Mobile reporting applications are useful mainly for reporting real-time issues related to public infrastructure, environmental or social incidents through smart mobile devices. The credibility of the cases reported are often a great challenge because users may report false information and as a result this affects the response team in the aspect of time, energy and other resources. Researchers in the past have developed many report trust estimation algorithms that focuses on user's location, behavior and reputation. We aim to analyze the textual part of a report. Text analyses have been used for email spam filtering and sentiment analysis but have not been used for false report identification. Therefore, the purpose of this study is to compare different text classification algorithms and propose a suitable classifier for distinguishing the genuine and fake reports. The comparative analysis can be used by other researchers in the area of false report or fake message identification.

In this research, a false report identification algorithm for mobile application is developed using a text classification technique. This algorithm is proposed to be applied to a reporting service application in an IoT environment. The algorithm is aimed to distinguish reports into true and false information. Support Vector Machine (SVM) is used as the text classifier because it has proven to be the most popularly used due to its good performance and higher accuracy compared to the other techniques such as Naive Bayes, Decision Tree and K-Nearest Neighbours. The algorithm is designed and developed in R Studio and we built a framework to show how the algorithms can be adapted into a reporting service application. The results show that the algorithm has successfully classified the reports.

The cloud computing model has been developed with promises to overcome the difficulties of other computing models. The low cost of computing resources, availability, location independence, and more focus on business objectives that this model promises are the most attractive features for companies. However, security issues are still the main obstacle that prevent widespread cloud computing adaptation. Digital forensics, a necessary process after any security incident, is faced with many challenges in the cloud computing environment. In this paper, we discuss the challenges digital forensics face in a cloud computing environment.

The traditional risk assessment methods calculate the risk based on the risk likelihood and the risk impact, which are not suitable to be applied in cloud computing environments. Therefore, this study proposes a framework to assess information security risk by using international risk management standard with adoptions of a medical research design and method. The risk management standard is based on BS. ISO/IEC27005: 2011, which consists of context establishment, risk identification, risk analysis, risk evaluation, risk treatment, monitoring and review, and communication and consultation. Adopting a retrospective cohort study and a method known as Cox Proportional Hazards (PH) Model under survival analysis method will be applied to identify potential information security threats in cloud computing environments. The survival analysis method is used to identify which factors have significant impact and predict the hazard probabilities for selected risk factors in medical field. The proposed framework will be used software as a service model of cloud computing. Therefore, the contribution of this research will be a framework for performing risk analysis studies to identify information security threats in cloud computing environment using medical research design and method.