While existing security protocols were designed with a focus on the core network, the enhancement of the security of the B5G access network becomes of critical importance. Despite the strengthening of 5G security protocols with respect to LTE, there are still open issues that have not been fully addressed. This work is articulated around the premise that rethinking the security design bottom up, starting at the physical layer, is not only viable in 6G but importantly, arises as an efficient way to overcome security hurdles in novel use cases, notably massive machine type communications (mMTC), ultra reliable low latency communications (URLLC) and autonomous cyberphysical systems. Unlike existing review papers that treat physical layer security orthogonally to cryptography, we will try to provide a few insights of underlying connections. Discussing many practical issues, we will present a comprehensive review of the state-of-the-art in i) secret key generation from shared randomness, ii) the wiretap channel and fundamental limits, iii) authentication of devices using physical unclonable functions (PUFs), localization and multi-factor authentication, and, iv) jamming attacks at the physical layer. We finally conclude with the proposers' aspirations for the 6G security landscape, in the hyper-connectivity and semantic communications era.
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The sixth generation (6G) of wireless technology is seen as one of the enablers of real-time fusion of the physical and digital realms, as in the Metaverse, extended reality (XR), or Digital Twin (DT). This would allow people to interact, work, and entertain themselves in immersive online 3D virtual environments. From the viewpoint of communication and networking, this will represent an evolution of the game networking technology, designed to interconnect massive users in real-time online gaming environments. This article presents the basic principles of game networking and discusses their evolution towards meeting the requirements of the Metaverse and similar applications. Several open research challenges are provided, along with possible solutions.
This paper explores the use of reconfigurable intelligent surfaces (RIS) in mitigating cross-system interference in spectrum sharing and secure wireless applications. Unlike conventional RIS that can only adjust the phase of the incoming signal and essentially reflect all impinging energy, or active RIS, which also amplify the reflected signal at the cost of significantly higher complexity, noise, and power consumption, an absorptive RIS (ARIS) is considered. An ARIS can in principle modify both the phase and modulus of the impinging signal by absorbing a portion of the signal energy, providing a compromise between its conventional and active counterparts in terms of complexity, power consumption, and degrees of freedom (DoFs). We first use a toy example to illustrate the benefit of ARIS, and then we consider three applications: (1) Spectral coexistence of radar and communication systems, where a convex optimization problem is formulated to minimize the Frobenius norm of the channel matrix from the communication base station to the radar receiver; (2) Spectrum sharing in device-to-device (D2D) communications, where a max-min scheme that maximizes the worst-case signal-to-interference-plus-noise ratio (SINR) among the D2D links is developed and then solved via fractional programming; (3) The physical layer security of a downlink communication system, where the secrecy rate is maximized and the resulting nonconvex problem is solved by a fractional programming algorithm together with a sequential convex relaxation procedure. Numerical results are then presented to show the significant benefit of ARIS in these applications.
Network anomaly detection is a very relevant research area nowadays, especially due to its multiple applications in the field of network security. The boost of new models based on variational autoencoders and generative adversarial networks has motivated a reevaluation of traditional techniques for anomaly detection. It is, however, essential to be able to understand these new models from the perspective of the experience attained from years of evaluating network security data for anomaly detection. In this paper, we revisit anomaly detection techniques based on PCA from a probabilistic generative model point of view, and contribute a mathematical model that relates them. Specifically, we start with the probabilistic PCA model and explain its connection to the Multivariate Statistical Network Monitoring (MSNM) framework. MSNM was recently successfully proposed as a means of incorporating industrial process anomaly detection experience into the field of networking. We have evaluated the mathematical model using two different datasets. The first, a synthetic dataset created to better understand the analysis proposed, and the second, UGR'16, is a specifically designed real-traffic dataset for network security anomaly detection. We have drawn conclusions that we consider to be useful when applying generative models to network security detection.
Single Sign-On (SSO) shifts the crucial authentication process on a website to to the underlying SSO protocols and their correct implementation. To strengthen SSO security, organizations, such as IETF and W3C, maintain advisories to address known threats. One could assume that these security best practices are widely deployed on websites. We show that this assumption is a fallacy. We present SSO-MONITOR, an open-source fully-automatic large-scale SSO landscape, security, and privacy analysis tool. In contrast to all previous work, SSO-MONITOR uses a highly extensible, fully automated workflow with novel visual-based SSO detection techniques, enhanced security and privacy analyses, and continuously updated monitoring results. It receives a list of domains as input to discover the login pages, recognize the supported Identity Providers (IdPs), and execute the SSO. It further reveals the current security level of SSO in the wild compared to the security best practices on paper. With SSO-MONITOR, we automatically identified 1,632 websites with 3,020 Apple, Facebook, or Google logins within the Tranco 10k. Our continuous monitoring also revealed how quickly these numbers change over time. SSO-MONITOR can automatically login to each SSO website. It records the logins by tracing HTTP and in-browser communication to detect widespread security and privacy issues automatically. We introduce a novel deep-level inspection of HTTP parameters that we call SMARTPARMS. Using SMARTPARMS for security analyses, we uncovered URL parameters in 5 Client Application (Client) secret leakages and 337 cases with weak CSRF protection. We additionally identified 447 cases with no CSRF protection, 342 insecure SSO flows and 9 cases with nested URL parameters, leading to an open redirect in one case. SSO-MONITOR reveals privacy leakages that deanonymize users in 200 cases.
In this paper, the wireless hierarchical federated learning (HFL) is revisited by considering physical layer security (PLS). First, we establish a framework for this new problem. Then, we propose a practical finite blocklength (FBL) coding scheme for the wireless HFL in the presence of PLS, which is self-secure when the coding blocklength is lager than a certain threshold. Finally, the study of this paper is further explained via numerical examples and simulation results.
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for significant changes in computational engineering applications. Here, we investigate the use of DeepONets to infer flow fields around unseen airfoils with the aim of shape optimization, an important design problem in aerodynamics that typically taxes computational resources heavily. We present results which display little to no degradation in prediction accuracy, while reducing the online optimization cost by orders of magnitude. We consider NACA airfoils as a test case for our proposed approach, as their shape can be easily defined by the four-digit parametrization. We successfully optimize the constrained NACA four-digit problem with respect to maximizing the lift-to-drag ratio and validate all results by comparing them to a high-order CFD solver. We find that DeepONets have low generalization error, making them ideal for generating solutions of unseen shapes. Specifically, pressure, density, and velocity fields are accurately inferred at a fraction of a second, hence enabling the use of general objective functions beyond the maximization of the lift-to-drag ratio considered in the current work.
Urban traffic attributed to commercial and industrial transportation is observed to largely affect living standards in cities due to external effects pertaining to pollution and congestion. In order to counter this, smart cities deploy technological tools to achieve sustainability. Such tools include Digital Twins (DT)s which are virtual replicas of real-life physical systems. Research suggests that DTs can be very beneficial in how they control a physical system by constantly optimizing its performance. The concept has been extensively studied in other technology-driven industries like manufacturing. However, little work has been done with regards to their application in urban logistics. In this paper, we seek to provide a framework by which DTs could be easily adapted to urban logistics networks. To do this, we provide a characterization of key factors in urban logistics for dynamic decision-making. We also survey previous research on DT applications in urban logistics as we found that a holistic overview is lacking. Using this knowledge in combination with the characterization, we produce a conceptual model that describes the ontology, learning capabilities and optimization prowess of an urban logistics digital twin through its quantitative models. We finish off with a discussion on potential research benefits and limitations based on previous research and our practical experience.
With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems are envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are not just immensely popular in the service layer applications but also have been proposed as essential enablers in many aspects of B5G networks, from IoT devices and edge computing to cloud-based infrastructures. However, most of the existing surveys in B5G security focus on the performance of AI/ML models and their accuracy, but they often overlook the accountability and trustworthiness of the models' decisions. Explainable AI (XAI) methods are promising techniques that would allow system developers to identify the internal workings of AI/ML black-box models. The goal of using XAI in the security domain of B5G is to allow the decision-making processes of the security of systems to be transparent and comprehensible to stakeholders making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as RAN, zero-touch network management, E2E slicing, this survey emphasizes the role of XAI in them and the use cases that the general users would ultimately enjoy. Furthermore, we presented the lessons learned from recent efforts and future research directions on top of the currently conducted projects involving XAI.
Command, Control, Communication, and Intelligence (C3I) system is a kind of system-of-system that integrates computing machines, sensors, and communication networks. C3I systems are increasingly used in critical civil and military operations for achieving information superiority, assurance, and operational efficacy. C3I systems are no exception to the traditional systems facing widespread cyber-threats. However, the sensitive nature of the application domain (e.g., military operations) of C3I systems makes their security a critical concern. For instance, a cyber-attack on military installations can have detrimental impacts on national security. Therefore, in this paper, we review the state-of-the-art on the security of C3I systems. In particular, this paper aims to identify the security vulnerabilities, attack vectors, and countermeasures for C3I systems. We used the well-known systematic literature review method to select and review 77 studies on the security of C3I systems. Our review enabled us to identify 27 vulnerabilities, 22 attack vectors, and 62 countermeasures for C3I systems. This review has also revealed several areas for future research and identified key lessons with regards to C3I systems' security.
Recommender systems play a fundamental role in web applications in filtering massive information and matching user interests. While many efforts have been devoted to developing more effective models in various scenarios, the exploration on the explainability of recommender systems is running behind. Explanations could help improve user experience and discover system defects. In this paper, after formally introducing the elements that are related to model explainability, we propose a novel explainable recommendation model through improving the transparency of the representation learning process. Specifically, to overcome the representation entangling problem in traditional models, we revise traditional graph convolution to discriminate information from different layers. Also, each representation vector is factorized into several segments, where each segment relates to one semantic aspect in data. Different from previous work, in our model, factor discovery and representation learning are simultaneously conducted, and we are able to handle extra attribute information and knowledge. In this way, the proposed model can learn interpretable and meaningful representations for users and items. Unlike traditional methods that need to make a trade-off between explainability and effectiveness, the performance of our proposed explainable model is not negatively affected after considering explainability. Finally, comprehensive experiments are conducted to validate the performance of our model as well as explanation faithfulness.
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