Trends in Android Malware Detection

This paper analyzes different Android malware detection techniques from several research papers, some of these techniques are novel while others bring a new perspective to the research work done in the past. The techniques are of various kinds ranging from detection using host based frameworks and static analysis of executable to feature extraction and behavioral patterns. Each paper is reviewed extensively and the core features of each technique are highlighted and contrasted with the others. The challenges faced during the development of such techniques are also discussed along with the future prospects for Android malware detection. The findings of the review have been well documented in this paper to aid those making an effort to research in the area of Android malware detection by understanding the current scenario and developments that have happened in the field thus far

A survey on cyber-crime prediction techniques

This paper is a review paper of journal and conference papers published in the field of crime prediction. Crime prediction is a growing field in the field of prediction. More and more law enforcement agencies are using or going to implement crime prediction software. In this paper firstly,data collection methods and findings of this paper are described and afterward the collected papers are described and their methods and findings are explained

An analysis method of forensic investigation for platform-as-a-service cloud storage services

Cloud computing has changed most of the ways users interact with computers and mobile devices. Every user, power-users or normal users, can take advantage of Cloud storage and in such a way that they can develop or store their data in cloud and access them anytime they want. There are three types of cloud Platform as a Service (PaaS), Software as a Service (SaaS) and Infrastructure as a Service (IaaS) but our focus is PaaS. Though, PaaS has made it easier to code and develop new application for developers, it has helped criminals to write their malicious application with minimum trace as well. PaaS cloud client applications could be a very useful for forensics investigators as they contain much information about the user. Although, there have been many digital forensics researches done on SaaS and IaaS, there have been close to none such research on PaaS. Therefore, the problem here is first there is not enough research in PaaS and second criminals use this service to create malicious applications. Previous researches on forensic analysis of PaaS cloud applications on Windows machines and smartphones used present forensic analyser tools and failed to detect all the data remnants such as file contents, email addresses, activity trails of users and many more. Also, majority of works were done on SaaS and IaaS cloud applications. In this research, to address the problems of lack of work on PaaS and lack of enough forensic data after analysis we propose a new analysis method for PaaS cloud applications to maximise the amount forensic that can be extracted in process of analysis. The proposed analysis method is valid for examining the internal storage, internal memory and network traffic of PC and smartphones. In the proposed analysis method of this project, the raw data of collected images is analysed. This analysis is done based on predefined keywords to detect login information. Upon identification of user’s data and pattern, the keywords which are common among PaaS applications are defined and then the raw data of images are analysed once again to find any remaining data remnants on the system. After the evidences are found and extracted then the researcher proceeds to presenting the findings in a report form. The new analysis method is tested on popular PaaS client applications namely Openshift and Heroku on Windows PC and mobile platforms iOS and Android. The outcome of this research establishes the use of the mentioned PaaS applications on the investigated computers and smartphones and results in identification of artefacts such as usernames, passwords, login information, application source code and application information. The result of this research assists forensic examiners and practitioners in understanding the types of artefacts that are likely to remain on Windows machines and iOS and Android smartphones after using PaaS applications and also it helps these applications’ developers to make the applications more secure and users to know the security issues of these applications