A large literature is available on quantization for communication efficiency in distributed learning. However, these studies often overlook the enhancement of privacy through quantization. This paper aims to fill this research gap by undertaking a systematic literature review on the use of quantization in distributed learning for privacy enhancement. We explore peer-reviewed literature that utilizes quantization for privacy-preserving purposes. Our analysis identifies the limitations and challenges of current approaches. It also highlights the need to integrate quantization techniques for dual objectives (privacy and communication efficiency) in distributed learning frameworks.

Analyzing packet loss, whether resulting from communication challenges or malicious attacks, is vital for broadcast authentication protocols. It ensures legitimate and continuous authentication across networks. While previous studies have mainly focused on countering Denial of Service (DoS) attacks' impact on packet loss, our research introduces an innovative investigation into packet loss and develops data recovery within variant TESLA protocols. We highlight the efficacy of our proposed hybrid TLI-µTESLA protocol in maintaining continuous and robust connections among network members, while maximizing data recovery in adverse communication conditions. The study examines the unique packet structures associated with each TESLA protocol variant, emphasizing the implications of losing each type on the network performance. We also introduce modifications to variant TESLA protocols to improve data recovery and alleviate the effects of packet loss. Using Java programming language, we conducted simulation analyses that illustrate the adaptability of variant TESLA protocols in recovering lost packet keys and authenticating previously buffered packets, all while maintaining continuous and robust authentication between network members. Our findings also underscore the superiority of the hybrid TLI-µTESLA protocol in terms of packet loss performance and data recovery, alongside its robust cybersecurity features, including confidentiality, integrity, availability, and accessibility. Additionally, we demonstrated the efficiency of our proposed protocol in terms of low computational and communication requirements compared to earlier TESLA protocol variants, as outlined in previous publications.

Digital twins and digital artifacts have become integral components of metaverse platforms, providing users with a rich, immersive, and interactive digital experience through the deployment of diverse digital twins and digital artifacts such as 3D avatars, images, and objects. To date, a significant challenge persists in the lack of practical mechanisms to enable seamless teleportation and cross-metaverse interoperability for these digital twins and digital artifacts. There is also a lack of trusted monetization methods that facilitate trading and leasing of digital twins and digital artifacts. To address these important challenges, this paper proposes a blockchain and Non-Fungible Token (NFT)-based solution that facilitates the integration and teleportation of these digital twins and digital artifacts by providing trusted metadata, verifying ownership, and ensuring the authenticity of digital creations in the virtual world. Key to our solution is the introduction of a bridging mechanism that enables cross-metaverse interoperability, allowing for the portable transfer of NFTs across decentralized metaverse platforms. In addition, our solution focuses on empowering original digital creators by enabling the monetization of their creations through the ownership management capabilities offered by NFTs. To reliably and securely store the metadata and content of tokenized digital twins and digital artifacts, we integrate into our solution the Interplanetary File System (IPFS), a decentralized storage system. To demonstrate the feasibility of our solution, we have developed and deployed all necessary smart contracts that govern the main functionalities and interactions of the proposed system on the Ethereum Goerli Testnet. We present our proposed system architecture, accompanied by informative sequence diagrams, algorithms, and testing details. We discuss how our proposed solution attains the main objectives outlined in the paper. We evaluate our proposed solution in terms of cost and security. We have made the complete source code of our smart contracts publicly available on GitHub.

Gait biometrics which concern with recognizing individuals by the way they walk are of a paramount importance these days. Human gait is a candidate pathway for such identification tasks since other mechanisms can be concealed. Most common methodologies rely on analyzing 2D/3D images captured by surveillance cameras. Thus, the performance of such methods depends heavily on the quality of the images and the appearance variations of individuals. In this study, we describe how gait biometrics could be used in individuals' identification using a deep feature learning and inertial measurement unit (IMU) technology. We propose a model that recognizes the biological and physical characteristics of individuals, such as gender, age, height, and weight, by examining high-level representations constructed during its learning process. The effectiveness of the proposed model has been demonstrated by a set of experiments with a new gait dataset generated using a shoe-type based on a gait analysis sensor system. The experimental results show that the proposed model can achieve better identification accuracy than existing models, while also demonstrating more stable predictive performance across different classes. This makes the proposed model a promising alternative to current image-based modeling.

The rapid integration of IoT devices into everyday decision-making processes underscores the need for continuous user authentication and data integrity checking during network communication, all while minimizing energy consumption to extend device lifespan. This paper introduces the Bio-Integrated Hybrid TESLA protocol, which is a fully symmetric and energy-efficient authentication protocol designed for resource-constrained IoT devices. Based on the Hybrid TLI-lTESLA protocol, this innovative solution prioritizes high cybersecurity levels and minimal computational requirements for continuous authentication. An innovative advancement involves eliminating the public cryptography process during the synchronization stage of TESLA protocols. Instead, biometric authentication through distorted fingerprint and EEG templates is employed, to establish a non-shared symmetric session key, utilized only once. Furthermore, neither the key nor the original biometric templates are transmitted over the network, ensuring user identity preservation and effectively resolving the key distribution challenge inherent in symmetric cryptography. By offloading intensive tasks to servers and avoiding the storage or transmission of biometric data, the proposed approach conserves IoT device energy and enhances cybersecurity. Simulation analyses and cybersecurity assessments demonstrate successful synchronization, privacy preservation, and low computational demands compared to existing protocols, making the Bio-Integrated Hybrid TESLA protocol a significant advancement in IoT authentication.

When deploying a wireless sensor network (WSN) identifying optimal locations for the nodes is challenging, especially so if the environment is either (i) unknown or (ii) obstacle-rich. We proposes BISON (Bio-Inspired Self-Organizing Network), a variant of the Voronoi algorithm to address this challenge. Among the features of the approach are the restrictions placed upon the abilities of the nodes: (i) all information is sensed locally as well as (ii) subject to communication noise. Performance is measured as (i) the percentage of area covered, (ii) the total distance traveled by the nodes, (iii) the cumulative energy consumption and (iv) the uniformity of nodes’ distribution. Obstacle constellations and noise levels are studied systematically and a collision-free recovery strategy for failing nodes is proposed. Results obtained from extensive simulations show the algorithm outperforming previously reported approaches in both, convergence speed, as well as deployment cost.

BACKGROUND: Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the network and to identify possible cancer-related genes. RESULTS: We evaluated the top 15 ranked genes for different cancer types (i.e., Prostate, Breast, and Lung Cancer). The average precisions for identifying breast, prostate, and lung cancer genes vary between 80-100%. On a prostate case study, the system predicted an average of 80% prostate-related genes. CONCLUSIONS: The results show that our system has the potential for improving the prediction accuracy of identifying gene-gene interaction and disease-gene associations. We also conduct a prostate cancer case study by using the threshold property in logistic regression, and we compare our approach with some of the state-of-the-art methods.

Spam has become the platform of choice used by cyber-criminals to spread malicious payloads such as viruses and trojans. In this paper, we consider the problem of early detection of spam campaigns. Collaborative spam detection techniques can deal with large scale e-mail data contributed by multiple sources; however, they have the well-known problem of requiring disclosure of e-mail content. Distance-preserving hashes are one of the common solutions used for preserving the privacy of e-mail content while enabling message classification for spam detection. However, distance-preserving hashes are not scalable, thus making large-scale collaborative solutions difficult to implement. As a solution, we propose Spamdoop, a Big Data privacy-preserving collaborative spam detection platform built on top of a standard Map Reduce facility. Spamdoop uses a highly parallel encoding technique that enables the detection of spam campaigns in competitive times. We evaluate our system's performance using a huge synthetic spam base and show that our technique performs favorably against the creation and delivery overhead of current spam generation tools.

This paper demonstrates how a K-12 educational system can be modeled using system dynamics in order to recommend policies changes with the objective to increase the number of students interested in Science, Technology, Engineering, and Mathematics (STEM) subjects in high schools and beyond, as well as to improve students' performance in STEM subjects. Although we used data and variables from the United Arab Emirates (UAE) education system, the system dynamics model is applicable to many education systems in the world with minor modifications.

Autonomous Mobile agents can be extremely useful in dynamic environments that require a continuous network connection. Network bandwidth reduction, protocol encapsulation, software automation and intelligence gathering can significantly affect Web Services. Integrating mobile agents with Web Services enables software adaptation to cope with a dynamic environment, automated system configuration and application requirement changes that are frequent in today''s ever fast-evolving technology. In this paper, we use a lightweight and efficient composition for mobile agents based Web services complying with Representational State Transfer (REST) principles for agent creation, migration, and control. The paper presents the overall concept and architecture of RESTful agents for web services. A security scheme is proposed for mobile agent security based on an infrastructure-less Identity Based Encryption (IBE) scheme integrated with Broadcast based Secure Mobile Agent Protocol (BROSMAP). A proof- of-concept implementation is provided.

This paper proposes a cluster-based routing pro-tocol for urban Vehicular Ad Hoc Networks (VANETs) using Optimized Link State Routing (OLSR). In OLSR, MultiPoint Relays (MPRs) used for routing, are selected at each node using neighbors' reachability resulting in a high percentage of MPRs. Quality-of-Service (QoS) was introduced to improve MPRs' quality in VANETs while clustering was introduced to reduce MPRs' percentage in dense areas. Relay selection in urban VANETs routing protocols incorporates vehicle mobility metrics, velocity and position whose significance is lowered by abrupt topology change. Our proposed clustering protocol extends the street-centric QoS-OLSR protocol for urban VANETs. QoS and current street are used for cluster head and MPR selection to improve network connectivity, stability, and performance. Simulations conducted using SUMO and NS3 demonstrate that the proposed protocol improves percentage of MPRs, percentage of stability, throughput, packet delivery ratio, hop count and end-to-end delay compared to OLSR and street-centric QoS-OLSR in urban VANET.

In this paper, we address the problem of routing in urban Vehicular ad hoc networks (VANETs) using the proactive Optimized Link State Routing (OLSR) protocol. OLSR selects MultiPoint Relays (MPRs) according to neighbors' reachability index while Quality-of-Service OLSR (QoS-OLSR) for highway VANET considers bandwidth, velocity and distance for MPR selection. For urban VANET, several reactive and position-based protocols were proposed using mobility metrics such as velocity and distance. Both QoS-OLSR and urban VANET protocols depend on basic mobility metrics which rapidly change due to environment restrictions; intersections and street topology. Our proposed street-centric QoS-OLSR protocol for urban VANET is considered as the first attempt to use an urban-based QoS metric for OLSR MPR selection. Link and street centric parameters such as bandwidth, street and lane are utilized in the proposed protocol. Simulations are conducted using the modified NS3 OLSR's implementation to incorporate the QoS metric. Simulation results demonstrate that our proposed QoS-OLSR improves throughput, Packet Delivery Ratio (PDR), average hop count and end-to-end delay compared to OLSR in urban VANET.

The rapid increase of connected devices and the major advances in information and communication technologies have led to great emergence in the Internet of Things (IoT). IoT devices require software adaptation as they are in continuous transition. Multi-agent based solutions offer adaptable composition for IoT systems. Mobile agents can also be used to enable interoperability and global intelligence with smart objects in the Internet of Things. The use of agents carrying personal data and the rapid increasing number of connected IoT devices require the use of security protocols to secure the user data. Elliptic Curve Cryptography (ECC) Algorithm has emerged as an attractive and efficient public-key cryptosystem. We recommend the use of ECC in the proposed Broadcast based Secure Mobile Agent Protocol (BROSMAP) which is one of the most secure protocols that provides confidentiality, authentication, authorization, accountability, integrity and non-repudiation. We provide a methodology to improve BROSMAP to fulfill the needs of Multi-agent based IoT Systems in general. The new BROSMAP performs better than its predecessor and provides the same security requirements. We have formally verified ECC-BROSMAP using Scyther and compared it with BROSMAP in terms of execution time and computational cost. The effect of varying the key size on BROSMAP is also presented. A new ECC-based BROSMAP takes half the time of Rivest-Shamir-Adleman (RSA) 2048 BROSMAP and 4 times better than its equivalent RSA 3072 version. The computational cost was found in favor of ECC-BROSMAP which is more efficient by a factor of 561 as compared to the RSA-BROSMAP.

Mobile agents are smart programs that migrate from one platform to another to perform the user task. Mobile agents offer flexibility and performance enhancements to systems and service real-time applications. However, security in mobile agent systems is a great concern. In this paper, we propose a novel Broadcast based Secure Mobile Agent Protocol (BROSMAP) for distributed service applications that provides mutual authentication, authorization, accountability, nonrepudiation, integrity, and confidentiality. The proposed system also provides protection from man in the middle, replay, repudiation, and modification attacks. We proved the efficiency of the proposed protocol through formal verification with Scyther verification tool.

The new global economy has a great potential to shift economic power on a massive level resulting in a new and growing digital divide in the world. Over the past few decades, computers have transformed both the world and the workforce in many profound ways. As a result, computer science and associated technologies now lie at the heart of smart economies worldwide. Many reports around the world state that failure to teach Computer Science and associated technologies in the Digital Age will be disastrous. This paper summarizes the recently developed comprehensive standards and performance criteria for K-12 computer science and technology education designed to strengthen computer science fluency and competency throughout primary and secondary Schools in UAE. The paper presents a comprehensive overview of the up-to-date trends in computer science education worldwide and then demonstrates the adaptation of such practices to the design of a fully customized standards document that fit UAE culture and vision regarding transformation to knowledge based economy, innovation, and entrepreneurship.

This paper aims to measure students' level of interest in Science, Technology, Engineering, and Mathematics (STEM) in the United Arab Emirates (UAE), and the main focus is on students in grades 9 to 12. Essentially, the goal is to understand the factors behind students' choice of STEM or non-STEM academic tracks. Public and private school students in STEM and non-STEM academic programs participated in the research through responding to surveys gauging students' outlooks on what influenced their choices of going to, or away from, STEM. The data collected revealed a number of reasons that make students like, or dislike, scientific majors. These reasons include the presence or absence of capable teachers, the choice of the teaching language and the impact of professional STEM role models. This paper also focuses on the differences in results between public and private institutions, male and female students, as well as nationals and non-nationals. Furthermore, it compares the findings to similar research done in other countries, within the Arab region and beyond. We show that several factors remain valid in most countries and education systems whereas others are specific to each of them. Finally, the paper makes recommendations on how to build upon the perceived successes and downfalls of the research's methodology and it suggests methods on how to increase successful STEM enrollment and competency.

New studies in the United Arab Emirates (UAE) have investigated the factors why students do or do not appreciate Science, Technology, Engineering, and Mathematics (STEM) courses. Many indicators point mainly to the effectiveness, or ineffectiveness, of teachers. The main purpose of this paper is to identify significant factors that affect teachers' effectiveness. This is done through an analysis of the results of a twenty-four-question survey, developed by the authors, given to 200 STEM teachers, from Kindergarten to Grade 12, in public and private schools across the country. A literature review consisting of comparable studies in other countries served as a backdrop to the many trends discovered in the analysis of teachers' responses. Furthermore, the authors provide additional critical interpretations to address the unique nature of the findings in the UAE educational system. Overall, the findings point to the dire need to address teachers' dissatisfaction with the teaching profession in the UAE. Specifically, addressing monetary compensation, improving the curricula, lack of resources and providing professional guidance via development courses and seminars are necessary if teachers are to be more effective in the classroom. The paper also provides recommendations that can improve the current conditions, not only in the UAE, but also in other regions of the world being affected by the same.

In this paper, we address the problem of MultiPoint Relay (MPR) node disconnection due to mobility in Vehicular ad-hoc networks (VANETs) using the cluster-based Quality of Service Optimized Link State Routing (QoS-OLSR) protocol. The protocol uses MPR nodes to establish communication among the clusters. MPR disconnection represents a major challenge in VANETs, due to the frequent change in the network topology. Consequently, the performance of the routing protocol will be weakened as it adversely affects the connectivity level of the network. Thus, our solution is a new cluster-based QoS-OLSR protocol based on intelligent water drop algorithm that is capable of (1) selecting the best set of MPR in terms of QoS (2) dealing with the MPR disconnection as it selects alternatives to assure a connected network (3) maintaining a reliable MPR failure management process. Simulation results demonstrate that the proposed model succeeds in improving the network connectivity and stability, reducing both the overhead and the path length, and increasing the packet delivery ratio compared to the original QoS-OLSR.

Wafer defects, which are primarily defective chips on a wafer, are of the key challenges facing the semiconductor manufacturing companies, as they could increase the yield losses to hundreds of millions of dollars. Fortunately, these wafer defects leave unique patterns due to their spatial dependence across wafer maps. It is thus possible to identify and predict them in order to find the point of failure in the manufacturing process accurately. This paper introduces a novel simplified subspaced regression framework for the accurate and efficient identification of defect patterns in semiconductor wafer maps. It can achieve a test error comparable to or better than the state-of-the-art machine-learning (ML)-based methods, while maintaining a low computational cost when dealing with large-scale wafer data. The effectiveness and utility of the proposed approach has been demonstrated by our experiments on real wafer defect datasets, achieving detection accuracy of 99.884% and R2of 99.905%, which are far better than those of any existing methods reported in the literature.

In this paper we study the interest of students in the United Arab Emirates (UAE) from grade 9 to 12 in Science, Technology, Engineering, and Mathematics (STEM). Surveys were distributed to students who chose STEM tracks and students who chose non-STEM tracks in public and private schools, as well as universities, across the country. The data collected revealed a number of reasons that make students like, or dislike, scientific majors. These reasons include the presence or absence of capable teachers and the choice of the teaching language. The results presented in the paper also focus on differences between public and private institutions, male and female students, as well as nationals and non-nationals. We also compare our findings to similar research done in the USA We show that several factors remain valid in both countries whereas others are specific to each of them.

New studies in the United Arab Emirates (UAE) have delved into investigating the factors why students do or do not appreciate Science, Technology, Engineering, and Mathematics (STEM) courses. Many indicators point mainly to the effectiveness, or ineffectiveness, of teachers. The main purpose of this paper is to identify significant factors that affect a teacher's effectiveness. This is done through an analysis of the results of a twenty-four-question survey, developed by the authors, given to 200 Science, Technology, and Mathematics teachers, from Kindergarten to Grade 12, in public and private schools across the country. A literature review consisting of comparable studies in other countries served as a backdrop to the many trends discovered in the analysis of teachers' responses. Furthermore, the authors provide additional critical interpretations to address the unique nature of the findings in the UAE educational system. Overall, the findings point to the dire need to address teachers' dissatisfaction with the teaching profession in the UAE. Specifically, addressing monetary compensation, improving the curricula, lack of resources and providing professional guidance via development courses and seminars is necessary if teachers are to be more effective in the classroom.

A bot is a piece of software that is usually installed on an infected machine without the user’s knowledge. A bot is controlled remotely by the attacker under a Command and Control structure. Recent statistics show that bots represent one of the fastest growing threats to our network by performing malicious activities such as email spamming or keylogging. However, few bot detection techniques have been developed to date. In this paper, we investigate a behavioural algorithm to detect a single bot that uses keylogging activity. Our approach involves the use of function calls analysis for the detection of the bot with a keylogging component. Correlation of the frequency of function calls made by the bot with other system signals during a specified time-window is performed to enhance the detection scheme. We perform a range of experiments with the spybot. Our results show that there is a high correlation between some function calls executed by this bot which indicates abnormal activity in our system.

Ensuring the security of computers is a non-trivial task, with many techniques used by malicious users to compromise these systems. In recent years a new threat has emerged in the form of networks of hijacked zombie machines used to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These zombie machines are said to be infected with a ‘hot’ - a malicious piece of software which is installed on a host machine and is controlled by a remote attacker, termed the ‘botmaster of a botnet’. In this work, we use the biologically inspired Dendritic Cell Algorithm (DCA) to detect the existence of a single hot on a compromised host machine. The DCA is an immune-inspired algorithm based on an abstract model of the behaviour of the dendritic cells of the human body. The basis of anomaly detection performed by the DCA is facilitated using the correlation of behavioural attributes such as keylogging and packet flooding behaviour. The results of the application of the DCA to the detection of a single hot show that the algorithm is a successful technique for the detection of such malicious software without responding to normally running programs.

Internet worms have become a major threat to the Internet due to their ability to rapidly compromise large numbers of computers. In response to this threat, there is a growing demand for effective techniques to detect the presence of worms and to reduce the worms' spread. Furthermore, existing approaches for anomaly detection of new worms suffer from scalability problems. In this paper, we present an approach for detecting worms based on similar patterns of connection activity. We then investigate how to improve the computational efficiency of worm detection by presenting a greedy algorithm, which minimizes the amount of traffic processing needed to detect worms, thus increasing the scalability of the system. Our evaluation shows that the greedy algorithm not only achieved high detection accuracy and reduced the amount of processing time to detect worms, but also achieved reasonable worm traffic detection in the early stages of an outbreak.