Federated Learning (FL) enables participant devices to collaboratively train deep learning models without sharing their data with the server or other devices, effectively addressing data privacy and computational concerns. however, FL faces a major bottleneck due to high communication overhead from frequent model updates between devices and the server, limiting deployment in resource-constrained wireless networks. In this paper, we propose a three-fold strategy: firstly, an Adaptive Feature-Elimination Strategy to drop less important features while retaining high-value ones; secondly, Adaptive Gradient Innovation and Error Sensitivity-Based Quantization, which dynamically adjusts the quantization level for innovative gradient compression; and thirdly, Communication Frequency Optimization to enhance communication efficiency. We evaluated our proposed model's performance through extensive experiments, assessing accuracy, loss, and convergence compared to baseline techniques. The results show that our model achieves high communication efficiency in the framework while maintaining accuracy.

The emergence of wearable medical devices and wireless body area networks (WBANs) has enabled continuous, real-time patient monitoring. These systems generate large volumes of health data, requiring low-latency and reliable processing for timely interventions. However, local processing is often inefficient due to the energy and computational limitations of mobile devices. Offloading tasks to edge computing and cloud resources offers a promising alternative. Nonetheless, optimizing offloading decisions in dynamic healthcare scenarios remains challenging due to heterogeneous task requirements and varying computational resources. This paper presents a hierarchical actor-critic task offloading approach (HACTO), a deep-reinforcement-learning framework designed to enhance the efficiency and adaptability of task offloading in healthcare scenarios. By introducing a hierarchical decision structure, HACTO reduces complexity and improves learning performance. The problem is modeled as a Markov decision process and solved using the deep deterministic policy gradient algorithm. HACTO jointly optimizes task offloading with respect to three objectives: meeting task deadlines, minimizing the energy consumption of mobile devices, and reducing resource usage costs. Our experimental results show that HACTO outperforms traditional and deep-reinforcement-learning-based offloading strategies, making it a promising solution for intelligent task offloading in resource-constrained WBAN environments.

Federated learning (FL) is a cutting-edge approach to machine learning where multiple clients (or nodes) collaboratively train a model while keeping their data localized. This method addresses significant privacy concerns and reduces data centralization risks. However, a key challenge in FL is efficiently selecting which clients contribute to the model and determining how often their updates should be aggregated. This process is crucial for enhancing model performance and maintaining data integrity. This paper introduces the Trust-Based Dynamic Node Selection and Aggregation Frequency Optimization methodology to tackle this challenge using a Deep Q-Network (DQN). We focus on dynamically selecting clients based on a trust metric that evaluates their reliability and the quality of their data contributions. This metric incorporates factors like historical accuracy, frequency of successful contributions, and consistency in participation. Furthermore, we optimize the frequency of aggregating client updates to improve learning efficiency and model accuracy. By integrating these elements, our approach aims to maximize the effectiveness of federated learning, ensuring that reliable and relevant data significantly influences the model, thereby enhancing its overall performance and trustworthiness.

Federated learning (FL) is a promising approach for training AI models across multiple clients in Edge Computing (EC), without sharing raw local data. By enabling local training and aggregating updates into a global model, FL maintains privacy while facilitating collaborative learning. Nevertheless, FL encounters several challenges, including trustworthy client participation, inefficient model aggregation due to client with malicious or less accurate model. In this paper, we propose a trustworthy FL method incorporating Q-learning, trust, and reputation mechanisms, enhancing model accuracy and fairness. This method promotes client participation, mitigates malicious attacks' impact, and ensures fair model distribution. Inspired by reinforcement learning, the Q-learning algorithm optimizes client selection using the Bellman equation, enabling the server to balance exploration and exploitation for improved system performance. Furthermore, we explored the advantages of peer-to-peer FL settings. Extensive experimentation demonstrates our proposed trustworthy FL approach's effectiveness in achieving high learning accuracy while ensuring fairness across clients and maintaining efficient client selection. Our results reveal significant improvements in model performance, convergence speed, and generalization.

In this growing technology epoch, one of the main concerns is about the cyber threats. To tackle this issue, highly skilled and motivated cybersecurity professionals are needed, who can prevent, detect, respond, or even mitigate the effect of such threats. However, the world faces workforce shortage of qualified cybersecurity professionals and practitioners. To solve this dilemma several cybersecurity educational programs have arisen. Before it was just a couple of courses in a computer science graduate program. Now a day’s different cybersecurity courses are introduced at the high school level, undergraduate computer science and information systems programs, even in the government level. Due to some peculiar nature of cybersecurity, educational institutions face many issues when designing a cybersecurity curriculum or cybersecurity activities.

In a jiffy after the outbreak of the 2019 novel coron-avirus, also called COVID-19 or SARS-CoV-2, the World Health Organization (WHO) considered it as a pandemic that threatens the demise of humanity. This quick decision was in conjunction with a real situation of biological inability to find a vaccine that can eliminate the virus or at least limit its spread. For that reason, technological intervention and cooperation are needed more than ever to face this pandemic. In this same context, we propose a novel system that deploys Vehicle-to-everything (V2X) technology and Blockchain in collaboration to face such a pandemic. Our proposal is centered on a triple-stage processing i) zone identification and classification based on vehicles' thermal cameras detection, ii) Blockchain-based information storage for enhanced patient medical information privacy, and iii) drones-based zone neutralization processes. Simulation results show that, thanks to the use of Blockchain technology, the network-related performance remain almost unchanged and hence, all Intelligent Transportation System (ITS) including inter-vehicles and inter-drones functionalities are not affected. In addition the additional overhead is very acceptable and does not exceed the 5 Kb in the worst case.

In healthcare sector, the move towards Electronic Health Records (EHR) systems has been accelerating in parallel with the increased adoption of IoT and smart devices. This is driven by the anticipated advantages for patients and healthcare providers. The integration of EHR and IoT makes it highly heterogenous in terms of devices, network standard, platforms, types data, connectivity, etc., and introduces security, patient and data privacy, and trust challenges. To address such challenges, this paper proposes an architecture that combines biometric-based blockchain technology with the EHR system. This integration ensures the integrity of data to control the access to the patient's Electronic Healthcare Records (EHRs) that are synchronized and exchanged, using Blockchain Technology, between distributed healthcare providers. More specifically, this paper describes a mechanism that enables the recovery of patient's access control on their EHRs securely without compromising their privacy and identity. A biometric-based blockchain EHR system (BBEHR) is proposed to uniquely identify patients, enable them to control access to their EHRs, and ensure recoverable access to their EHRs. The system takes into account the security and privacy requirements of Health Insurance Portability and Accountability Act (HIPAA) compliance, and it overcomes the challenges of using secret keys to control access to EHRs, in the cases of lost secret keys and the need for emergency access to EHRs without the presence of secret keys.

As more of the activities of daily living take place online, computer security education for high school students is of increasing importance. To address this need, we design a course model as prototype curriculum to teach high school students about the basics of information security. Prototype examples of hands-on lab activities are also introduced for enhancing students' practical skills on information security. IT educators can consider the proposed work in this paper as a guideline for designing and developing information security courses to high school students. We identify challenges encountered in this process, and contend that these challenges stem from the nature of the information security field.

Healthcare experts and researchers have been promoting the need for IoT-based remote health monitoring systems that take care of the health of elderly people. However, such systems may generate large amounts of data, which makes the security and privacy of such data to become imperative. This paper studies the security and privacy concerns of the existing Healthcare Monitoring System (HMS) and proposes a reference architecture (security integration framework) for managing IoT-based healthcare monitoring systems that ensures security, privacy, and reliable service delivery for patients and elderly people to reduce and avoid health related risks. Our proposed framework will be in the form of state-of-the-art Security Platform, for HMS, using the emerging Software Defined Network (SDN) networking paradigm. Our proposed integration framework eliminates the dependency on specific Software or vendor for different security systems, and allows for the benefits from the functional and secure applications, and services provided by the SDN platform.

Measuring vehicles density and distribution in urban environments is an important task. The results of such estimation are highly required for different applications such as road lights configuration, congestion control, and also inter-vehicle data routing. This task, which is known as crowd sensing, is mostly based on smartphone-assisted sensing. However, in urban environments the multiple kinds of obstacles make it hard and mostly inaccurate especially for RoadSide Units (RSUs) low density cases. Furthermore, the assumption that all vehicles are collaborative and honest can lead to unexpected and unwanted situations. To address the above mentioned problems, we propose in this paper a trust-aware crowd sensing technique based on Unmanned Aerial Vehicle (UAV) for vehicular urban environments. Considering the real traffic information and the distribution of dishonest nodes in the network gathered by UAVs, our proposed solution provides a global view to both vehicles and RSUs, which can be used for different applications such as: finding the shortest and most trusted possible path to messages' final destinations, and also for the intelligent congestion control. Our simulation results show that our solution offers instant crowd and trust information over which in addition to the high detection ratios, also high packet delivery ratios with low network overhead are achieved.

Software Defined Network (SDN) is a revolutionary idea to realize software-driven network with the separation of control and data planes. In essence, SDN addresses the problems faced by the traditional network architecture; however, it may as well expose the network to new attacks. Among other attacks, distributed denial of service (DDoS) attacks are hard to contain in such software-based networks. Existing DDoS mitigation techniques either lack in performance or jeopardize the accuracy of the attack detection. To fill the voids, we propose in this paper a machine learning-based DDoS mitigation technique for SDN. First, we create a model for DDoS detection in SDN using NSL-KDD dataset and then after training the model on this dataset, we use real DDoS attacks to assess our proposed model. Obtained results show that the proposed technique equates favorably to the current techniques with increased performance and accuracy.

Users of plug-in-electric vehicles (PEV) often need to charge outside their home power grid networks i.e. outside their supplier. This basically constitutes a roaming charging case. Hence, users should be protected from possible security and privacy attacks. Moreover, remote authorization and payment transaction mechanism is required between the different power suppliers. In this paper, we propose a secure charging and payment transaction protocol for roaming PEV charging. Our protocol is developed based on the principles of the Secure Electronic Transaction (SET) protocol. The protocol protects user's privacy not only from external suppliers but also from their own suppliers. Moreover, our protocol is designed in such a way that it is robust against the known security attacks. Our proposed work provides anonymous authorization and payment simultaneously not only between the roaming user and an external supplier but also between suppliers.

Plug-in electrical vehicles (PEVs) are becoming very popular among the car manufacturing industry and among researchers. This is due to their multiple benefits which come from their ability to charge and discharge power from and to the power grid. This can help reduce CO2emission and can be a great source of stabilization to the power system through delivering ancillary services such as spinning reserve, peak power, voltage and frequency regulations. However, this rapidly increasing industry has also introduced various kinds of security and privacy concerns that need to be addressed. Among these security issues is the lack of access control system which can manage who/is able to charge/discharge these types of vehicles particularly in systems where PEVs are driven by multiple drivers such as in fleet management and car renting company. In this paper, we propose architecture, called UConPEV, for managing PEV charging/discharging and controlling the usage of PEVs not only before an operation, but also during and after. We utilize the notion of usage control described in the well-known Usage Control model (UCON).

This paper introduces PSCAN, a port scanning-based network covert channel that violates non-discretionary system security policy that does not allow data transfer from a given process (the sender) to another given process (the receiver). Using PSCAN, the sender opens and closes network ports in a way that encodes covert data. The receiver performs a synchronized port scanning procedure on the sender's host to determine which ports are open and which ones are closed then decodes the data. The paper defines the covert channel and analyzes its data rate, stealthiness, and robustness. In addition, the paper investigates countermeasures against the channel.

In multicast communications, where service providers distribute sensitive information such as military operations information, an important issue is to control operation participants' access to transmitted data and network resources. This issue becomes more complicated when multicast communications take place in mobile ad hoc network (MANET) environments. In this study, we discuss the multicast security issues in MANET and propose a new approach for securing the communications under these conditions. Specifically, we determine the security requirements based on certain use case scenarios and define access control policies in mobile ad hoc multicast communications accordingly. The goal is to create a new protocol for multicast data dissemination while enforcing the required data access policies. The protocol that we propose is based on the multicast features of the well-known Ad hoc On-Demand Distance Vector Routing (MAODV) protocol and incorporates the features of the role-based access control model. The study shows that our approach is simple, but yet flexible and effective in controlling the access to data being transmitted within the multicast groups.

In multicast communications, where service providers distribute services such as streaming multimedia, distributed databases, and etc., an important issue is to control the access to both the transmitted data and the network resources. This issue becomes more complicated when the multicast communication takes place in a hierarchical organizational structure, such as the military. In this paper, we analyze the multicast security issues and propose a new approach to securing hierarchical multicast communications. Specifically, we specify requirements for developing access control policies in hierarchical multicast communications and propose a model for enforcing such policies. Our approach is based on the well-known role-based access control model. We show that our approach is simple, but yet is very flexible, scalable, and effective in controlling the access to the transmitted data as well as to the shared secret key, used in providing data confidentiality.

Abstract In the communication sector, the optimal objective is to equate quality and cost. The technologies that best serve these objectives are Wireless Access Technologies since they are easily deployed and capable of reaching and serving customers everywhere in a cost effective way. In this paper, we examine the communication options, and account for a country's geography to propose optimal WiMax planning keeping in mind the security concerns that are inherent in wireless communication. To perform WiMax radio network planning, we use a network simulation tool from ATDI called ICS Telecom. Our approach offers all users a minimum bandwidth of 1.4 Mbps as well as a coverage that exceeds 90% for all indoor users in the area under study. We optimize the network by iteratively minimizing the number of base stations required, and equivalently minimizing the cost, while maximizing the coverage for the subscribers. We also analyze the impact of security on the performance of WiMax. More specifically, we use well‐known simulation software called Qualnet to simulate a WiMax environment under different security protocols and encryption scenarios. We then analyze the results to determine the impact of the added security features on the data rates between the base station(s) subscribers. The results of our proposed WiMax planning approach and the conducted security experiments showed that efficient deployment and coverage plans could be achieved for big cities as well as for rural areas. Copyright © 2009 John Wiley & Sons, Ltd.

This paper describes a framework for addressing the administration of role delegation introduced in the well-known role-based access control model (RBAC). More specifically, this paper describes how a third party, called an agent, can administer the delegation of roles on behalf of a user who is a member of a certain role and wishes to delegate his role to another user who belongs to another role. Furthermore, this paper describes a framework of reference to systematically address the diverse manifestations of the agent-based delegation, such as Role participant agent, non-role participant agent, static, and dynamic types of delegation and introduces an agent-based delegation model that illustrates delegation based on non-role participant delegation.

This paper investigates the impact of security on the performance of WLAN. More specifically, it analyzes the impact of applying encryption used by the well-known security protocol wired equivalent privacy (WEP) on the throughput over an infrastructure WLAN, IEEE 802.11g. This paper also addresses the different issues related to the security protocols currently used in WLAN IEEE 802.11g and demonstrates how these issues affect the final results of the experiments conducted. The main result is that the security adds moderate degradation on the throughput that may affect some applications over infrastructure WLANs.

The advances in the wireless technology, represented by the improved computational capabilities of third generation (3G) and fourth generation (4G) wireless devices and the wider bandwidth wireless networks, make it possible to support a variety of security-sensitive applications such as m-banking and m-commerce. This, however, requires increasingly robust End-to-End security solutions.The contribution of this paper is twofold. First, it presents a design and an implementation of a light weight application-level security solution for handheld devices in wireless LAN. Second, it analyzes the impact of the processing power, time, and memory on the performance of two of the widely known encryption algorithms—RC4 and AES—used by the lightweight handheld devices in the wireless network environment.The work in this paper uses pure Java components to provide end-to-end client authentication and data confidentiality and integrity between wireless J2ME-based clients and J2ME-based servers.

The basic idea behind delegation is that some active entity in a system delegates authority to another active entity in order to carry out some functions on behalf of the former. User delegation in RBAC is the ability of one user (called the delegating user) who is a member of the delegated role to authorize another user (called the delegate user) to become a member of the delegated role. This paper introduces a new model, which we consider it to be an extension of REDM0 [BS2000]. The central contribution of this paper is to introduce a new model, referred to as RBDM1 (role-based delegation model/ hierarchical roles), that uses the details from RBDM0, which was described in the literature by Barka and Sandhu [BS2000] to address the temporary delegation based on hierarchical roles. We formally defined a role-based delegation model based on hierarchical relationship between the roles involved. We also identified the different semantics that impact the can-delegate relation, we analyzed these semantics to determine which ones we consider as more appropriate in business today, thus allowed in our model, and provided a justification to why those selections are made.

The basic idea behind delegation is that some active entity in a system delegates authority to another active entity to carry out some functions on behalf of the former. Delegation in computer systems can take many forms: human to human, human to machine, machine to machine, and perhaps even machine to human. We focuses on the human to human form of delegation using roles. As we show, there are many different ways in which role-based human-to-human delegation can occur. We develop a framework for identifying interesting cases that can be used for building role-based delegation models. This is accomplished by identifying the characteristics related to delegation, using these characteristics to generate possible delegation cases, and using a systematic approach to reduce the large number of cases into few useful cases which can be used to build delegation models.