Showing papers by "Yaser Jararweh published in 2021"

A Survey on Blockchain for Information Systems Management and Security

Abstract: Blockchain technologies have grown in prominence in recent years, with many experts citing the potential applications of the technology in regard to different aspects of any industry, market, agency, or governmental organizations. In the brief history of blockchain, an incredible number of achievements have been made regarding how blockchain can be utilized and the impacts it might have on several industries. The sheer number and complexity of these aspects can make it difficult to address blockchain potentials and complexities, especially when trying to address its purpose and fitness for a specific task. In this survey, we provide a comprehensive review of applying blockchain as a service for applications within today’s information systems. The survey gives the reader a deeper perspective on how blockchain helps to secure and manage today information systems. The survey contains a comprehensive reporting on different instances of blockchain studies and applications proposed by the research community and their respective impacts on blockchain and its use across other applications or scenarios. Some of the most important findings this survey highlights include the fact that blockchain’s structure and modern cloud- and edge-computing paradigms are crucial in enabling a widespread adaption and development of blockchain technologies for new players in today unprecedented vibrant global market. Ensuring that blockchain is widely available through public and open-source code libraries and tools will help to ensure that the full potential of the technology is reached and that further developments can be made concerning the long-term goals of blockchain enthusiasts.

Blockchain-Enhanced Data Sharing With Traceable and Direct Revocation in IIoT

Abstract: The industrial Internet of Things (IIoT) supports recent developments in data management and information services, as well as services for smart factories. Nowadays, many mature IIoT cloud platforms are available to serve smart factories. However, due to the semicredibility nature of the IIoT cloud platforms, how to achieve secure storage, access control, information update and deletion for smart factory data, as well as the tracking and revocation of malicious users has become an urgent problem. To solve these problems, in this article, a blockchain-enhanced security access control scheme that supports traceability and revocability has been proposed in IIoT for smart factories. The blockchain first performs unified identity authentication, and stores all public keys, user attribute sets, and revocation list. The system administrator then generates system parameters and issues private keys to users. The domain administrator is responsible for formulating domain security and privacy-protection policies, and performing encryption operations. If the attributes meet the access policies and the user's ID is not in the revocation list, they can obtain the intermediate decryption parameters from the edge/cloud servers. Malicious users can be tracked and revoked during all stages if needed, which ensures the system security under the Decisional Bilinear Diffie–Hellman (DBDH) assumption and can resist multiple attacks. The evaluation has shown that the size of the public/private keys is smaller compared to other schemes, and the overhead time is less for public key generation, data encryption, and data decryption stages.

Federated Learning in Vehicular Networks: Opportunities and Solutions

Abstract: The emerging advances in personal devices and privacy concerns have given the rise to the concept of Federated Learning. Federated Learning proves its effectiveness and privacy preservation through collaborative local training and updating a shared machine learning model while protecting the individual data-sets. This article investigates a new type of vehicular network concept, namely a Federated Vehicular Network (FVN), which can be viewed as a robust distributed vehicular network. Compared to traditional vehicular networks, an FVN has centralized components and utilizes both DSRC and mmWave communication to achieve more scalable and stable performance. As a result, FVN can be used to support data-/computation-intensive applications such as distributed machine learning and Federated Learning. The article first outlines the enabling technologies of FVN. Then, we briefly discuss the high-level architecture of FVN and explain why such an architecture is adequate for Federated Learning. In addition, we use auxiliary Blockchain-based systems to facilitate transactions and mitigate malicious behaviors. Next, we discuss in detail one key component of FVN, a federated vehicular cloud (FVC), that is used for sharing data and models in FVN. In particular, we focus on the routing inside FVCs and present our solutions and preliminary evaluation results. Finally, we point out open problems and future research directions of this disruptive technology.

A Blockchain-empowered Access Control Framework for Smart Devices in Green Internet of Things

Abstract: Green Internet of things (GIoT) generally refers to a new generation of Internet of things design concept. It can save energy and reduce emissions, reduce environmental pollution, waste of resource...

Fake Reviews Detection: A Survey

Abstract: In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.

Enabling Intelligent IoCV Services at the Edge for 5G Networks and Beyond

Abstract: The Fifth Generation (5G) communication technology has paved the way for intelligent and diversified Internet of Connected Vehicles (IoCV) services that meet stringent Quality of Service (QoS) requirements Both Artificial Intelligence (AI) and Blockchain are playing and will continue to play an imperative role in providing secure and decentralized resource sharing to solve complex and time-sensitive problems at the edge The integration of both those techniques will enhance the performance of smart vehicular services, especially in beyond 5G (B5G) networks Ensuring secure transactions in complex autonomous network architectures is an immense challenge This article addresses computational, storage, connectivity and intelligence concerns using a collaborative approach to engage multiple Internet of Things (IoT) nodes such as connected- vehicles, drones and mobile devices for the provisioning of QoS-optimal complex service compositions in autonomous mobile networks Continuous and fast compositions emerge using decentralized decisions and interactions with diversified neighboring nodes with the aid of reinforcement learning Blockchain is used to ensure that nodes interact with each other verifiably and record transactions without the need for trusted intermediaries We assess whether having an AI-enabled blockchain collaborative composition solution improves service availability and delivery of smart city vehicular services

Enhancing human activity recognition using deep learning and time series augmented data

Abstract: Human activity recognition is concerned with detecting different types of human movements and actions using data gathered from various types of sensors. Deep learning approaches, when applied on time series data, offer promising results over intensive handcrafted feature extraction techniques that are highly reliant on the quality of defined domain parameters. In this paper, we investigate the benefits of time series data augmentation in improving the accuracy of several deep learning models on human activity data gathered from mobile phone accelerometers. More specifically, we compare the performance of the Vanilla, Long-Short Term Memory, and Gated Recurrent Units neural network models on three open-source datasets. We use two time series data augmentation techniques and study their impact on the accuracy of the target models. The experiments show that using gated recurrent units achieves the best results in terms of accuracy and training time followed by the long-short term memory technique. Furthermore, the results show that using data augmentation significantly enhances recognition quality.

Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture

Abstract: Nowadays, the economy of countries highly depends on the agriculture productivity which has a great effect on the development of human civilization. Sometimes, plant diseases cause a major reduction in agricultural products. This paper proposes a new approach for the automatic detection and classification of plant leaf diseases based on using the ELM deep learning algorithm on a real dataset of plant leaf images. The proposed approach uses the k-means clustering algorithm for image segmentation and applies the GLCM for feature extraction. The BDA optimization algorithm is employed for feature selection, and lastly the ELM algorithm is used for plant leaf diseases classification. The presented approach optimizes the input weights and hidden biases for ELM. The dataset used in this study includes 73 plant leaf images, such that we tested our approach on four diseases that usually affect plants, including: Alternaria alternata, Anthracnose, Bacterial blight, and Cercospora leaf spot. The experimental results show that the proposed approach has achieved encouraging results in terms of these classification measures: accuracy, error rate, recall, F score, and AUC which are 94%, 6%, 92%, 95%, and 96% respectively. Babu

DDoS detection in 5G-enabled IoT networks using deep Kalman backpropagation neural network

Abstract: The fifth-generation (5G) wireless communication systems associating with the high achievable data-transfer speeds will significantly affect the performance of IoT networks. On one hand, the internet goes through a dramatic transaction period that shapes every aspect of our lives, industry, and business where cloud computing, smart cities, and the Internet of Things (IoT) play a significant role in the advancement of data transfer, storing, and processing. On the other hand, it plays a significant role in emerging advanced versions of different types of cybersecurity attacks especially that are novel, hard-to-detect, and that of distributive never cease-fire characteristics. To mitigate these concerns, we present a distributed denial-of-service (DDoS) intrusion detection model that can be implemented in IoT dynamic environments, providing an intelligent intrusion detection mechanism against the second biggest threat to data traffic and transfer on IoT networks. Kalman backpropagation neural network-based DDoS intrusion detection is proposed in this work. The framework is validated through various simulations via the most up to date CICDDoS2019 dataset to demonstrate the effectiveness of the solution in terms of intrusion detection. the proposed solution achieved an average detection accuracy of 94% with 0.0952 false alarm rate and 97.49%, 91.22% for detection rate, and precision respectively.

An Incentive-based Mechanism for Volunteer Computing Using Blockchain

Abstract: The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed, and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.

Sentiment Analysis Based on Deep Learning Methods for Explainable Recommendations with Reviews

Abstract: Explainable recommendation systems have gained much attention in the last few years. Most of them use textual reviews to provide users with interpretability about why services or products are liked by users or recommended for them. Sentiment analysis has potential advantages to determine the attitudes of users in online communities using websites such as Twitter, Facebook, and YouTube. However, sentiment analysis of textual reviews in explainable recommendation systems seems to be a really challenging task. In this paper, we present a deep learning-based architecture for sentiment analysis to automatically predict the sentiment of reviews, which are considered as explanations of recommendations. It consists of two instances of the prediction model, one with the Long Short-Term Memory (LSTM) method and the other with the Gated Recurrent Unit (GRU) method. We evaluate their performance on one real-world dataset from Amazon and compare them with one state-of-the-art method. The experimental results show that our methods perform better than the baseline approach.

GDBApex: A graph‐based system to enable efficient transformation of enterprise infrastructures

Abstract: Graph‐based database engines have been developed by different researchers and companies. Many optimization methods have been integrated within these engines to enable fast and efficient data processing. However, many small‐ and medium‐size organizations have not changed their database infrastructures and still rely on a relational management modeling approach. This limits their service performance, especially in today's large‐scale data processing requirements. Transformation to the use of graph‐based modeling and design is not a straightforward process. In order to make a successful transformation, correct process semantics as well as the design of vertices, edges, labels, and process relations are required. The goal of this article is to help small‐ and medium‐size organizations make this transformation successful in order to satisfy customers' expectations and meet the requirements of data‐intensive applications. The proposed graph‐based modeling approach uses a graph structure for semantic queries and applies software engineering design principles. Moreover, it provides a case study with many data transactions. The system outperformed relational database management systems by an order of magnitude. Scalability of the system is examined and compared with the regular relational‐based modeling. In addition, a load balancing solution is used to achieve high scalability.

Building an Intelligent Global IoT Reputation and Malicious Devices Detecting System

Abstract: The Internet of Things (IoT) applications are growing immensely. However, malicious IoT devices are major concerns that threaten the security of IoT applications. This paper proposes an intelligent reputation system for IoT devices using edge computing and cloud computing infrastructures. The proposed system can be used to mitigate the effect of malicious and malfunction IoT devices. Therefore, the proposed system can be used to enhance the effectiveness of IoT based systems such as smart cities, and reduce the risk of malicious IoT devices especially in sensitive systems, such as military applications, that leverage IoT devices. To achieve this goal, the paper proposes a new identification method for uniquely and globally identifying IoT devices wherever they move. Moreover, the paper proposes a new approach for computing the reputation of IoT devices, and calculating correct values based on these reputations. The results show that the proposed approach achieves very good results in detecting malicious IoT devices and computing very close values to the true values.

Effective peer-to-peer routing in heterogeneous half-duplex and full-duplex multi-hop cognitive radio networks

Abstract: This paper proposes an adaptive routing and spectrum assignment protocol for heterogeneous Full-duplex (FD) and half-duplex (HD) cognitive radio (CR) networks. The key design goal of the developed protocol is to select a path between a source-destination pair, and to optimally assign channels to each hop along the selected path. One main feature of the proposed protocol is that it takes into account the dynamic activity of Primary Users (PUs) over radio channels. To that end, radio channels are characterized in terms of their average availability-time, and the proposed protocol selects the channels with maximum availability-time. Another main feature of the proposed protocol is considering network heterogeneity, where the connected devices may have HD or FD transmission capabilities. The proposed protocol follows a segmentation strategy that relies on the existence of HD nodes. It choses the path with least number of path-segments (and hence minimum HD nodes) to minimize the likelihood of time-shared transmissions, and hence improves network throughput. Compared to existing FD-based schemes, simulation results show that the proposed routing scheme provides considerable enhancement on the overall network performance.

Empirical Evaluation of Energy Consumption for Mobile Applications

Abstract: The study of software energy consumption is gaining more importance due to the wildly increasing use of resource limited portable devices that run on batteries, in addition to the economical and environmental concerns. Mobile hardware has been mostly well optimized on their energy consumption, but that cannot be said for mobile applications. Studying the energy consumption of applications requires investigating the amount of energy consumed at a granule level (e.g., method calls), and therefore, identifying the leaks which are responsible for peaks in energy consumed by an application. In this paper, we performed an empirical measurement of energy consumption for 10 Android applications using a software-based tool called PETRA. We reported and compared the energy consumed by method calls by the test cases. The study reveals that there are clear variations on the average energy consumption in the studied applications and are ranging from 0.25 Joule/second to 1.25 Joule/second. Moreover, the study revealed that the relative high average energy consumption in is associated with some frequently called methods by the test cases. These methods are identified and reported as energy hotspots. These findings could help practitioners to minimize the energy pattern by applying refactoring techniques during software maintenance.

Social Network Analysis of the Panama Papers Concentrating on the MENA Region

Abstract: The release of millions of financial documents, which has been known as the ‘WikiLeaks’ of the financial world (a.k.a. ‘Panama Papers’), has dragged global attention in how highly structured means applied by some of the elite to conceal their financial assets. Consequently, significant financial corruption allegations were raised. We concentrate on a somewhat overlooked region, the Middle East and North Africa (MENA) region. This study aims to use social network analytics to study the information contained in these documents. We are checking the major players in the MENA’s trends and patterns to determine if it matches the known economic powers. The analysis reveals that while the constructed network enjoys some typical characteristics, many interesting observations and properties are worth discussing. Specifically, using the extracted network consisting of 62 987 nodes and 84 692 edges, our social network analysis finding shows that, perhaps surprisingly, the nodes or the social network are not necessarily directly correlated with perceived economic influence.

A conflict‐free replicated data type for collaborative annotation systems

Abstract: With the advent of Web 2.0, numerous collaborative annotation systems have been developed in an effort to enable distant users to annotate the same multimedia resources such as texts, audio, images, and videos. However, the existing systems do not support the semantic aspect of the data available on the Web and ignore the convergence aspect when executing concurrent annotations. Based on the technologies of Semantic Web, this article presents a new conflict‐free replicated data type called OAC‐Set, which extends Open Annotation Collaboration data model to enable concurrent annotations while guaranteeing convergence, causality, and intention preservation criteria. The experimental results show that our approach is efficient and effective.