Mabrook Al-Rakhami

A framework for cloud-based healthcare services to monitor noncommunicable diseases patient

Monitoring patients who have noncommunicable diseases is a big challenge. These illnesses require a continuous monitoring that leads to high cost for patients’ healthcare. Several solutions proposed reducing the impact of these diseases in terms of economic with respect to quality of services. One of the best solutions is mobile healthcare, where patients do not need to be hospitalized under supervision of caregivers. This paper presents a new hybrid framework based on mobile multimedia cloud that is scalable and efficient and provides cost-effective monitoring solution for noncommunicable disease patient. In order to validate the effectiveness of the framework, we also propose a novel evaluation model based on Analytical Hierarchy Process (AHP), which incorporates some criteria from multiple decision makers in the context of healthcare monitoring applications. Using the proposed evaluation model, we analyzed three possible frameworks (proposed hybrid framework, mobile, and multimedia frameworks) in terms of their applicability in the real healthcare environment.

Sybil Defense Techniques in Online Social Networks: A Survey

The problem of malicious activities in online social networks, such as Sybil attacks and malevolent use of fake identities, can severely affect the social activities in which users engage while online. For example, this problem can affect content publishing, creation of friendships, messaging, profile browsing, and commenting. Moreover, fake identities are often created to disseminate spam, use the private information of other users, commit fraud, and so on. A malicious person can generate numerous fake accounts for these purposes to reach a large number of trustworthy users. Thus, these types of malicious accounts must be detected and deactivated as quickly as possible. However, this objective is challenging, because a fake account can exhibit trustworthy behaviors and have a type of name that will prevent it from being detected by the security system. In this paper, we provide a comprehensive survey of literature from 2006 to 2016 on Sybil attacks in online social networks and use of social networks as a tool to analyze and prevent these attack types. We first review existing Sybil attack definitions, including those in the context of online social networks. We then discuss a new taxonomy of Sybil attack defense schemes and methodologies. Finally, we compare the literature and identify areas for further research in Sybil attacks in online social networks.

A Multistage Credibility Analysis Model for Microblogs

Currently, microblogs such as the well-known social network Twitter are one of the most important sources of information in an era of information overload, restiveness and uncertainty. Consequently, developing models to verify information from Twitter has become both a challenging and necessary task. In this paper, we propose a novel multi-stage credibility analysis framework to identify implausible content in Twitter in order to prevent the proliferation of fake or malicious information. We used Naive Bayes classifier and it is enhanced by considering the relative importance of the used features to improve the classification accuracy. We examine the classifier with 1000 unique tweets along with 700 account. The result quite motivating with accuracy 90.3%, 86.24% Precision and 98.8% recall.

An efficient key agreement protocol for Sybil-precaution in online social networks

Abstract Identifying malicious users in online social networks (OSNs) is a challenging task that demands a great deal of skill and knowledge because these users can have multiple forms: Sybils, bots, spammers, phishers, impersonations or fake accounts. Different types of research methodologies have been proposed to solve this problem; hence, there are varied solutions. Most of the work on OSNs has focused on trust, distrust to detect and preventing these types of attacks. Some researchers have found that a suspected node can generate private/public keys to prevent its identity from being stolen by an adversary; however, they have not explained how these keys are generated and managed. We propose a new and efficient centralized key management protocol to prevent Sybil attack and to provide a secure communication service among users in OSNs. The core tenet of this method is the existence of a ‘roadblock’ that any user intending to join a group must go through, which includes a task that only a human user can accomplish. Hence, automatically controlled accounts are prevented from joining, and the group will consist only of users that have been confirmed as genuine. The mechanism is very effective in recognizing bot accounts, which enables it to guard the network against malicious behavior by fake accounts.

Cloud-based Graphical Simulation Tool of ECG for Educational Purpose

One of the most significant techniques for determining the prevalence of cardiovascular diseases involves the use of the Electrocardiogram (ECG). However, education on ECG has often been provided in a manner that can be described to be old-fashioned. The old-fashioned style involves the use of didactic teaching techniques as well as the incorpofiguration of ECG model strips available in the literature or textbooks. Nevertheless, the current explosion in the utilization of the Internet has made multimedia educational websites as well as knowledge sharing platforms popular. This paper proposes a novel cloud-based simulation tool that can help healthcare students to learn and interpret ECG signals as well as cases. This simulation tool works by constructing an interactive 2D simulator of ECG signals. Even though it is regarded to be a mere simulation tool, it has the capacity to provide teachers and students in the healthcare industry with the experiences derived from the real ECG signals. From this study, it is evident that the simulation tool enhances the educational process of ECG signals. The results and discussions in this study aim at complementing the traditional tools used in the learning process and deploying the new simulation tool in various educational organizations such as e-learning sites, medical colleges, and healthcare institutes.

A Credibility Assessment Model for Online Social Network Content

Online social networks such as Twitter are among the most important sources of information in the current era of information overload, restiveness, and uncertainty. Therefore, it is necessary to develop a model for verifying information from Twitter, which is a challenging task. We propose a new credibility assessment model for identifying implausible content on Twitter to prevent the proliferation of false/malicious information. The proposed model consists of six integrated components operating in an algorithmic form to assess the credibility of tweets. We enhanced our classifier by weighting features extracted from tweets according to their relative importance. Further, we applied our model to two different datasets created from 155,794 unique accounts. To evaluate the performance of our model, we trained two naive Bayes models, M1 (without relative importance algorithm) and M2 (with relative importance algorithm). The results were quite encouraging: M2 achieved accuracies of 82.25 and 85.47% on the two datasets.

CredFinder: a real-time tweets credibility assessing system

Lately, Twitter has grown to be one of the most favored ways of disseminating information to people around the globe. However, the main challenge faced by the users is how to assess the credibility of information posted through this social network in real time. In this paper, we present a real-time content credibility assessment system named CredFinder, which is capable of measuring the trustworthiness of information through user analysis and content analysis. The proposed system is capable of providing a credibility score for each users tweets. Hence, it provides users with the opportunity to judge the credibility of information faster. CredFinder consists of two parts: a frontend in the form of an extension to the Chrome browser that collects tweets in real time from a Twitter search or a user-timeline page and a backend that analyzes the collected tweets and assesses their credibility.