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The proliferation of the internet and computing devices has drawn much attention during the Covid-19 pandemic stay home and work, and this has led the organization to adapt to staying home. Also, to let the organization work due to the infrastructure for working on proxy during the pandemic. The alarming rate of cyber-attacks, which through this study infer that phishing is one of the most effective and efficient ways for cyber-attack success. In this light, this study aims to study phishing attacks and mitigation methods in play, notwithstanding analysing performance metrics of the current mitigation performance metrics. Results indicate that business enterprises and educational institutions are the most hit using email (social engineering) and web app phishing attacks. The most effective mitigation methods are training/awareness campaigns on social engineering and using artificial intelligence/machine learning (AI/ML). To gain zero or 100% phishing mitigation, AI/ML need to be applied in large scale to measure its efficiency in phishing mitigation.

Malaysia recorded its first COVID-19 case on 9th March 2020 and recorded a total of 59,817 by end of November 2020. Buzz in social media over COVID-19 and measures by the Government to curb infection spread among citizens. The study aims to understand Malaysian public awareness and perception of COVID-19 related issues on Facebook during the 1st and 2nd week of Movement Control Order (MCO). Data mining was conducted on DG Tan Sri Noor Hisham Abdullah’s official Facebook account user comments and a total of 77,351 comments was collected between 18 March and 14 April 2020. The analyses included data pre-processing and sentiment analysis to identify and explore sentiments in discussion topics within the first two weeks of lockdown. The results yield majority of comments are in the Malay language and mix languages of English and Malay as a secondary type. Secondly, sentiment analysis showed that people have a positive reaction towards the frontliners and all efforts by the Ministry of Health towards fighting the pandemic. Many positive remarks are given in form of prayers, which is in line with the Islamic teaching of positive thinking and optimism, especially during crises. In conclusion, sentiment analysis is effective in producing useful insights about trends of COVID-19 discussion on social media, collecting public perception and feedback of COVID-19 efforts by the Government, and gives a different viewing angle of the current situation on the ground. These findings can be useful for health officials or the Government in developing communication mitigation plans or conduct extensive studies on pertaining issues within areas of concern.

The reason to explain the cloud computing assimilation, as well as the understanding of the operational performance, still remains unclear. To investigate this issue, this study has examined the assimilation of cloud computing by examining the organization’s implementation of cloud computing and performance in the Malaysian public sector. Grounded by the Diffusion of Innovation Theory (DOI), Technology-Organization-Environment Framework (TOE) and IS Success Model. This study proposes a framework by decomposing Technological characteristic, Organizational characteristics, Environmental characteristics and Human characteristics as factors. A total of 169 agencies from the Malaysian public sector have participated in this study. Data for all the study variables have been collected through self-administered survey questionnaires and analysed using SEM-PLS. The study has shown that the effect of the operational effectiveness of cloud implementation is at a lower level. The study also reveals that factors of relative advantage, compatibility, perceived risks, top management support, and organizational readiness are found to have a significant effect on cloud computing implementation. However, both external support and government regulations are not significant to the cloud computing implementation in the Malaysian public sector. The results indicate that the model provides a good understanding of the factors that influence the implementation of cloud computing as well as operational performance.

Overhang property issue has sustained over the past ten years in Malaysia. Major overhang property issue was contributed from the unsold residential property. Though the government announced to build a data system and provide the housing data to prevent a mismatch of supply-demand in the property market, there are still not many relevant studies or research on predicting residential property prices. Hence, it is essential to understand the factors that influence the price of residential properties. The study aims to predict the price of a residential property by using a machine learning algorithm. Three algorithms were selected, namely Decision Tree, Linear Regression, and Random Forest, tested against the training and testing datasets obtained from the Malaysian Valuation and Property Services Department. Results show that the Random Forest model produced high accuracy with lower r_squared (R2), RMSE, and MAE values. Significantly, the study has contributed a new insight into essential property features that primarily influence the property price, which will be useful for property developers and buyers who wish to invest in the property market.

To date, public sector organisation begin to implement the digital workplace as part of their digital government initiative but yet there is proven the success of its implementation and how it impacts the government operation effectiveness. Therefore, based on a Technology-Organization-Environment (TOE) framework, this paper aims to investigate the current landscape, issues and challenges in workplace digitalization in public sector organization. A comparative analysis of the digital workplace approach was performed on nine Malaysian Public Sector organisations of the various business core function. This study found that there are nine factors related to digital workplace implementation. The main finding revealed that most organisation are not fully optimizing the digital workplace functionality and only using it as a digital repository. Based on this finding, significantly it can contribute the development of a new digital workplace framework for public sector organization in ensuring its contribution to operational effectiveness

Internet of Things (IoT) is predicted as one of the biggest emerging environments in the future that creates simultaneous smart communication between machines or a variety of digital devices. Besides improving how data can be controlled, monitored and collected, IoT also allows us to generate revenue through the identifying of new business opportunities and deployment of advanced analytics processes for decision-making purposes. IoT enables us to accelerate changes in the healthcare environment in improving patient engagement and outcome, as well as transforming healthcare from reactive to proactive accessibility. Nevertheless, IoT’s expansion brings new vulnerabilities, risk and security challenges for healthcare practitioners and their patients. However, there is still a lack of study focusing on IoT risk management in healthcare. Existing researches tend to focus only on the implementation of IoT peripherals in a healthcare environment and tend to embed the secured applications solution with it. Since healthcare information and data are highly confidential, it is important to ensure that a secured health IoT application is in place. Thus, the aim of this study is to investigate the IoT risk management aspects in a healthcare environment with particular attention to proposing a step-by-step process of an IoT risk management model. The proposed IoT risk management model was developed leveraging on DEMATEL IoT Risk Assessment Procedure. This study was conducted based on a case study of one hospital in Sudan which has recently invested in IoT technologies in their operation. Interviews were conducted with selected respondents from the hospital and findings suggested that the selected case study does not have an established IoT risk management mechanism due to the ad-hoc IoT implementation approach. The case study also lacks of protection for health data and information with several unclear work process. As a solution, this study proposes an enhanced IoT risk management model for healthcare with consideration of three risk categories; 1) Secured Technology, 2) Human Privacy and 3) Trustable Process and Data. The proposed model was then evaluated by three IoT experts and two IT healthcare practitioners based on System Usability Score (SUS) and received Good Usability score which means the model is usable as a healthcare IoT risk management model.

Visual analytics fundamentally influences the analytical process. Without proper guidance, the use of visual analytics in complex domain can become an obstacle that hides the usefulness of analytical data. Since evaluation is the only way to identify the effectiveness of visual analytics to represent analytical outcomes, misconception of the evaluation process will bury the relevancy of visualization to support valuable decision. Recently, the nature domain of data has changed and we are now dealing with data that is massive, ambiguous, and dynamic, is often processed in real time. Hence, the data is more complex and cognitive activities that visual analytics facilitates are also getting more complex. Thus, this research revisits the way to evaluate visual analytics in complex cognitive conditions that are natural, uncertain and context dependent. Governed by the Design Science Research Methodology (DSRM), there are three phases involve during the evaluation process; i). demonstration, ii). evaluation and iii). communication. The design process is embedded with the human-activity centered design approach to gain better understanding on the visual analytics users and the complex cognitive activities involved. Thus, the research proposes Focus Group Observation method in conducting the evaluation in authentic setting. By offering a set of evaluation recommendations, this research aims to enhance visual analytics among users. It also recommends the evaluation criteria, sampling strategy and participation, focus group tasks

The quickly propelling Voice over Internet Protocol (VoIP) innovation, is picking up customer fascination in the business today. Its uses, including IP communication frameworks, includes the transmission of voice as information bundles which after achieving the getting side, reassembles and deciphers according to necessity. This encourages open just as private IP frameworks. The real drawback, keeping numerous organizations off from utilizing VoIP advances is related to the security of this framework. This paper examines a strategy and the following framework that get to arrange data for approved system gadgets. The entrance insights and data empower to follow gadget development all through the system. In addition, it offers to recognize unapproved gadgets making false endeavors to get to the system, presenting to be an approved access gadget.

In software development phase software artifacts are not in consistent states such as: some of the class artifacts are fully developed some are half developed, some are major developed, some are minor developed and some are not developed yet. At this stage allowing too many software requirement changes may possibly delay in project delivery and increase development budget of the software. On the other hand rejecting too many changes may increase customer dissatisfaction. Software change effort estimation is one of the most challenging and important activity that helps software project managers in accepting or rejecting changes during software development phase. This paper extends our previous works on developing a software requirement change effort estimation model prototype tool for the software development phase. The significant achievements of the tool are demonstrated through an extensive experimental validation using several case studies. The experimental analysis shows improvement in the estimation accuracy over current change effort estimation models.

With the rapid digitalisation of the economy nowadays, it is becoming increasingly important for organisations to embrace digital innovation. The emergence of blockchain technology illustrates digital innovation's disruptive impact and, at the same time, poses challenges to organisations. This study has been assisting government organisations in blockchain adoption that is reflected in far-reaching measures in terms of technology, organisation, and environment. Based on this framework, this research builds on an empirical study to explore the technology, organisation, environment and trust adoption of blockchain technology. This proposed framework aims to assist the government in developing blockchain adoption based on the baseline theory models, which are Technology, Organization, Environment (TOE), emphasis on trust and other blockchain adoption theories. This research uses a conventional literature review to identify the factors influencing blockchain adoption. Preliminary investigation has been conducted prior to develop the proposed framework. The interview session was conducted to verify the availability of the blockchain technology in government organisations. The proposed conceptual model can be valuable to government organisations and may assist those government organisations which consider blockchain technology in the organisation to be more secure.

The increase in social-engineering threats within the Saudi public sector has changed awareness and training methods. However, due to employees' lack of awareness, social engineering could lead to a breach whereby attackers identify vulnerabilities and subsequently launch their attacks. A social-engineering attack is a high risk to the Saudi public sector and may significantly affect its security measures. Thus, the benefits of adopting awareness-enhancement tools in the public sector are undeniable. This study proposes a conceptual awareness model designed to enhance employee awareness in the Saudi public sector to address this issue. This study reviews seven main factors of social engineering risk: phishing, baiting, pretexting, quid pro quo, tailgating, related security policies, and the ability to identify attacks and respond to threats. Additionally, this research examines one public sector actor in Saudi Arabia as a case study. The findings led to a model creation comprising of five components: a situation-awareness model for phishing, an information-security awareness tool, a power-knowledge-practice triangle, Saudi public sector follow-up metrics, and implementation phases. As a result, an a priori model was successfully developed, tested, and applied in the subsequent stage by the case study participants, the employees.

The Internet of Things and Industry 4.0 is changing the way we live including Internet Banking. Although, the government has been encouraging, the acceptance of internet banking is still not well received by Chinese customers. The reason is because they are more concerned about the economic development on the banking industry, and less concerned about technology acceptance and its development. This lack of acceptance is also attributed to the current lack of social network influence to internet banking which including its weak compliance, lack of identification, and lack of internalization. This paper aims to identify the factors that may be influencing IB acceptance by proposing a model which can determine customer’s behavioural intention based on social network influence. The TAM, UTAUT and the Social Influence Theory (SIT) were simultaneously adopted for this study. It is to use the TAM model’s perceived ease of use and perceived usefulness, the UTAUT model’s social influence, and the SIT’s compliance, identification and internalization factor for the analysis. For this purpose, a preliminary study was first administered so as to identify customers’ attitude of the IB acceptance. Thirty respondents were recruited for a structured interview. The behavioural intention and social network influence on internet banking in China was then examined with the proposed model. It appears that the model can be used to forecast Chinese customers’ acceptance of internet banking. It will give benefits to bank and society on paying attention to the influence of social network for internet banking in China.

Globally, the rise of disasters has caused billions of dollars lost each year. These include the loss of properties, life and has created a negative impact on socioeconomic level of a country. Currently, geospatial datasets are becoming crucial for situational awareness and management of disasters. The timely and accurate information on disastrous occurrences must be collected, maintained, and managed for efficient management of emergency. These geospatial datasets are from different data provider agencies. Thus, there is a need to focus on the geospatial data sharing that would benefits the authorities in decision making. This initiative entails high commitment and collaboration from the data provider agencies, which can be achieved through the sharing of geospatial datasets approach. This study aims to identify the critical success factors of geospatial data sharing in the context of natural disaster. A preliminary review, focus group discussion and interviews were conducted to get insights of the subject being studied. The findings revealed that there are thirteen (13) critical success factors for geospatial data sharing in disaster management. Technology, Organisation, Social, Environment, Ecology and Economy are the dimensions identified and mapped accordingly to the thirteen critical success factors.

Cloud computing is one of the platforms that drive organisations and users to be better prepared for a simpler computing platform and offers significant benefits to the quality of work. The transition from conventional computing to the virtual world helps organisations to maximise their potential. However, not all users can accept cloud computing adoption. Failure to understand the factors of user's acceptance will negatively impact the organisation's mission of empowering the technology. Therefore, this study proposes to assess to what extent the users are accepting cloud computing. This study adopts the unified theory of acceptance and use of technology (UTAUT) and six technological and human factors assessed for the Malaysian public sectors. Survey data from several ministries were analysed using partial least squares structural equation modelling (PLS-SEM). The study found out that performance expectancy, compatibility, security, mobility, information technology (IT) knowledge, and social influence had a significant impact on the user's intention to accept cloud computing. The results of this study contribute to a clear understanding of the factors affecting the Malaysian public sectors about cloud computing. &nbsp

Coastal recreational activities are one of the main attractions for local public beachgoers and overseas tourists. The accessibility to better-quality coastal water will enhance safety and public health awareness when the information is available. Existing platforms showing the risk of whether a beach is suitable for public recreational use is less available in Malaysia. The Internet of Things (IoT) based system design specifically for coastal recreational suitability may differ from the existing configuration depending on the environment and requirements. This paper reports the design and implementation of an IoT-based system to capture the coastal environmental data and recommend recreational suitability. The system captures sensor data, store it in a database and displays the result using a dashboard. The variable data include the temperature, humidity, rain, pH, turbidity, oxidation-reduction potential (ORP), and total dissolved solids (TDS) in a coastal area. The hardware used in the design is the development boards such as Raspberry Pi, Arduino Uno, and ESP32 controller. The system is developed using PHP, MySQL, and Apache Web Server and can be accessed online at https://ipantai.xyz. When using Message Queuing Telemetry Transport (MQTT) as the effective messaging protocol and HiveMQ broker, the result has shown improvement for message size, throughput, and power consumption. The further potential of an IoT-based system is to bring value for coastal management and serve as a powerful tool to determine whether the coastal area is suitable for the public to access water recreational activities.

The Internet of Things (IoT) has become a prevalent technology in the IT industry. One of the industries that can benefit extensively in this technology is healthcare. However, the healthcare IoT is still under debate with several studies suggesting it is lack of interoperability, security, and too much complexity. Even more, the risk involved in deploying it is still enormous. Many traditional risk assessment models are unable to provide a specific IoT risk guideline and specification, especially in the healthcare area. Thus, it is essential to understand the full extent of the IoT risk and how to manage its risk in the healthcare area. The risk management models, such as NIST SP 800-30, ISO/IEC 27005, OCTAVE, CRAMM, and EBIOS, which are among the leading and widely used in many areas and healthcare fields, have also been described. Besides, this paper includes a review of three IoT risk assessment models that are based on ABA-IDS, Deep Learning, and AHP-SVM. Based on the review analysis, we proposed a new enhanced healthcare IoT risk assessment model, which aims to provide a real-time monitoring and mitigating risks that incorporate the NIST SP 800-30 framework, ABA-IDS, and CNN deep learning. This shall constitute a better classification of each risk identified to find the best risk mitigation plan.

Ensuring the quality of data in healthcare is important as to determine the level of performance of health services offered by them. The key factor that helps boost their performance are the enhancement of timely diagnosis and management of diseases and patient, keeping personnel satisfaction and lessen the hospital costs in running them. Assessing the quality of data in adoption electronic health record (EHR) and preventing the rising of patient readmission rates in Malaysia is the main concern of the project. However, there is lack of study on patient readmission factors in Malaysia and the adoption of electronic health record (EHR) also causing poor quality of patients' data to be analyzed. Lastly, delivering the information to patients is difficult and confusing because of lacking in medical knowledge and terms. Therefore, this paper proposes consistency as additional dimension for data quality dimension to be used for validating patient readmission dataset. Theoretically, this project is seen to improve the quality of information by proposing improvement in used dimension to assess data quality of EHR. On the other hand, practically, this paper will help to visualize the results of medicinal information in the form of dashboard, which can be easily understood by both patients and health practitioners.

Currently, the process in Disaster Risk Reduction (DRR) relies heavily on geospatial information defined as information contained in topographic maps, thematic maps, plans, charts and satellite images. The preparation and use of geospatial information involve parties such as custodians, data providers, users and vendors. However, nonspatial data also crucial in DRR. Due to different data formats, sizes and type of both spatial and nonspatial data, data processing becomes more complex and time-consuming. Furthermore, some supplied data are lack of specific reasoning behind it. As a result, a volume of spatial and nonspatial data keep piling up daily without any beneficial utilisation. Therefore, this paper aims to investigate the impact of disaster-related data of both spatial and nonspatial that contribute to DRR from multiple stakeholders’ perspective. The research is conducted through a focus group discussion (FGD) interview with a total of 54 agencies that involve in DRR in Selangor State, Malaysia. Based on the findings, vulnerability data is the most impactful data for DRR, followed by base data, hazard data, exposure data and risk impact assessment data. The findings from this study will provide the DRR data priority list from various agencies perspective, which can help to anticipated is asters or at least reduce the risk of disaster. This could also assist the relevant agencies to prepare for DRR management more proactively.

The emerging of Internet of Things (IoT) technologies for unified and interconnected medical devices and sensors has changed the scenario in the healthcare industry. However, with the ‘openness’ of the distributed environment and medical devices, IoT will be the point of a breach where attackers are able to identify vulnerabilities and subsequently launch their attacks. This becomes high risk to the healthcare environment which may cause a big impact on its security measure. Nonetheless, the benefits of IoT solution in healthcare are undeniable. To address this issue, this study proposes an IoT Security Risk Management Model for Secured Practice in Healthcare Environment. This study reviewed all IoT risks from related works and has selected one Malaysian government hospital as a case study. From the findings, a model was formulated which consist of three parts, the Healthcare IoT Risk Management, the Hospital Performance Indicator for Accountability (HPIA) and the implementation phases. As a result, a priori model was successfully developed and yet to be validated by the case study participants in the next stage.