Virtualization is a transformative technology that enables multiple virtual machines to operate concurrently on a single physical host, thereby rationalizing resource utilization in cloud data centers. These data centers consist of multiple servers and consume a significant amount of energy, which requires cloud providers to have mechanisms that optimize the placement of virtual machines in these servers. An optimal placement reduces energy consumption and improves resource utilization rate. In this paper, we introduce a novel algorithm based on a recent bio-inspired metaheuristic called Manta Ray Foraging Optimization (MRFO) to solve the virtual machine placement (VMP) problem. Although MRFO has been successfully applied to various engineering optimization tasks, to the best of our knowledge, it has never been used in the context of VMP. Accordingly, this paper investigates for the first time an enhanced version of the MRFO algorithm adapted for VMP problem in cloud data centers. The proposed algorithm is evaluated with CloudSim toolkit under various performance metrics, including energy consumption, resource utilization, and number of active servers, under several cloud environment heterogeneity levels. Simulation results are compared with several baseline and hybrid algorithms widely adopted in this field. The proposed algorithm achieves an improvement of 11.53

Forecasting crowd congestion is a critical aspect of crowd management, particularly in dynamic and densely populated areas, such as urban centers, events, or pilgrimage sites. In this paper, we proposed the first crowd congestion forecasting framework for the pilgrimage of Umrah. We addressed the crowd congestion forecasting problem by clustering the crowd flow trajectory in Masjid Al-Haram (Great Mosque) in the city of Makkah into six zones. The framework consists of two main components: 1) Ensemble forecasting model that aims at forecasting the crowd density of Masjid Al-Haram and its six zones, and 2) decision making algorithm that aims at keeping the crowd density at an acceptable level, and recommends updating the crowd flows when the forecasted crowd density exceeds the crowd density threshold. We built the ensemble learning model in three phases. In the first phase, we selected and evaluated different learning base models, including ARIMA, Sequence to Sequence (Seq2Seq) learning, M-1D-CNN-LSTM, and DeepSTN. In the second phase, the best three models, which performed well in the first phase, are selected to build the stacked ensemble model. The latter is validated using the walk-forward technique in the third phase. To evaluate the framework, we built a crowd dataset based on two temporal properties: 1) hourly context and 2) daily context. We evaluated the three phases of the ensemble forecasting model. In the first phase, DeepSTN performs the best by achieving a Mean Absolute Error (MAE) of 0.281. The results also indicate that DeepSTN is the best fit for five zones, and one variant of Seq2Seq, named Seq2Seq2b is the best fit for one zone under Mean Square Error (MSE) and Root Mean Squared Error (RMSE). Under MAE, DeepSTN and Seq2Seq2b, each of which is the best choice for three zones. In the second phase, the stacked ensemble achieves a MAE of 0.257. In the third phase, the stacked ensemble model is validated using the walk forward technique, which allows to reduce the MAE to 0.253. Although this framework focuses on Umrah, it can be customized for other use cases that involve crowd congestion forecasting.

Crowd management is crucial for countries and organizations as it can lead to severe consequences or serious safety concerns. Most of the existing research focus on addressing limited crowd management issues, namely crowd counting, density estimation, localization, and behavior monitoring. Furthermore, the generated incidents’ alerts are mostly not interpretable and remediable. Therefore, there is no comprehensive solution that addresses all these issues. This research proposes a comprehensive intelligence-based crowd management framework that employs anomaly rules to monitor, predict, and detect crowd accidents and help in providing quick response. The suggested crowd intelligence framework addresses all crowd management issues. The use case chosen for this framework is the management of crowds of pilgrims in Umrah Holy event. The proposed framework is then implemented and evaluated with respect to efficiency, scalability, interpretability, remediability, and the number of false positive, true positive, and false negative alerts. In addition, the suggested framework is compared with other recent related work in terms of supporting crowd management issues. The design of the proposed framework and implementation are then fine-tuned in light of the evaluation results. The results and findings of this research can be extended to manage crowds at any event.

A number of countries are today implementing open government data (OGD) initiatives. Yet many of these initiatives are failing to attract the levels of continuous use they need to deliver an acceptable return on investment. This raises the obvious question of why this should be the case. To answer this question, it is important to understand the factors that most strongly influence user behaviour in OGD adoption. Qualitative data were used to identify the factors that play a key role in influencing the intention to engage with ODG. A quantitative approach was then used to evaluate the extent to which these factors drive/limit behaviour. The study's findings showed that there are four factors that play a significant role in intention to use OGD. It is also believed that the findings will be useful in helping policymakers in all jurisdictions formulate and implement strategies that successfully drive up continuous OGD engagement.

A number of countries are today implementing open government data (OGD) initiatives. Yet many of these initiatives are failing to attract the levels of continuous use they need to deliver an acceptable return on investment. This raises the obvious question of why this should be the case. To answer this question, it is important to understand the factors that most strongly influence user behaviour in OGD adoption. Qualitative data were used to identify the factors that play a key role in influencing the intention to engage with ODG. A quantitative approach was then used to evaluate the extent to which these factors drive/limit behaviour. The study's findings showed that there are four factors that play a significant role in intention to use OGD. It is also believed that the findings will be useful in helping policymakers in all jurisdictions formulate and implement strategies that successfully drive up continuous OGD engagement.

Privacy preservation (PP) in Digital forensics (DF) is a conflicted and non-trivial issue. Existing solutions use the searchable encryption concept and, as a result, are not efficient and support only a keyword search. Moreover, the collected forensic data cannot be analyzed using existing well-known digital tools. This research paper first investigates the lawful requirements for PP in DF based on the organization for economic co-operation and development OECB) privacy guidelines. To have an efficient investigation process and meet the increased volume of data, the presented framework is designed based on the selective imaging concept and advanced encryption standard (AES). The proposed framework has two main modules, namely Selective Imaging Module (SIM) and Selective Analysis Module (SAM). The SIM and SAM modules are implemented based on advanced forensic format 4 (AFF4) and SleuthKit open source forensics frameworks, respectively, and, accordingly, the proposed framework is evaluated in a forensically sound manner. The evaluation result is compared with other relevant works and, as a result, the proposed solution provides a privacy-preserving, efficient forensic imaging and analysis process while having also sufficient methods. Moreover, the AFF4 forensic image, produced by the SIM module, can be analyzed not only by SAM, but also by other well-known analysis tools available on the market.

Organizations are required to implement an information security management system (ISMS) for making a central cybersecurity framework, reducing costs, treating risks, and so on. Several ISMS standards have been issued and implemented locally and internationally. In Saudi Arabia, the most widely implemented international ISMS is ISO/IEC 27001. Currently, the Saudi National Cybersecurity Authority (NCA) issued a local framework called Essential Cybersecurity Controls (NCA-ECC). Therefore, many ISO/IEC 27001 certified organizations in Saudi Arabia are trying to convert from ISO/IEC 27001 to NCA-ECC or comply with both frameworks. Nevertheless, cybersecurity experts need to know which cybersecurity controls are already implemented, based on the ISO/IEC 27001, and which are not. This paper first measures the extent to which certified ISO/IEC 27001 Saudi organizations comply with the NCA-ECC. Second, it presents a framework for complying with the required unimplemented or partially implemented NCA-ECC controls. The framework can also help organization to be in compliance with both frameworks, if required. Three ISO/IEC 27001-certified Saudi public universities are selected as samples. The data is collected by interviewing the cybersecurity officers in the selected universities. This research shows that certified ISO/IEC 27001 organizations are approximately 64% in compliance with the NCA-ECC. The presented framework can help any ISO/IEC 27001 certified Saudi organization convert from ISO/IEC 27001 to NCA-ECC in a quick and cost-effective manner by considering only NCA-ECC nonconformities.

The rapid development of different social media and content-sharing platforms has been largely exploited to spread misinformation and fake news that make people believing in harmful stories, which allow to influence public opinion, and could cause panic and chaos among population. Thus, fake news detection has become an important research topic, aiming at flagging a specific content as fake or legitimate. The fake news detection solutions can be divided into three main categories: content-based, social context-based, and knowledge-based approaches. In this paper, we propose a novel hybrid fake news detection system that combines linguistic and knowledge-based approaches and inherits their advantages, by employing two different sets of features: (1) linguistic features (i.e., title, number of words, reading ease, lexical diversity,and sentiment), and (2) a novel set of knowledge-based features, called fact-verification features that comprise three types of information namely, (i) reputation of the website where the news is published, (ii) coverage , i.e., number of sources that published the news, and (iii) fact-check , i.e., opinion of well-known fact-checking websites about the news, i.e., true or false. The proposed system only employs eight features, which is less than most of the state-of-the-art approaches. Also, the evaluation results on a fake news dataset show that the proposed system employing both types of features can reach an accuracy of 94.4%, which is better compared to that obtained from separately employing linguistic features (i.e., accuracy=89.4%) and fact-verification features (i.e., accuracy=81.2%).

Information sharing via social networking systems (SNS) is a common practice among academics, as well as others, that brings substantial benefits. At the same time, privacy concerns are widespread among SNS users, which may tend to inhibit their maximising the benefit from using the systems. This paper investigates the proposition that SNS user attitudes and behaviour are affected by privacy concerns, and that the effects are subject to significant cultural factors. A broad assessment of the literature provides the context for the study. Working in the context of Saudi Arabia, we apply a mixed-methods approach beginning with in-depth interviews, exposing in detail a range of views and concerns about privacy and SNS use, also allowing us to identify three key factors that bear on SNS usage and users' concerns. Analysis of these factors in the light of the "theory of reasoned action" derives a structural model predicting several hypotheses relating the factors and users' attitudes and behaviour. We assess the model through development of a questionnaire, administered to a large pool of academic participants, that allows us to examine how the model responds in general, and via multigroup partial least squares analyses, differentially to gender and to culturally distinct (Arab vs. non-Arab) constituents of the participant group. Results show good support for the hypotheses and clear gender and culture effects. Picking up issues from the interviews, discussion focuses on users' views about SNS providers' privacy policies and their inadequacy regarding culturally specific ethical concerns. We argue that these views may reflect different regulatory environments in combination with other cultural factors.

The Bitcoin technique faces several security challenges such as bitcoin wallet damage and attacks. This research presents a Bitcoin Wallet Security System (BWSS) to ensure the confidentiality and integrity of bitcoin wallet. The proposed system functions are distributed into two components and then implemented using Java programming language. A two-factor authentication is used to provide a more confidential bitcoin wallet while a backup mechanism is used to back up the wallet automatically or manually. Finally, the system is evaluated and refined after evaluation to come out with the required security features in a secure manner. The proposed system enables the bitcoin wallet owner to first protect his/her bitcoin wallet(s) with a second security layer using a two factor authentication and, second, back up his/her wallet in a secure and flexible ways. The system can be improved in the future to secure bitcoin wallets in mobile devices.

Dynamically managing service level agreements (SLAs) is a non-trivial issue for both active media technology users and providers. Cloud computing is considered as one of the significant components of the rapidly emerging active media in the current era. As the technology grows rapidly, cloud computing requirements and techniques also change over time, while the current cloud SLA management methods are rigorous in terms of SLA contents updates with the technology innovation and user requirement change. In other words, public SLAs are used as templates, and when issuing a new SLA, it is necessary to map its requirements to all public SLAs. In addition, updating any consumer’s SLA requires a cumbersome process of remapping their changed requirements to all public SLAs. This paper proposes a cloud computing SLA management mechanism based on the real options analysis concept to manage cloud SLAs in a dynamic manner. At the beginning, or when issuing a new SLA, the proposed framework receives a new user’s requirements and maps these to all public SLAs, while executes only the most appropriate SLA based on options analysis and records/marks the other related solutions (SLAs) for future analysis to address any further change due to internal/external factor. This also provides a mechanism to effectively counter uncertainty parameters triggering dynamic change in cloud SLA management. The framework was deployed using Web SLA and Java followed by an experimental study to evaluate the efficacy and scalability of the proposed framework. The results show that the proposed framework provides efficient and scalable cloud SLA management mechanism.

Privacy policies are required by most, if not all, existing privacy acts to make clear how private data are collected, used and disclosed. They are used as a guideline by the data collector to control its behaviour. They also help the data owner to know how the data will be maintained. Therefore, defining privacy policies for computer forensics is an important legal issue to make the investigation process more lawful in terms of privacy preservation. However, as Waleed Halboob, Ramlan Mahmod, Nur Izura Udzir and Mohd Taufik Abdullah of the Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, explain, current policies and processes are inadequate. They propose a number of policies that cover all computer forensics investigation steps – imaging, analysis and presentation.

Computer forensics and privacy protection fields are two conflicting directions in computer security. In the other words, computer forensics tools try to discover and extract digital evidences related to a specific crime, while privacy protection techniquesaim at protecting the data owner's privacy. As a result, finding a balance between these two fields is a serious challenge. Existing privacy-preserving computer forensics solutions consider all data owner's data as private and, as a result, they collect and encrypt the entire data. This increases the investigation cost in terms of time and resources. So, there is a need for having privacy levels for computer forensics so that only relevant data are collected and then only private relevant data are encrypted. This research paper proposes privacy levels for computer forensics. It starts with classifying forensic data, and analyzing all data access possibilities in computer forensics. Then, it defines several privacy levels based on the found access possibilities. The defined privacy levels lead to more efficient privacy-preserving computer forensics solution.

In this paper, we have proposed the design of an efficient computer forensics investigation framework. The proposed framework improves the investigation efficiency using Data Warehouse (DW) concept, which provides a selective evidence identification, collection and analysis. So, only relevant data is investigated instead of investigating the entire user data. The proposed framework consists of a Data Warehouse Engine (DWE) to selectively identify, collect and analyze digital evidences from multiple digital resources. A Digital Evidence Preservation (DEP) mechanism is also introduced for preservation of the collected digital evidences whose authenticity is ensured using cryptographic techniques. An access control mechanism is implemented to allow only authorized investigator to access the preserved digital evidences. The DEP mechanism provides court of law with a Secure Forensic Audit Trial (SFAT) that helps in tracking happened activities on the collected evidences for ensuring the authenticity and reliability of the presented digital evidence.

Several access control policy languages have been proposed for specifying access control policies for push-based XML access control systems. This paper investigates the scalability of the current XML-based policy languages. It starts by introducing the well-known general access control models with more focus on their scalability. Then, the XML-based policy languages are presented followed by evaluating their management and system scalability. This paper founds that there is a need for using decentralized trusted management concept for addressing the scalability issue in XML access control. Also, using IBE will help in providing several access control features as temporal and delegable access.

Managing Service Level Agreements (SLAs) in cloud computing in a dynamic manner becomes a critical issue for cloud service providers. This is due to the emerging technology and the frequent and continuous change in cloud service requirements and techniques over the time. Current cloud SLAs management methods are rigorous in terms of SLA updation to incorporate new changes. Since updating any SLA, to meet any required change in cloud requirements and techniques, requires reformulation and remapping to all available SLAs (called public SLAs). This paper proposed a mechanism to dynamically manage cloud computing SLAs based on Real Options Analysis (ROA) concept. The proposed model maps the required changes to all public SLAs and sorts out the most related or suitable SLAs (solutions) based on options theory while recording the other solutions for any future change according to emerging circumstances. The technique incorporates any new change dynamically; by mapping it to a limited number of SLAs (recorded solutions) based on various options presented by ROA. The framework presented in this paper would provide a flexible solution in managing cloud SLAs in both cloud provider and the user’s perspectives.

The traditional computer forensics procedures and tools collect and analyze the entire user data. This scenario has been proven to be not appropriate any more due to increased size of user data and storage. Accordingly, selective imaging and distributed analysis concepts have been introduced in the literature to reduce the digital evidences collection and analysis costs (time and resources). Current selective imaging approaches image the relevant data according the order of their selection and not according to their physical offsets order inside the targeted storage. Furthermore, integrating the selective imaging and distributed analysis has not been considered yet. This study proposed a computer forensics investigation process that provides an efficient imaging and scalable analysis. The selected data artifacts are first ordered upon their physical offsets. Then, based on the selected data size and available investigation time, the selected data are imaged into one or more partial forensic image in such a way that the produced images can be analyzed by different investigators and using several machines. An Advanced Forensic File Format 4 (AFF4) is used as a container for the collected relevant data. An experiment study has been used to evaluate the performance of the selected imaging process. The result shows that, even if ordering the selected digital evidences has a small performance negative impact but it has a positive effect on the performance of the selective imaging process itself. A qualitative study has been also used to evaluate the system and management scalability of the distributed analysis.

Controlling access to the cloud computing services involves new scalability and security challenges that cannot be directly addressed by traditional access control solutions. In other words, cloud computing access control requires establishing a trust between users, service provider and provided services in a scalable manner. Moreover, several access control issues have not been considered in cloud computing such as providing users with a temporal and delegable access. This paper proposed the design of a trusted and decentralized cloud computing access control model for better security and scalability. To address the scalability issue, the components of the proposed model are separated in such a way that they can be decentralized and executed in different extensible machines. The proposed model includes a Trusted XML-based Cloud Computing Policy Language (TXCCPL) that specifies access control policies for authenticating and authorizing both the user and his platform based on the Decentralized Trust Management (DTM) and Trusted Computing (TC) concepts. An Identity Based Encryption (IBE) is used for providing users with temporal and delegable access. Our analysis shows that the proposed model is cost-effectively distributed while avoiding the possible bottlenecks and, as a result, effectively scales with increased workloads. Also, using the DTM and TC concepts help on addressing several security challenges found in cloud computing.

Selective imaging is a new concept in computer forensics. It is used for collecting only the data that is relevant to the crime and helps in improves the scalability of the investigation process. However, the current selective imaging approaches directly image the identified data without considering their offsets on the targeted user storage. This paper investigates the impact of the relevant data offsets on the efficiency of the selective imaging process. A practical selective imaging model is presented which includes a digital evidence ordering algorithm (DEOA) for ordering the selected relevant data items. The proposed selective imaging model has been implemented and evaluated in different types of storage devices. The evaluation result shows that even if our proposed algorithm has a small efficiency negative impact before the imaging process starts; it has a large positive effect on the efficiency of the selective imaging process itself.

XML language is becoming a de facto standard for representing and transmitting data on the web so controlling access to XML documents is an important issue. Several XML access control approaches have been proposed to control the access of the published XML documents but these approaches still cannot scale well wim increased system and management workload because they specify and enforce their access control policies in centralized servers. We have proposed a distributed XML access control model for that improves the scalability by distributing the system and management workloads into several servers and different administrator, respectively. In this paper, the scalability of the proposed model is evaluated using a quantitative approach that shows that the proposed model is cost-effectively scalable with an increase in system and management workloads.