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.

Cloud computing introduces concerns about data protection and intrusion detection mechanism. A review of the literature shows that there is still a lack of works on cloud IDS that focused on implementing real-time hybrid detections using Dendritic Cell algorithm (DCA) as a practical approach. In addition, there is also lack of specific threat detection built to detect intrusions targeting cloud computing environment where current implementations still using traditional open source or enterprise IDS to detect threats targeting cloud computing environment. Cloud implementations also introduce a new term, "co-residency" attack and lack of research focusing on detecting this type of attack. This research aims to provide a hybrid intrusion detection model for Cloud computing environment. For this purpose, a modified DCA is proposed in this research as the main detection algorithm in the new hybrid intrusion detection mechanism which works on Cloud Co-Residency Threat Detection (CCTD) that combines anomaly and misuse detection mechanism. This research also proposed a method in detecting co-residency attacks. In this paper the co-residency attack detection model was proposed and tested until satisfactory results were obtained with the datasets. The experiment was conducted in a controlled environment and conducted using custom generated co-residency denial of service attacks for testing the capability of the proposed model in detecting novel co-residency attacks. The results show that the proposed model was able to detect most of the types of attacks that conducted during the experiment. From the experiment, the CCTD model has been shown to improve DCA previously used to solve similar problem.