Azzam Mourad

• Doctor of Philosophy
• Full Professor of Computer Science at LAU and NYU-AD

In the ever-evolving telecommunications sector, advancing from 5G towards 6G, maintaining the security of core infrastructures has become supreme. This study addresses the critical need for proactive and real-time anomaly detection within cloud-native environments. Leveraging cloud-native implementations within Kubernetes clusters, our framework ut...

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation,...

Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefore, it is necessary to consider ways to automate these tasks and assist medical professionals in classifying and assessing cardiac US images. Machine learning (ML) techniques are regarded as a prominent...

Medical Ultrasound (US) imaging has seen increasing demands over the past years, becoming one of the most preferred imaging modalities in clinical practice due to its affordability, portability, and real-time capabilities. However, it faces several challenges that limit its applicability, such as operator dependency, variability in interpretation,...

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby improving adaptability. However, in dynamic and mobile environments, the a...

The rapid evolution of communication networks in recent decades has intensified the need for advanced Network and Service Management (NSM) strategies to address the growing demands for efficiency, scalability, enhanced performance, and reliability of these networks. Large Language Models (LLMs) have received tremendous attention due to their unpara...

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, educati...

Federated learning gained importance in sensitive IoT environments by creating a privacy-preserving ecosystem where participants share machine-learning models instead of raw data. However, federated learning shifts data control away from the server, exposing it to Non-Independent and Identically Distributed (non-IID) problems caused by biased clien...

Federated learning is a promising collaborative and privacy-preserving machine learning approach in data-rich smart cities. Nevertheless, the inherent heterogeneity of these urban environments presents a significant challenge in selecting trustworthy clients for collaborative model training. The usage of traditional approaches, such as the random c...

In urban environments, efficiently decrypting CP-ABE in VANETs is a significant challenge due to the dynamic and resource-constrained nature of these networks. VANETs are critical for ITS that improve traffic management, safety, and infotainment through V2V and V2I communication. However, managing computational resources for CP-ABE decryption remai...

In this paper, we tackle the network delays in the Internet of Things (IoT) for an enhanced QoS through a stable and optimized federated fog computing infrastructure. Network delays contribute to a decline in Quality-of-Service (QoS) for IoT applications and may even disrupt time-critical functions. Our paper addresses the challenge of establishing...

With the widespread use of smartphones and wearable devices, Mobile Crowdsourcing (MCS) has become a powerful method for gathering and processing data from various users. MCS offers several advantages, including improved mobility, scalability, cost-effectiveness, and the utilization of collective human intelligence. However, ensuring the authentici...

The “black-box” nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In...

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 ran...

Internet of Things (IoT), Digital Twin (DT), and Federated Learning (FL) are redefining the future vision of globalization. While IoT is about sensing data from physical devices, DTs reflect their digital representation and enable optimized decision-making by tightly integrating Artificial Intelligence (AI). Although swiftly growing, DTs are raisin...

Fog computing empowers the internet of vehicles (IoV) paradigm by offering computational resources near the end users. In this dynamic paradigm, users tend to move in and out of the range of fog nodes which has implications for the quality of service of the vehicular applications. To cope with these limitations, scholars addressed forming federatio...

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the mo...

Due to the current improvement in self-driving cars and the extensive focus and research on the topic of the Internet of Vehicles (IoV), the near future may behold a great revolution in the automotive industry as cars become fully autonomous. This change entails a considerable amount of data to be transferred from internet of things (IoT) devices,...

In this work, we propose a new paradigm of Federated Learning (FL) for Internet of Things (IoT) devices called Coalitional Federated Learning . The proposed paradigm aims to address the challenges of (1) non-independent and identically distributed (non-IID) data across clients; (2) communication overhead due to the large number of messages exchan...

Federated Learning (FL) is a revolutionary privacy-preserving distributed learning framework that allows a small group of users to cooperatively build a machine-learning model using their own data locally. Smart cities are areas that can generate high volume and critical data, which has the potential to revolutionize federated learning. Nevertheles...

Driven by privacy concerns and the promise of Deep Learning, researchers have devoted significant effort to exploring the applicability of Machine Learning (ML). In the domains of communication, network, and service management, ML-based decision-making solutions are eagerly sought to replace traditional model-driven approaches, addressing the growi...

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, educati...

The black-box nature of artificial intelligence (AI) models has been the source of many concerns in their use for critical applications. Explainable Artificial Intelligence (XAI) is a rapidly growing research field that aims to create machine learning models that can provide clear and interpretable explanations for their decisions and actions. In t...

Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from...

The rapid progress of the artificial intelligence sector has greatly impacted Vehicular Edge Components (VEC) in the Vehicular Ad-Hoc Network (VANET). Various AI applications, including automatic driving, preaccident alerts, and video broadcasting, have become essential to meet VANET’s diverse requirements. However, implementing these applications...

As the cloud moves from monolithic infrastructure to a self-isolated cloud native microservice environment, automation is becoming an important aspect for the management of the application life cycle. In this context, there are many tools available that can monitor these applications and raise alarms. However, automated orchestration is still in it...

Federated Learning (FL) is a novel distributed privacy-preserving learning paradigm, which enables the collaboration among several devices. 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...

Machine Learning's influence increases daily, and its application prevails in different, critical, and life-changing fields. However, collecting the needed data is challenging due to the increasing concerns about clients' privacy. In this context, federated learning addresses privacy issues by adopting an on-device model training strategy while com...

Continuous behavioural authentication methods add a unique layer of security by allowing individuals to verify their unique identity when accessing a device. Maintaining session authenticity is now feasible by monitoring users' behaviour while interacting with a mobile or Internet of Things (IoT) device, making credential theft and session hijackin...

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the mo...

Conventional systems are usually constrained to store data in a centralized location. This restriction has either precluded sensitive data from being shared or put its privacy on the line. Alternatively, Federated Learning (FL) has emerged as a promising privacy-preserving paradigm for exchanging model parameters instead of private data of Internet...

Autism Spectrum Disorder (ASD) is a neuro-developmental syndrome resulting from alterations in the embryological brain before birth. This disorder distinguishes its patients by special socially restricted and repetitive behavior in addition to specific behavioral traits. Hence, this would possibly deteriorate their social behavior among other indiv...

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 ran...

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 thi...

In this paper, we propose Value Iteration Network for Reward Shaping (VIN-RS), a potential-based reward shaping mechanism using Convolutional Neural Network (CNN). The proposed VIN-RS embeds a CNN trained on computed labels using the message passing mechanism of the Hidden Markov Model. The CNN processes images or graphs of the environment to predi...

The continuous proliferation of social media platforms and the exponential increase in users’ engagement are impacting social behavior and leading to various challenges, including the detection and identification of key influencers. In fact the opinions of these influencers are at the core of decision-making strategies, and are leading trends on th...

Federated learning using fog computing can suffer from the dynamic behavior of some of the participants in its training process, especially in Internet-of-Vehicles where vehicles are the targeted participants. For instance, the fog might not be able to cope with the vehicles’ demands in some areas due to resource shortages when the vehicles gather...

In this paper, we consider the problem of low-speed convergence in Reinforcement Learning (RL). As a solution, various potential-based reward shaping techniques were proposed to form the potential function. Learning a potential function is still challenging and comparable to building a value function from scratch. In this work, our main contributio...

Integrating device-to-device (D2D) cooperation with mobile edge computing (MEC) for computation offloading has proven to be an effective method for extending the system capabilities of low-end devices to run complex applications. This can be realized through efficient computing data offloading and yet enhanced while simultaneously using multiple wi...

Due to the exploding traffic demands and the diversity of novel applications requiring extensive computation and radio resources, research has been active to devise mechanisms for responding to these challenges. Mobile edge computing (MEC) and device-to-device (D2D) computation task offloading are expected to play a major role in serving devices wi...

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low...

Continuous authentication for mobile devices acknowledges users by studying their behavioural interactions with their devices. It provides an extended protection mechanism that supplies an additional layer of security for smartphones and Internet of Things (IoT) devices and locks out intruders in cases of stolen credentials or hijacked sessions. Mo...

Intelligent vehicles optimize road traveling through their reliance on autonomous driving applications to navigate. These applications integrate machine learning to extract statistical patterns and sets of rules for the vehicles to follow when facing decision-making scenarios. The immaturity of such systems, caused by the lack of a diverse dataset,...

In the past several years, the world has witnessed an acute surge in the production and usage of smart devices which are referred to as the Internet of Things (IoT). These devices interact with each other as well as with their surrounding environments to sense, gather and process data of various kinds. Such devices are now part of our everyday’s li...

Cloud computing is a reliable solution to provide distributed computation power. However, real-time response is still challenging regarding the enormous amount of data generated by the IoT devices in 5G and 6G networks. Thus, multi-access edge computing (MEC), which consists of distributing the edge servers in the proximity of end-users to have low...

Federated fog computing is an answer for horizontally upscaling fog resources to improve the Quality of Service (QoS) of Internet of Things (IoT) applications. However, the dynamic nature of some IoT’s crucial components, such as the ones of Internet of Vehicles (IoV), may hinder the QoS improvement and result in its deterioration instead. Specific...

Instability within fog federations is considered as a serious problem that degrades the performance of the provided services. The latter may affect the service availability due to fog providers withdrawing their resources. It may either lead to failures for some users invocations, or to an increase in the number of tasks inside the servers’ process...

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 a...

The increasing number of Internet of Things (IoT) devices necessitates the need for a more substantial fog computing infrastructure to support the users' demand for services. In this context, the placement problem consists of selecting fog resources and mapping services to these resources. This problem is particularly challenging due to the dynamic...

The increasing number of Internet of Things (IoT) devices necessitates the need for a more substantial fog computing infrastructure to support the users' demand for services. In this context, the placement problem consists of selecting fog resources and mapping services to these resources. This problem is particularly challenging due to the dynamic...

Multi-persona mobile computing has begun to make its way to determine the battle about practical strategy for adopting personal devices in workplace. Though its competency, multi-persona performance and viability are critically threatened by the limited resources of mobile devices. In recent years, mobile edge computing (MEC) has risen as promising...

Currently, researchers have motivated a vision of 6G for empowering the new generation of the Internet of Everything (IoE) services that are not supported by 5G. In the context of 6G, more computing resources are required, a problem that is dealt with by Mobile Edge Computing (MEC). However, due to the dynamic change of service demands from various...

The communication and networking field is hungry for machine learning decision-making solutions to replace the traditional model-driven approaches that proved to be not rich enough for seizing the ever-growing complexity and heterogeneity of the modern systems in the field. Traditional machine learning solutions assume the existence of (cloud-based...

Online Social Network (OSN) is considered a key source of information for real-time decision making. However, several constraints lead to decreasing the amount of information that a researcher can have while increasing the time of social network mining procedures. In this context, this paper proposes a new framework for sampling Online Social Netwo...

Driven by privacy concerns and the visions of Deep Learning, the last four years have witnessed a paradigm shift in the applicability mechanism of Machine Learning (ML). An emerging model, called Federated Learning (FL), is rising above both centralized systems and on-site analysis, to be a new fashioned design for ML implementation. It is a privac...

Network delays cause disturbance and reduction in the Quality-of-Service (QoS) for Internet-of-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 fo...

As an alternative to centralized systems, which may prevent data to be stored in a central repository due to its privacy and/or abundance, Federated Learning (FL) is nowadays a game changer addressing both privacy and cooperative learning. It succeeds in keeping training data on the devices, while sharing locally computed then globally aggregated m...

With the ever increasing number of cyber-attacks, Internet of Things (IoT) devices are being exposed to serious malware, attacks, and malicious activities alongside their development. While past research has been focused on centralized intrusion detection assuming the existence of a central entity to store and perform analysis on data from all part...

In the current banking systems and business processes, the permission granted to employees is controlled and managed by the configured access control methods, in which static role-based models focus on access to information and functions. The deployed configuration is not reviewed/updated systematically and is handled manually by managers. Conseque...

Internet of Vehicles and Vehicular networks have been compelling targets for malicious security attacks where several intrusion detection solutions have been proposed for protecting them. Nonetheless, their main problem lies in their heavy computation, which makes them unsuitable for next-generation AI-powered self-driving vehicles whose computatio...