Cloud computing is an Internet-based computing service provided by the third party allowing share of resources and data among devices. It is widely used in many organizations nowadays and becoming more popular because it changes the way of how the Information Technology (IT) of an organization is organized and managed. It provides lots of benefits such as simplicity and lower costs, almost unlimited storage, least maintenance, easy utilization, backup and recovery, continuous availability, quality of service, automated software integration, scalability, flexibility and reliability, easy access to information, elasticity, quick deployment and lower barrier to entry. While there is increasing use of cloud computing service in this new era, the security issues of the cloud computing become a challenges. Cloud computing must be safe and secure enough to ensure the privacy of the users. This paper firstly lists out the architecture of the cloud computing, then discuss the most common security issues of using cloud and some solutions to the security issues since security is one of the most critical aspect in cloud computing due to the sensitivity of user's data.

Security warning is a form communication between user and computer to inform the users on the risk of allowing random applications to run on the computer system. Security warning is designed to warn the users such as notify, inform and advice users about the consequence effect of an action. However, computer user s tend to ignore security warning due to the lack of attentions, did not understand the meaning of the warnings, difficulties on the technical jargon used, lack of motivation and users become habituated to security warning. Therefore, a survey was mounted online utilising 156 participants to investigate and to understand further general understanding of user’s perceptions on security warnings. This paper describes two main findings utilising Chi-Square and Cramer’s V test. The findings suggested that in all three scenarios, the results were not statistically significant. However, results portrayed in Cramer’s V test were in a better outlook. The result indicates on the need to look closely on each variable involves within the study sampling to improve the security warnings.

Security warning is a form of computer dialog communication that is used to inform the users on the risks of allowing random applications to run on a computer system. Accordingly, it is specifically designed to impersonate a legitimate security alerting function (e.g., notify, warn and advice) to a user about the consequence effect of an action. However, most of the computer users tend to ignore those security warnings conveying the same message over and over again. This eventually leads to habituation. Considering the fact that there is a significant lack of focus been paid to address this issue, the main objective of this paper is to describe and summarize the related studies on users’ habituation to the security warnings. This paper presents a systematic literature review to explore the current key issues, challenges and the possible solutions related to habituation effects in security warnings. It is expected that this paper could contribute to a more complete understanding of the habituation effects in security warnings and eventually bring benefits to the research communities or general publics.

Automatic data annotation eliminates most of the challenges we faced due to the manual methods of annotating sensor data. It significantly improves users’ experience during sensing activities since their active involvement in the labeling process is reduced. An unsupervised learning technique such as clustering can be used to automatically annotate sensor data. However, the lingering issue with clustering is the validation of generated clusters. In this paper, we adopted the k-means clustering algorithm for annotating unlabeled sensor data for the purpose of detecting sensitive location information of mobile crowd sensing users. Furthermore, we proposed a cluster validation index for the k-means algorithm, which is based on Multiple Pair-Frequency. Thereafter, we trained three classifiers (Support Vector Machine, K-Nearest Neighbor, and Naïve Bayes) using cluster labels generated from the k-means clustering algorithm. The accuracy, precision, and recall of these classifiers were evaluated during the classification of “non-sensitive” and “sensitive” data from motion and location sensors. Very high accuracy scores were recorded from Support Vector Machine and K-Nearest Neighbor classifiers while a fairly high accuracy score was recorded from the Naïve Bayes classifier. With the hybridized machine learning (unsupervised and supervised) technique presented in this paper, unlabeled sensor data was automatically annotated and then classified.