As the usage of smart devices is increasing, malware affecting such devices is rapidly evolving a... more As the usage of smart devices is increasing, malware affecting such devices is rapidly evolving as well. Security risks affecting the confidentiality, integrity, and privacy of smart devices are rapidly emerging. Mobile security suites exist to defend the device against malware and other intrusions. However, they require extensive resources which is a constraint of the device itself. In this paper, we address the problem of intrusion detection for smart devices taking into account the devices' limited resources such as energy, CPU usage, and internet connectivity. We provide an ad-hoc mobile cloud based intrusion detection framework that takes advantage of Wi-Fi Direct as means of achieving connectivity and sharing resources and services for providing security. The proposed framework allows exchange of data with or without the availability of internet connection. The paper also provides experiments, carried out using real devices, showing various improvements of using our approach. Up to 61% and 40% enhancement in energy consumption and response time respectively is reached compared to local execution.
Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enable... more Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server's side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.
Numerous research recently proposed integrating Federated Learning (FL) to address the privacy co... more Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In this paper, we propose ModularFed, a research-focused framework that addresses the complexity of FL implementations and the lack of adaptability and extendability in the available frameworks. We provide a comprehensive architecture that assists FL approaches through well-defined protocols to cover three dominant FL paradigms: adaptable workflow, datasets distribution, and third-party application support. Within this architecture, protocols are blueprints that strictly define the framework's components' design, contribute to its flexibility, and strengthen its infrastructure. Further, our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators coupled with major built-in data distributors in the field. Additionally, the framework support wrapping multiple approaches in a single environment to enable consistent replication of FL issues such as clients' deficiency, data distribution, and network latency,
2017 IEEE International Conference on Web Services (ICWS), 2017
Insider attacks in which misbehaving Virtual Machines (VMs) take part of the cloud system and lea... more Insider attacks in which misbehaving Virtual Machines (VMs) take part of the cloud system and learn about its internal vulnerabilities constitute a major threat against cloud resources and infrastructure. This demands setting up continuous and comprehensive security arrangements to restrict the effects of such attacks. However, limited security resources prohibit full detection coverage on all VMs at all times, which can be exploited by attackers to examine the selective detection strategies and adjust their own attack plans accordingly. Motivated by the absence of any approach that accounts for such a challenge in the domain of cloud computing, we propose in this work an adaptive detection strategy that formulates a Stackelberg security game to enable the cloud system to optimally exploit its available amount of security resources to maximize the detection of distributed attacks, knowing that attackers have the ability to monitor the cloud system's strategies and adjust their own attack plans. Experiments carried out on the CloudSim framework reveal that the proposed solution maximizes the detection of distributed attacks and minimizes false negatives and positives compared to a maximin-based detection strategy, while being scalable to the increase in both the number of co-hosted VMs and percentage of co-resident attackers.
Abstract In this paper, we address the problem of task allocation in Mobile Crowdsensing (MCS) by... more Abstract In this paper, we address the problem of task allocation in Mobile Crowdsensing (MCS) by means of forming tasks publisher coalition taking into consideration workers' route preferences. In prior research works, only one of the MCS components (either task publishers, contributors or platform) dominates the task allocation process. Currently, other approaches have investigated tasks coalition based on their geographical locations. In this paper, we address the aforementioned problem by proposing a new scheme taking into accounts the interest of all the participating parties. To this end, our approach provides (1) strategies for selecting the best routes for workers with better long-term earnings and (2) task publishers' coalition formation based on worker's routes selection and preferences regardless of the order of individual execution of tasks. We proposed two models for the coalition formation: i) a centralized approach to solve the problem of the coalition formation together with the route selection, and ii) a simplified heuristic version that first determines disjoint tasks' coalitions based on the preferred routes selected by workers, then, MCS platform sorts the coalitions with best quality of information and selects the best routes for each ordered coalition. Simulation results with real data-set show that the coalition of task publishers together with the distributed route selection per worker does guarantee the quality of information satisfaction of the sensing tasks while enhancing the worker payment.
Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internetof-Thi... more Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internetof-Things (IoT) while end-users are running critical real-time services. In parallel, federated fogs are not effective when formed without considering the performance perceived by the end-users. This article presents a novel architecture for the federated fog concept and proposes an adaptive and intelligent federation formation approach using Genetic Algorithm and Machine Learning models. Fog federations serve as a solution for fog providers to offer the required QoS they serve. Such a concept allows efficient distribution of load among multiple fog providers that share their resources. Throughout this process, the issue of QoS deterioration, due to local overloads, is relatively solved. Hence, the end users can enjoy a delay-free experience when using real-time applications. Real data is used to evaluate the proposed architecture and formation mechanism. The results show a notable improvement in the throughput as well as a decrease in the response time for the services requested.
As the usage of smart devices is increasing, malware affecting such devices is rapidly evolving a... more As the usage of smart devices is increasing, malware affecting such devices is rapidly evolving as well. Security risks affecting the confidentiality, integrity, and privacy of smart devices are rapidly emerging. Mobile security suites exist to defend the device against malware and other intrusions. However, they require extensive resources which is a constraint of the device itself. In this paper, we address the problem of intrusion detection for smart devices taking into account the devices' limited resources such as energy, CPU usage, and internet connectivity. We provide an ad-hoc mobile cloud based intrusion detection framework that takes advantage of Wi-Fi Direct as means of achieving connectivity and sharing resources and services for providing security. The proposed framework allows exchange of data with or without the availability of internet connection. The paper also provides experiments, carried out using real devices, showing various improvements of using our approach. Up to 61% and 40% enhancement in energy consumption and response time respectively is reached compared to local execution.
Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enable... more Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several participants (e.g., Internet of Things devices) for the training of machine learning models. However, selecting the participants that would contribute to this collaborative training is highly challenging. Adopting a random selection strategy would entail substantial problems due to the heterogeneity in terms of data quality, and computational and communication resources across the participants. Although several approaches have been proposed in the literature to overcome the problem of random selection, most of these approaches follow a unilateral selection strategy. In fact, they base their selection strategy on only the federated server's side, while overlooking the interests of the client devices in the process. To overcome this problem, we present in this paper FedMint, an intelligent client selection approach for federated learning on IoT devices using game theory and bootstrapping mechanism. Our solution involves the design of: (1) preference functions for the client IoT devices and federated servers to allow them to rank each other according to several factors such as accuracy and price, (2) intelligent matching algorithms that take into account the preferences of both parties in their design, and (3) bootstrapping technique that capitalizes on the collaboration of multiple federated servers in order to assign initial accuracy value for the newly connected IoT devices. Based on our simulation findings, our strategy surpasses the VanillaFL selection approach in terms of maximizing both the revenues of the client devices and accuracy of the global federated learning model.
Numerous research recently proposed integrating Federated Learning (FL) to address the privacy co... more Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In this paper, we propose ModularFed, a research-focused framework that addresses the complexity of FL implementations and the lack of adaptability and extendability in the available frameworks. We provide a comprehensive architecture that assists FL approaches through well-defined protocols to cover three dominant FL paradigms: adaptable workflow, datasets distribution, and third-party application support. Within this architecture, protocols are blueprints that strictly define the framework's components' design, contribute to its flexibility, and strengthen its infrastructure. Further, our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators coupled with major built-in data distributors in the field. Additionally, the framework support wrapping multiple approaches in a single environment to enable consistent replication of FL issues such as clients' deficiency, data distribution, and network latency,
2017 IEEE International Conference on Web Services (ICWS), 2017
Insider attacks in which misbehaving Virtual Machines (VMs) take part of the cloud system and lea... more Insider attacks in which misbehaving Virtual Machines (VMs) take part of the cloud system and learn about its internal vulnerabilities constitute a major threat against cloud resources and infrastructure. This demands setting up continuous and comprehensive security arrangements to restrict the effects of such attacks. However, limited security resources prohibit full detection coverage on all VMs at all times, which can be exploited by attackers to examine the selective detection strategies and adjust their own attack plans accordingly. Motivated by the absence of any approach that accounts for such a challenge in the domain of cloud computing, we propose in this work an adaptive detection strategy that formulates a Stackelberg security game to enable the cloud system to optimally exploit its available amount of security resources to maximize the detection of distributed attacks, knowing that attackers have the ability to monitor the cloud system's strategies and adjust their own attack plans. Experiments carried out on the CloudSim framework reveal that the proposed solution maximizes the detection of distributed attacks and minimizes false negatives and positives compared to a maximin-based detection strategy, while being scalable to the increase in both the number of co-hosted VMs and percentage of co-resident attackers.
Abstract In this paper, we address the problem of task allocation in Mobile Crowdsensing (MCS) by... more Abstract In this paper, we address the problem of task allocation in Mobile Crowdsensing (MCS) by means of forming tasks publisher coalition taking into consideration workers' route preferences. In prior research works, only one of the MCS components (either task publishers, contributors or platform) dominates the task allocation process. Currently, other approaches have investigated tasks coalition based on their geographical locations. In this paper, we address the aforementioned problem by proposing a new scheme taking into accounts the interest of all the participating parties. To this end, our approach provides (1) strategies for selecting the best routes for workers with better long-term earnings and (2) task publishers' coalition formation based on worker's routes selection and preferences regardless of the order of individual execution of tasks. We proposed two models for the coalition formation: i) a centralized approach to solve the problem of the coalition formation together with the route selection, and ii) a simplified heuristic version that first determines disjoint tasks' coalitions based on the preferred routes selected by workers, then, MCS platform sorts the coalitions with best quality of information and selects the best routes for each ordered coalition. Simulation results with real data-set show that the coalition of task publishers together with the distributed route selection per worker does guarantee the quality of information satisfaction of the sensing tasks while enhancing the worker payment.
Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internetof-Thi... more Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internetof-Things (IoT) while end-users are running critical real-time services. In parallel, federated fogs are not effective when formed without considering the performance perceived by the end-users. This article presents a novel architecture for the federated fog concept and proposes an adaptive and intelligent federation formation approach using Genetic Algorithm and Machine Learning models. Fog federations serve as a solution for fog providers to offer the required QoS they serve. Such a concept allows efficient distribution of load among multiple fog providers that share their resources. Throughout this process, the issue of QoS deterioration, due to local overloads, is relatively solved. Hence, the end users can enjoy a delay-free experience when using real-time applications. Real data is used to evaluate the proposed architecture and formation mechanism. The results show a notable improvement in the throughput as well as a decrease in the response time for the services requested.
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Papers by Azzam Mourad