is able to achieve superior performance.

The future fifth-generation (5G) mobile communications system has already become a focus around the world. A large number of late-model services and applications including high definition visual communication, internet of vehicles, multimedia interaction, mobile industry automation, and etc, will be added to 5G network platform in the future. Different application services have different security requirements. However, the current user authentication for services and applications: Extensible Authentication Protocol (EAP) suggested by the 3GPP committee, is only a unitary authentication model, which is unable to meet the diversified security requirements of differentiated services. In this paper, we present a new diversified identity management as well as a flexible and composable three-factor authentication mechanism for different applications in 5G multi-service systems. The proposed scheme can provide four identity authentication methods for different security levels by easily splitting or assembling the proposed three-factor authentication mechanism. Without a design of several different authentication protocols, our proposed scheme can improve the efficiency, service of quality and reduce the complexity of the entire 5G multi-service system. Performance analysis results show that our proposed scheme can ensure the security with ideal efficiency.

Emerging next generation wireless communication devices call for high-performance filters that operate at 3-10 GHz frequency range and offer low loss, small form factor, wide bandwidth and steep skirts. Bulk and surface acoustic wave devices have been long used in the RF front-end for filtering applications, however their operation frequencies are mostly below 2.6 GHz band. To scale up the frequency of the filters, the thickness of the piezoelectric material needs to be reduced to sub-micron ranges. One of the challenges of such scaling is maintaining high electromechanical coupling as the film thickness decreases, which in turn, determines the filter bandwidth.Aluminum Nitride (AlN) - popular in today's film bulk acoustic resonators (FBARs) and mostly deposited using sputtering techniques-shows degraded crystal quality and poor electromechanical coupling when the thickness of AlN film is smaller than 1 μm.In this work, we propose using high-quality single-crystalline AlN and Scandium (Sc)-doped AlN epi-layers grown on Si substrates, wherein high crystal quality is maintained for ultra-thin films of only 400 nm thickness. Experimental results verify improved kt2 for 3-10 GHz resonators, with quality factors of the order of 250 and kt2 values of up to 5%based on bulk acoustic wave resonators. The experimental results suggest that single-crystal Sc-AlN is a great material candidate for 5G resonators and filters.

The convergence of access networks in the fifth-generation (5G) evolution promises multi-tier networking infrastructures for the successes of various applications realizing the Internet-of-Everything era. However, in this context, the support of a massive number of connected devices also opens great opportunities for attackers to exploit these devices in illegal actions against their victims, especially within the distributed denial-of-services (DDoS) attacks. Nowadays, DDoS prevention still remains an open issue in term of performance improvement although there is a significant number of existing solutions have been proposed in the literature. In this paper, we investigate the advances of multi-access edge computing (MAEC), which is considered as one of the most important emerging technologies in 5G networks, in order to provide an effective DDoS prevention solution (referred to be MAEC-X). The proposed MAEC-X architecture and mechanism are developed as well as proved its effectiveness against DDoS attacks through intensive security analysis.

In this paper we look into 5G requirements for channel coding and review candidate channel coding schemes for 5G. A comparative study is presented for possible channel coding candidates of 5G covering Convolutional, Turbo, Low Density Parity Check (LDPC), and Polar codes. It seems that polar code with Successive Cancellation List (SCL) decoding using small list length (such as 8) is a promising choice for short message lengths (≤128 bits) due to its error performance and relatively low complexity. Also adopting non-binary LDPC can provide good performance on the expense of increased complexity but with better spectral efficiency. Considering the implementation, polar code with decoding algorithms based on SCL required small area and low power consumption when compared to LDPC codes. For larger message lengths (≥256 bits) turbo code can provide better performance at low coding rates (\textbackslashtextless;1/2).

In the Self-Organizing Networks (SON) concept, self-healing functions are used to detect, diagnose and correct degraded states in the managed network functions or other resources. Such methods are increasingly important in future network deployments, since ultra-high reliability is one of the key requirements for the future 5G mobile networks, e.g. in critical machine-type communication. In this paper, we discuss the considerations for improving the resiliency of future cognitive autonomous mobile networks. In particular, we present an automated anomaly detection and diagnosis function for SON self-healing based on multi-dimensional statistical methods, case-based reasoning and active learning techniques. Insights from both the human expert and sophisticated machine learning methods are combined in an iterative way. Additionally, we present how a more holistic view on mobile network self-healing can improve its performance.

The fifth generation of cellular networks (5G) will enable different use cases where security will be more critical than ever before (e.g. autonomous vehicles and critical IoT devices). Unfortunately, the new networks are being built on the certainty that security problems cannot be solved in the short term. Far from reinventing the wheel, one of our goals is to allow security software developers to implement and test their reactive solutions for the capillary network of 5G devices. Therefore, in this paper a solution for analysing proximity-based attacks in 5G environments is modelled and tested using OMNET++. The solution, named CRAT, is able to decouple the security analysis from the hardware of the device with the aim to extend the analysis of proximity-based attacks to different use-cases in 5G. We follow a high-level approach, in which the devices can take the role of victim, offender and guardian following the principles of the routine activity theory.

In order to cater the growing spectrum demands of large scale future 5G Internet of Things (IoT) applications, Dynamic Spectrum Access (DSA) based networks are being proposed as a high-throughput and cost-effective solution. However the lack of understanding of DSA paradigm's inherent security vulnerabilities on IoT networks might become a roadblock towards realizing such spectrum aware 5G vision. In this paper, we make an attempt to understand how such inherent DSA vulnerabilities in particular Spectrum Sensing Data Falsification (SSDF) attacks can be exploited by collaborative group of selfish adversaries and how that can impact the performance of spectrum aware IoT applications. We design a utility based selfish adversarial model mimicking collaborative SSDF attack in a cooperative spectrum sensing scenario where IoT networks use dedicated environmental sensing capability (ESC) for spectrum availability estimation. We model the interactions between the IoT system and collaborative selfish adversaries using a leader-follower game and investigate the existence of equilibrium. Using simulation results, we show the nature of adversarial and system utility components against system variables. We also explore Pareto-optimal adversarial strategy design that maximizes the attacker utility for varied system strategy spaces.

Ultra-dense Networks are attracting significant interest due to their ability to provide the next generation 5G cellular networks with a high data rate, low delay, and seamless coverage. Several factors, such as interferences, energy constraints, and backhaul bottlenecks may limit wireless networks densification. In this paper, we study the effect of mobile node densification, access node densification, and their aggregation into virtual entities, referred to as virtual cells, on location privacy. Simulations show that the number of tracked mobile nodes might be statistically reduced up to 10 percent by implementing virtual cells. Moreover, experiments highlight that success of tracking attacks has an inverse relationship to the number of moving nodes. The present paper is a preliminary attempt to analyse the effectiveness of cell virtualization to mitigate location privacy threats in ultra-dense networks.