Extracting Android Applications Data for Anomaly-based Malware Detection

In order to apply any machine learning algorithm or classifier, it is fundamentally important to first and foremost collect relevant features. This is most important in the field of dynamic analysis approach to anomaly malware detection systems. In this approach, the behaviour patterns of applications while in execution are analysed. The behaviour features that Android as a system allows access permissions to depend on the type of device; either rooted or not. Android is based on the Linux kernel at the bottom layer, all layers on top of the kernel run without privileged mode. Thus, if a behaviour feature vector is created from features of Android (Application Programming Interface) API in unrooted mode, then only system information made available by Android can be used. In this paper, a Device Monitoring system for an unrooted device is developed and used to collect Android application data. The application data is used to build feature vectors that describes the Android application behaviour for Anomaly malware detection. This application is able to collect essential information from Android application such as installed applications and services running within the device before or after the Monitoring application was started, the date/time stamp, calls initiated from the device, calls received by the device, sent short message services (SMSs), SMSs received, and the status of the device as at when the event took place. This information is loggedin a comma separated value (.csv) file format and stored on the SDcard of the device. The .csv file is converted toattribute relation file format (.arff); the format acceptable by WEKA machine learning tool. This.arff file of feature vectors is then used as input to the Classifier in the Android malware detection system

Challenge of COVID-19 and Nigerian Economic Change: The Way Forward

The Nigerian Economic implication of COVID-19 motivated this study. The study discussed the argument and counterargument within scientific discussions on the challenge of COVID-19 on Nigerian Economy. The broad objective of this study is to investigate the Nigerian Economic Change and Challenge of COVID-19 as well as the way forward. The specific objective is to determine the relationship between Nigerian Gross Domestic Product and COVID-19 comparing 2019 and 2020 Nigerian Economic Change, the study also aimed at establishing the way out of COVID-19. Descriptive statistics method of data analysis was used to present the results and findings of the study. The research design adopted in this study is ex-post facto. In this research, the type of data analysis that will be employed is descriptive statistics. The techniques will involve a view and appraisal of the effect of COVID-19 on Nigerian economy. That notwithstanding, the methodology can produce useful and meaningful results. To achieve this, quantitative analysis involving the use of percentages, degrees and graphical charts for the explanation of the data collected will be employed. Findings revealed that the coronavirus pandemic lockdown reduced the volume and value of production in Nigeria and affected the gross domestic product of Nigeria which almost jeopardized the Nigerian economy. Findings also revealed that resumption of offices and trading activities leads to an increase in the level and volume of production in Nigeria and this led to an increase in the value of GDP in Nigeria. The study therefore recommends that the Nigeria government should pay more attention to the Nigerian health sector in terms of funding, equipping hospitals, and training of medical staff. Nigerians should make use of preventive measures of COVID-19 which is the best way out of COVID-19; this will prevent shutting down of sectors and lead to a very high level of production in Nigeria. The study is highly unique as it will make Nigerians to know the effect of COVID-19 on Nigerian Economy. The study will shed light on different ways out of Coronavirus. The data to be analyzed covers 2019-2020 Nigerian quaterly Gross Domestic Product