Network slicing architecture, enabled by new technologies such as Network Functions Virtualizatio... more Network slicing architecture, enabled by new technologies such as Network Functions Virtualization (NFV) and Software-Defined Networking (SDN), is one of the main pillars of Fifth-generation and Beyond (B5G). In B5G settings, the number of coexisting slices with varying degrees of complexity and very diverse lifespans, resource requirements, and performance targets is expected to explode. This creates significant challenges towards zero-touch slice management and orchestration, including security, fault management, and trust. In addition, network slicing opens the business market to new stakeholders, namely the vertical or tenant, the network slice provider, and the infrastructure provider. In this context, there is a need to ensure not only a secure interaction between these actors, but also that each actor delivers the expected service to meet the network slice requirements. Therefore, new trust architectures should be designed, which are able to identify/detect the new forms of slicing-related attacks in real-time, while securely and automatically managing Service Level Agreements (SLA) among the involved actors. In this thesis, we devise new security architectures tailored to network slicing ready networks (B5G), heavily relying on blockchain and Artificial Intelligence (AI) to enable secure and trust network slicing management.
IEEE Transactions on Network and Service Management
The zero Touch Management (ZSM) concept in 5G and Beyond networks (B5G) aims to automate the mana... more The zero Touch Management (ZSM) concept in 5G and Beyond networks (B5G) aims to automate the management and orchestration of running network slices. This requires heavy usage of advanced deep learning techniques in a closed-loop way to auto-build the suitable decisions, enabling to meet network slices' requirements. In this context, Federated Learning (FL) is playing a vital role in training deep learning models in a collaborative way among thousands of network slice participants while ensuring their privacy and hence network slice isolation. Specifically, running network slices may share only their model parameters with a central entity, e.g., Inter Domain Slice Manager, to aggregate them and build a global model. Thus, the central entity does not directly access the training data. However, FL is vulnerable to poisoning attacks, where an insider participant may upload poisoning updates to the central entity so that it can cause a construction failure of the global model and thus affect its global performance. Therefore, it is crucial to design security means to detect and mitigate such threats. In this paper, we design a novel framework to automatically detect malicious participants in the FL process. In particular, our framework first uses a deep reinforcement algorithm to dynamically select a network slice as a trusted participant, based mainly on its reputation. The selected participant will then be in charge of identifying poisoning model updates by leveraging unsupervised machine learning. We demonstrate the feasibility of our framework on top of a real dataset that we generate using the 5G OpenAirInterface (OAI) platform. Evaluation results show the efficiency of our framework in dealing with poisoning attacks even with the presence of several malicious participants.
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
The emergent Zero touch Service and Management (ZSM) paradigm aims to automate the orchestration ... more The emergent Zero touch Service and Management (ZSM) paradigm aims to automate the orchestration and management of running network slices, in Beyond 5G networks (B5G), with an unprecedented level of scalability. To achieve this vision, ZSM calls for a large usage of advanced deep learning algorithms, in order to dynamically build efficient decisions. In this context, Federated deep Learning (FL) proved their efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Indeed, FL-based solutions give "machine-centric" decisions about running network slices and their performance, which will be then executed/applied by managers, i.e., slice manager staff/module. However, FL-enabled solutions do not provide any details about why and how such decisions were made, and thus such decisions cannot be properly trusted/understood by slice managers. To alleviate this issue, we leverage eXplainable Artificial Intelligence (XAI) paradigm that aims to improve the transparency of black-box FL decision-making process. In particular, XAI helps to explain the FL-based decisions to make them interpretable/trustable by network slices managers. In this paper, we design a novel XAI-powered framework to explain FL-based decisions. We first build a deep learning model in federated way, to predict key performance indicators (KPI) of network slices. Our FL-based KPI prediction is useful for the configuration and the management of network slice lifecycle, especially for the Service Level Agreement (SLA) violation and the network slice re-configuration. Then, we develop several XAI models on the top of our FL-based model, such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), RuleFit, and Partial Dependence Plot (PDP), to enhance the level of trust, credibility (in the local data/models), transparency, and explanation of the FL-based decisions, while adhering the data privacy, to different B5G network stakeholders, such as slice managers. Experiments results show the efficiency of our XAI-powered framework, to explain FL-based decisions related to latency KPI predictions.
The future network slicing enabled mobile ecosystem is expected to support a wide set of heteroge... more The future network slicing enabled mobile ecosystem is expected to support a wide set of heterogenous vertical services over a common infrastructure. The service robustness and their intrinsic requirements, together with the heterogeneity of mobile infrastructure and resources in both the technological and the spatial domain, significantly increase the complexity and create new challenges regarding network management and orchestration. High degree of automation, flexibility and programmability are becoming the fundamental architectural features to enable seamless support for the modern telco-based services. In this paper, we present a novel management and orchestration platform for network slices, which has been devised by the Horizon 2020 MonB5G project. The proposed framework is a highly scalable solution for network slicing management and orchestration that implements a distributed and programmable AI-driven management architecture. The cognitive capabilities are provided at diff...
ICC 2021 - IEEE International Conference on Communications, 2021
It is well established that 5G will impact not only the end-users by allowing several new service... more It is well established that 5G will impact not only the end-users by allowing several new services, but also the vertical industry and network operators business. 5G will open the business market to new stakeholders with the introduction of Network Slicing, namely the vertical or tenant, the network slice provider, and the infrastructure provider. The Network Slice provider sells end-to-end network slices (virtual end-toend mobile network) to the vertical while leasing virtual and physical resources from Infrastructure Providers to enforce these end-to-end network slices. Accordingly, there is a need to establish Service Level Agreement (SLA) among these actors to ensure: (1) that the service is well-delivered to the vertical and (2) the infrastructure providers are respecting their involvement with the network slice provider. To fill this gap, in this paper, we propose a trust architecture to automatically manage the SLAs and apply penalties and compensations if the SLAs are not respected by one of the involved actors.
5G opens the door not only for new network services but also for a new business model involving n... more 5G opens the door not only for new network services but also for a new business model involving new business actors. Particularly, the introduction of Network Slicing encourages the appearance of new stakeholders, namely Verticals or network slice tenants, Network Slice Providers, and Resource Providers. The Network Slice Provider sells end‐to‐end network slices (virtual end‐to‐end mobile network) to the Vertical, while leasing virtual and physical resources from Resource Providers to deploy and instantiate these end‐to‐end network slices. Consequently, a trusted relationship among these actors should be established and maintained. To fill this gap, in this paper, we propose a Blockchain‐based trust architecture that aims to: (1) create and negotiate the deployment of an end‐to‐end slice network; (2) manage automatically the service level agreements (SLA) established between the involved actors; while guaranteeing security and anonymous transactions.
Network slicing architecture, enabled by new technologies such as Network Functions Virtualizatio... more Network slicing architecture, enabled by new technologies such as Network Functions Virtualization (NFV) and Software-Defined Networking (SDN), is one of the main pillars of Fifth-generation and Beyond (B5G). In B5G settings, the number of coexisting slices with varying degrees of complexity and very diverse lifespans, resource requirements, and performance targets is expected to explode. This creates significant challenges towards zero-touch slice management and orchestration, including security, fault management, and trust. In addition, network slicing opens the business market to new stakeholders, namely the vertical or tenant, the network slice provider, and the infrastructure provider. In this context, there is a need to ensure not only a secure interaction between these actors, but also that each actor delivers the expected service to meet the network slice requirements. Therefore, new trust architectures should be designed, which are able to identify/detect the new forms of slicing-related attacks in real-time, while securely and automatically managing Service Level Agreements (SLA) among the involved actors. In this thesis, we devise new security architectures tailored to network slicing ready networks (B5G), heavily relying on blockchain and Artificial Intelligence (AI) to enable secure and trust network slicing management.
IEEE Transactions on Network and Service Management
The zero Touch Management (ZSM) concept in 5G and Beyond networks (B5G) aims to automate the mana... more The zero Touch Management (ZSM) concept in 5G and Beyond networks (B5G) aims to automate the management and orchestration of running network slices. This requires heavy usage of advanced deep learning techniques in a closed-loop way to auto-build the suitable decisions, enabling to meet network slices' requirements. In this context, Federated Learning (FL) is playing a vital role in training deep learning models in a collaborative way among thousands of network slice participants while ensuring their privacy and hence network slice isolation. Specifically, running network slices may share only their model parameters with a central entity, e.g., Inter Domain Slice Manager, to aggregate them and build a global model. Thus, the central entity does not directly access the training data. However, FL is vulnerable to poisoning attacks, where an insider participant may upload poisoning updates to the central entity so that it can cause a construction failure of the global model and thus affect its global performance. Therefore, it is crucial to design security means to detect and mitigate such threats. In this paper, we design a novel framework to automatically detect malicious participants in the FL process. In particular, our framework first uses a deep reinforcement algorithm to dynamically select a network slice as a trusted participant, based mainly on its reputation. The selected participant will then be in charge of identifying poisoning model updates by leveraging unsupervised machine learning. We demonstrate the feasibility of our framework on top of a real dataset that we generate using the 5G OpenAirInterface (OAI) platform. Evaluation results show the efficiency of our framework in dealing with poisoning attacks even with the presence of several malicious participants.
GLOBECOM 2022 - 2022 IEEE Global Communications Conference
The emergent Zero touch Service and Management (ZSM) paradigm aims to automate the orchestration ... more The emergent Zero touch Service and Management (ZSM) paradigm aims to automate the orchestration and management of running network slices, in Beyond 5G networks (B5G), with an unprecedented level of scalability. To achieve this vision, ZSM calls for a large usage of advanced deep learning algorithms, in order to dynamically build efficient decisions. In this context, Federated deep Learning (FL) proved their efficiency in not only building collaborative deep learning models, among several network slices, but also ensuring the privacy and isolation of such network slices. Indeed, FL-based solutions give "machine-centric" decisions about running network slices and their performance, which will be then executed/applied by managers, i.e., slice manager staff/module. However, FL-enabled solutions do not provide any details about why and how such decisions were made, and thus such decisions cannot be properly trusted/understood by slice managers. To alleviate this issue, we leverage eXplainable Artificial Intelligence (XAI) paradigm that aims to improve the transparency of black-box FL decision-making process. In particular, XAI helps to explain the FL-based decisions to make them interpretable/trustable by network slices managers. In this paper, we design a novel XAI-powered framework to explain FL-based decisions. We first build a deep learning model in federated way, to predict key performance indicators (KPI) of network slices. Our FL-based KPI prediction is useful for the configuration and the management of network slice lifecycle, especially for the Service Level Agreement (SLA) violation and the network slice re-configuration. Then, we develop several XAI models on the top of our FL-based model, such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), RuleFit, and Partial Dependence Plot (PDP), to enhance the level of trust, credibility (in the local data/models), transparency, and explanation of the FL-based decisions, while adhering the data privacy, to different B5G network stakeholders, such as slice managers. Experiments results show the efficiency of our XAI-powered framework, to explain FL-based decisions related to latency KPI predictions.
The future network slicing enabled mobile ecosystem is expected to support a wide set of heteroge... more The future network slicing enabled mobile ecosystem is expected to support a wide set of heterogenous vertical services over a common infrastructure. The service robustness and their intrinsic requirements, together with the heterogeneity of mobile infrastructure and resources in both the technological and the spatial domain, significantly increase the complexity and create new challenges regarding network management and orchestration. High degree of automation, flexibility and programmability are becoming the fundamental architectural features to enable seamless support for the modern telco-based services. In this paper, we present a novel management and orchestration platform for network slices, which has been devised by the Horizon 2020 MonB5G project. The proposed framework is a highly scalable solution for network slicing management and orchestration that implements a distributed and programmable AI-driven management architecture. The cognitive capabilities are provided at diff...
ICC 2021 - IEEE International Conference on Communications, 2021
It is well established that 5G will impact not only the end-users by allowing several new service... more It is well established that 5G will impact not only the end-users by allowing several new services, but also the vertical industry and network operators business. 5G will open the business market to new stakeholders with the introduction of Network Slicing, namely the vertical or tenant, the network slice provider, and the infrastructure provider. The Network Slice provider sells end-to-end network slices (virtual end-toend mobile network) to the vertical while leasing virtual and physical resources from Infrastructure Providers to enforce these end-to-end network slices. Accordingly, there is a need to establish Service Level Agreement (SLA) among these actors to ensure: (1) that the service is well-delivered to the vertical and (2) the infrastructure providers are respecting their involvement with the network slice provider. To fill this gap, in this paper, we propose a trust architecture to automatically manage the SLAs and apply penalties and compensations if the SLAs are not respected by one of the involved actors.
5G opens the door not only for new network services but also for a new business model involving n... more 5G opens the door not only for new network services but also for a new business model involving new business actors. Particularly, the introduction of Network Slicing encourages the appearance of new stakeholders, namely Verticals or network slice tenants, Network Slice Providers, and Resource Providers. The Network Slice Provider sells end‐to‐end network slices (virtual end‐to‐end mobile network) to the Vertical, while leasing virtual and physical resources from Resource Providers to deploy and instantiate these end‐to‐end network slices. Consequently, a trusted relationship among these actors should be established and maintained. To fill this gap, in this paper, we propose a Blockchain‐based trust architecture that aims to: (1) create and negotiate the deployment of an end‐to‐end slice network; (2) manage automatically the service level agreements (SLA) established between the involved actors; while guaranteeing security and anonymous transactions.
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Papers by Sabra Saad