Browsing by Author "Benzaid, Chafika"
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Item AI-driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-02) Benzaid, Chafika; Taleb, Tarik; Department of Communications and Networking; Mobile Network Softwarization and Service CustomizationThe foreseen complexity in operating and managing 5G and beyond networks has propelled the trend toward closed-loop automation of network and service management operations. To this end, the ETSI Zero-touch network and Service Management (ZSM) framework is envisaged as a next-generation management system that aims to have all operational processes and tasks executed automatically, ideally with 100 percent automation. Artificial Intelligence (AI) is envisioned as a key enabler of self-managing capabilities, resulting in lower operational costs, accelerated time-tovalue and reduced risk of human error. Nevertheless, the growing enthusiasm for leveraging AI in a ZSM system should not overlook the potential limitations and risks of using AI techniques. The current paper aims to introduce the ZSM concept and point out the AI-based limitations and risks that need to be addressed in order to make ZSM a reality.Item Deep Ensemble Learning based GPS Spoofing Detection for Cellular-Connected UAVs(IEEE, 2022-12-15) Dang, Yongchao; Benzaid, Chafika; Yang, Bin; Taleb, Tarik; Shen, Yulong; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Xidian University; University of Oulu; Chuzhou UniversityUnmanned Aerial Vehicles (UAVs) are an emerging technology in the 5G and beyond systems with the promise of assisting cellular communications and supporting IoT deployment in remote and density areas. Safe and secure navigation is essential for UAV remote and autonomous deployment. Indeed, the open-source simulator can use commercial software-defined radio tools to generate fake GPS signals and spoof the UAV GPS receiver to calculate wrong locations, deviating from the planned trajectory. Fortunately, the existing mobile positioning system can provide additional navigation for cellular-connected UAVs and verify the UAV GPS locations for spoofing detection, but it needs at least three base stations at the same time. In this paper, we propose a novel deep ensemble learning-based, mobile network-assisted UAV monitoring and tracking system for cellular-connected UAV spoofing detection. The proposed method uses path losses between base stations and UAVs communication to indicate the UAV trajectory deviation caused by GPS spoofing. To increase the detection accuracy, three statistics methods are adopted to remove environmental impacts on path losses. In addition, deep ensemble learning methods are deployed on the edge cloud servers and use the multi-layer perceptron (MLP) neural networks to analyze path losses statistical features for making a final decision, which has no additional requirements and energy consumption on UAVs. The experimental results show the effectiveness of our method in detecting GPS spoofing, achieving above 97% accuracy rate under two BSs, while it can still achieve at least 83% accuracy under only one BS.Item Energy and Delay aware Physical Collision Avoidance in Unmanned Aerial Vehicles(IEEE, 2018) Ouahouah, Sihem; Prados-Garzon, Jonathan; Taleb, Tarik; Benzaid, Chafika; Department of Communications and Networking; Mobile Network Softwarization and Service CustomizationSeveral solutions have been proposed in the literature to address the Unmanned Aerial Vehicles (UAVs) collision avoidance problem. Most of these solutions consider that the ground controller system (GCS) determines the path of a UAV before starting a particular mission at hand. Furthermore, these solutions expect the occurrence of collisions based only on the GPS localization of UAVs as well as via object-detecting sensors placed on board UAVs. The sensors' sensitivity to environmental disturbances and the UAVs' influence on their accuracy impact negatively the efficiency of these solutions. In this vein, this paper proposes a new energy- and delay-aware physical collision avoidance solution for UAVs. The solution is dubbed EDCUAV. The primary goal of EDC-UAV is to build in-flight safe UAVs trajectories while minimizing the energy consumption and response time. We assume that each UAV is equipped with a global positioning system (GPS) sensor to identify its position. Moreover, we take into account the margin error of the GPS to provide the position of a given UAV. The location of each UAV is gathered by a cluster head, which is the UAV that has either the highest autonomy or the greatest computational capacity. The cluster head runs the EDC-UAV algorithm to control the rest of the UAVs, thus guaranteeing a collision free mission and minimizing the energy consumption to achieve different purposes. The proper operation of our solution is validated through simulations. The obtained results demonstrate the efficiency of EDC-UAV in achieving its design goals.Item Energy-aware Collision Avoidance stochastic Optimizer for a UAVs set(2020-06) Ouahouah, Sihem; Prados-Garzon, Jonathan; Taleb, Tarik; Benzaid, Chafika; Department of Communications and Networking; Mobile Network Softwarization and Service CustomizationUnmanned aerial vehicles (UAVs) is one of the promising technology in the future. A recent study claims that by 2026, the commercial UAVs, for both corporate and customer applications, will have an annual impact of 31 billion to 46 billion on the country's GDP. Shortly, many UAVs will be flying everywhere. For this reason, there is a need to suggest efficient mechanisms for preventing the collisions among the UAVs. Traditionally, the collisions are prevented using dedicated sensors, however, those would generate uncertainty in their reading due to their external conditions sensitivity. From another side, the use of those sensors could create an extra overhead on the UAVs in terms of cost and energy consumption. To deal with these challenges, in this paper, we have suggested a solution that leverages the chance-constrained optimization technique for avoiding the collision in an energy-efficient manner. Building on the expressions for the non-central Chi-square CDF and expected value, and through the convexification of the resulting expressions, the chance-constrained optimization program is transformed into a convex Mixed Binary Nonlinear one. The resulting program allows us to find the optimal safety distance that extends UAVs life-time and allows every UAV to move with a guaranteed probability of collision between any pair of UAVs.Item GPS Spoofing Detector with Adaptive Trustable Residence Area for Cellular based-UAVs(2020-12) Dang, Yongchao; Benzaid, Chafika; Shen, Yulong; Taleb, Tarik; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; Xidian UniversityThe envisioned key role of Unmanned Aerial Vehicles (UAVs) in assisting the upcoming mobile networks calls for addressing the challenge of their secure and safe integration in the airspace. The GPS spoofing is a prominent security threat of UAVs. In this paper, we propose a 5G-assisted UAV position monitoring and anti-GPS spoofing system that allows live detection of GPS spoofing by leveraging Uplink received signal strength (RSS) measurements to cross-check the position validity. We introduce the Adaptive Trustable Residence Area (ATRA); a novel strategy to determine the trust area within which the UAV’s GPS position should be located in order to be considered as non-spoofed. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with a rate of above 95%.Item INSPIRE-5Gplus: Intelligent security and pervasive trust for 5G and beyond networks(2020-08-25) Ortiz, Jordi; Sanchez-Iborra, Ramon; Bernabe, Jorge Bernal; Skarmeta, Antonio; Benzaid, Chafika; Taleb, Tarik; Alemany, Pol; Muñoz, Raul; Vilalta, Ricard; Gaber, Chrystel; Wary, Jean Philippe; Ayed, Dhouha; Bisson, Pascal; Christopoulou, Maria; Xilouris, George; De Oca, Edgardo Montes; Gür, Gürkan; Santinelli, Gianni; Lefebvre, Vincent; Pastor, Antonio; Lopez, Diego; Department of Communications and Networking; Mobile Network Softwarization and Service Customization; University of Murcia; Catalan Telecommunications Technology Centre; Orange Polska; Thales; Demokritos National Centre for Scientific Research; Montimage; Zurich University of Applied Sciences; SolidshieldThe promise of disparate features envisioned by the 3GPP for 5G, such as offering enhanced Mobile Broadband connectivity while providing massive Machine Type Communications likely with very low data rates and maintaining Ultra Reliable Low Latency Communications requirements, create a very challenging environment for protecting the 5G networks themselves and associated assets. To overcome such complexity, future 5G networks must employ a very high degree of network and service management automation, which is a security challenge by itself as well as an opportunity for smarter and more efficient security functions. In this paper, we present the smart, trustworthy and liable 5G security platform being designed and developed in the INSPIRE-5Gplus1 project. This platform takes advantage of new techniques such as Machine Learning (ML), Artificial Intelligence (AI), Distributed Ledger Technologies (DLT), network softwarization and Trusted Execution Environment (TEE) for closed-loop and end-to-end security management following a zero-touch model in 5G and Beyond 5G networks. To this end, we specifically elaborate on two key aspects of our platform, namely security management with Security Service Level Agreements (SSLAs) and liability management, in addition to the description of the overall architecture.Item Robust Self-Protection Against Application-Layer (D)DoS Attacks in SDN Environment(2020-05) Benzaid, Chafika; Boukhalfa, Mohammed; Taleb, Tarik; Department of Communications and Networking; Mobile Network Softwarization and Service CustomizationThe expected high bandwidth of 5G and the envisioned massive number of connected devices will open the door to increased and sophisticated attacks, such as application-layer DDoS attacks. Application-layer DDoS attacks are complex to detect and mitigate due to their stealthy nature and their ability to mimic genuine behavior. In this work, we propose a robust application-layer DDoS self-protection framework that empowers a fully autonomous detection and mitigation of the application-layer DDoS attacks leveraging on Deep Learning (DL) and SDN enablers. The DL models have been proven vulnerable to adversarial attacks, which aim to fool the DL model into taking wrong decisions. To overcome this issue, we build a DL-based application-layer DDoS detection model that is robust to adversarial examples. The performance results show the effectiveness of the proposed framework in protecting against application-layer DDoS attacks even in the presence of adversarial attacks.Item Transfer Learning based GPS Spoofing Detection for Cellular-Connected UAVs(2022-07-19) Dang, Yongchao; Benzaid, Chafika; Taleb, Tarik; Yang, Bin; Shen, Yulong; Department of Communications and Networking; Department of Bioproducts and Biosystems; Mobile Network Softwarization and Service Customization; Xidian UniversityUnmanned Aerial Vehicles (UAVs) are set to become an integral part of 5G and beyond systems with the promise of assisting cellular communications and enabling advanced applications and services, such as public safety, caching, and virtual/mixed reality-based remote inspection. However, safe and secure navigation of UAVs is a key requisite for their integration in the airspace. The GPS spoofing is one of the major security threats to remotely and autonomously controlled UAVs. In this paper, we propose a machine learning-based, mobile network-assisted UAV monitoring and control system that allows live monitoring of UAVs' locations and intelligent detection of spoofed positions. We introduce the Convolutional Neural Network (CNN) in the edge UAV Flight Controller (UFC) to locate a UAV and detect any GPS spoofing by comparing differences between the theoretical path loss computed by UFC and the corresponding path loss reported by the connected base station (BS). To reduce the detection latency as well as to increase the detection accuracy, transfer learning is leveraged to transfer the CNN knowledge between edge servers when the UAV handovers from one BS to another. The performance evaluation shows that the proposed solution can successfully detect spoofed GPS positions with an accuracy rate above 88% using only one BS.Item Trust in 5G and beyond Networks(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2021-05-01) Benzaid, Chafika; Taleb, Tarik; Farooqi, Muhammad Zubair; Department of Communications and Networking; Mobile Network Softwarization and Service Customization5G and beyond ecosystems will be characterized by a growing set of stakeholders and an increasing number of interconnected devices and services, not necessarily under the administration of the same entity. Establishing trust in such an open and diverse ecosystem is a cornerstone for a global adoption of the technology. In this vein, it is important to tackle security and privacy risks stemming from this rich ecosystem. In this article, we shed light on the trust concept in 5G and beyond networks and its dimensions, while pointing out potential emerging trust enablers and research directions. Furthermore, we propose a blockchain-based data integrity framework to foster trust in data used by a machine learning pipeline.Item ZSM Security: Threat Surface and Best Practices(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2020-05-01) Benzaid, Chafika; Taleb, Tarik; Department of Communications and Networking; Mobile Network Softwarization and Service CustomizationThe ETSI's Zero touch network and Service Management (ZSM) framework is a prominent initiative to tame the envisioned complexity in operating and managing 5G and beyond networks. To this end, the ZSM framework promotes the shift toward full Automation of Network and Service Management and Operation (ANSMO) by leveraging the flexibility of SDN/NFV technologies along with Artificial Intelligence, combined with the portability and reusability of model-driven, open interfaces. Besides its benefits, each leveraged enabler will bring its own security threats, which should be carefully tackled to make the ANSMO vision a reality. This paper introduces the ZSM's potential attack surface and recommends possible mitigation measures along with some research directions to safeguard ZSM system security.