Muhammad Al-Qurishi

Evaluating the impact of a cloud-based serious game on obese people

This paper describes the process of monitoring obese people through a cloud-based serious game that promotes them to engage in physical exercises in a playful manner. The monitoring process focuses on obtaining various health and exercise-related parameters of obese during game-play, such as heart rate, weight, step count and calorie burn, which contributes to their weight loss. While the obese are engaged in the game session, therapists/caregivers can access their health data anytime, anywhere and from any device to change the game complexity level and accordingly provide on the spot recommendation. In our study, we evaluate how the different physical activities performed through this game impact their cognitive behavior in terms of attention, relevance, confidence and satisfaction. The evaluation was based on the participation of 150 undergraduate obese and overweight students who were asked to play the game and fill a questionnaire after game-play. The data analysis conducted on their feedback showed that they were self-aware and motivated to play the game for weight loss.

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.

A cloud-based serious games framework for obesity

Obesity is becoming an outstanding public health issue in most countries of the world. One innovative approach to address this problem is utilizing serious games which combine exercise and gaming in more attractive manners. While attempts have been made to use gaming to tackle obesity, existing work generally fail to provide the full potential of pervasive exergaming (i.e. anywhere, anytime and from any device) with activity recommendation, evaluation, and in many cases even a suitable context (i.e. temporal, spatial, and vital sign parameters) tracking mechanism. In this paper, we propose a cloud computing-based pervasive serious games framework that can address above issues effectively and thus augments current obesity treatment methods and empower therapists and patients with a comprehensive process of continuous treatment and long-term management of obesity. The design and implementation of the proposed platform for obesity is described and illustrated through the description of a sample serious game.

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.

Cloud-based mobile IPTV terminal for video surveillance

Surveillance video streams monitoring is an important task that the surveillance operators usually carry out. The distribution of video surveillance facilities over multiple premises and the mobility of surveillance users requires that they are able to view surveillance video seamlessly from their mobile devices. In order to satisfy this requirement, we propose a cloud-based IPTV (Internet Protocol Television) solution that leverages the power of cloud infrastructure and the benefits of IPTV technology to seamlessly deliver surveillance video content on different client devices anytime and anywhere. The proposed mechanism also supports user-controlled frame rate adjustment of video streams and sharing of these streams with other users. In this paper, we describe the overall approach of this idea, address and identify key technical challenges for its practical implementation. In addition, initial experimental results were presented to justify the viability of the proposed cloud-based IPTV surveillance framework over the traditional IPTV surveillance approach.

Health monitoring of obese people through a cloud-based serious game framework

This paper proposes a mechanism to monitor in real-time various heath conditions of obese people through a cloud-based serious game framework. We integrate body sensors and physical activity sensors within this gaming framework. Using a Treasure Hunting serious game scenario, we monitor various health and exercise related parameters of obese people during exercise such as heart rate, step count and calorie burn, which help them to become self-aware and motivated for weight loss. Besides, the therapists/caregivers are able to access the health data anytime, anywhere and from any device using our system to provide on the spot recommendations. In our experiment, we show how the proposed system monitors the obese while capturing their health-related data and guides them to improve their obesity stature.

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.

A novel prevention mechanism for Sybil attack in online social network

In Online Social Network (OSN) one of the major attacks is Sybil attack, in which an attacker subverts the system by creating a large number of pseudonymous identities (i.e. fake user accounts) and using them to establish as many as possible of friendships with honest users to have disproportionately large influence in the network. Finally, the Sybil accounts led to many malicious activities in the online social network. To detect these kinds of fake accounts in online social network is a big challenge. In this paper, we propose a prevention mechanism for Sybil attack in OSN based on pairing and identity-based cryptography. During the formation of a group when any user wants to join the group, a user needs to pass a trapdoor which is built based on pairing-based cryptography and consists of a challenge and response mechanism (process). Only the authenticated users can pass the trapdoor and the fake users cannot pass the process, therefore, exclusively the genuine users can join a group. Thus, the Sybil nodes would not be able to join the group and the Sybil attack would be prevented in the OSN.

SybilTrap: A graph-based semi-supervised Sybil defense scheme for online social networks

Sybil attacks are increasingly prevalent in online social networks. A malicious user can generate a huge number of fake accounts to produce spam, impersonate other users, commit fraud, and reach many legitimate users. For security reasons, such fake accounts have to be detected and deactivated immediately. Various defense schemes have been proposed to deal with fake accounts. However, most identify fake accounts using only the structure of social graphs, leading to poor performance. In this paper, we propose a new and scalable defense scheme, SybilTrap. SybilTrap uses a semi‐supervised technique that automatically integrates the underlying features of user activities with the social structure into one system. Unlike other machine learning–based approaches, the proposed defense scheme works on unlabeled data, and it is effective in detecting targeted attacks, because it manipulates different levels of features of user profiles. We evaluate SybilTrap on a dataset collected from Twitter. We show that our proposed scheme is able to accurately detect Sybil nodes as well as huge conspiracies among them.