Machine Learning Approach to Mobile Forensics Framework for Cyber Crime Detection in Nigeria

The mobile Cyber Crime detection is challenged by number of mobiledevices (internet of things), large and complex data, the size, the velocity,the nature and the complexity of the data and devices has become sohigh that data mining techniques are no more efficient since they cannothandle Big Data and internet of things. The aim of this research work wasto develop a mobile forensics framework for cybercrime detection usingmachine learning approach. It started when call was detected and thisdetection is made by machine learning algorithm furthermore intelligentmass media towers and satellite that was proposed in this work has theability to classified calls whether is a threat or not and send signal directlyto Nigerian communication commission (NCC) forensic lab for necessaryaction.

Integrated Approach to Detect Cyberbullying Text:Mobile Device Forensics Data

Mobile devices and social networks provide communication opportunities among the young generation,which increases vulnerability and cybercrimes activities.A recent survey reports that cyberbullying and cyberstalking constitute a developing issue among youngsters.This paper focuses on cyberbullying detection in mobile phone text by retrieving with the help of an oxygen forensics toolkit.We describe the data collection using forensics technique and a corpus of suspicious activities like cyberbullying annotation from mobile phones and carry out a sequence of binary classification experiments to determine cyberbullying detection.We use forensics techniques,Machine Learning(ML),and Deep Learning(DL)algorithms to exploit suspicious patterns to help the forensics investigation where every evidence contributes to the case.Experiments on a real-time dataset reveal better results for the detection of cyberbullying content.The Random Forest in ML approach produces 87%of accuracy without SMOTE technique,whereas the value of F1Score produces a good result with SMOTE technique.The LSTM has 92%of validation accuracy in the DL algorithm compared with Dense and BiLSTM algorithms.

Instagram Mobile Application Digital Forensics

In this research,we developed a plugin for our automated digital forensics framework to extract and preserve the evidence from the Android and the IOS-based mobile phone application,Instagram.This plugin extracts personal details from Instagram users,e.g.,name,user name,mobile number,ID,direct text or audio,video,and picture messages exchanged between different Instagram users.While developing the plugin,we identified resources available in both Android and IOS-based devices holding key forensics artifacts.We highlighted the poor privacy scheme employed by Instagram.This work,has shown how the sensitive data posted in the Instagram mobile application can easily be reconstructed,and how the traces,as well as the URL links of visual messages,can be used to access the privacy of any Instagram user without any critical credential verification.We also employed the anti-forensics method on the Instagram Android’s application and were able to restore the application from the altered or corrupted database file,which any criminal mind can use to set up or trap someone else.The outcome of this research is a plugin for our digital forensics ready framework software which could be used by law enforcement and regulatory agencies to reconstruct the digital evidence available in the Instagram mobile application directories on both Android and IOS-based mobile phones.

MobSafe:Cloud Computing Based Forensic Analysis for Massive Mobile Applications Using Data Mining

With the explosive increase in mobile apps, more and more threats migrate from traditional PC client to mobile device. Compared with traditional Win＋Intel alliance in PC, Android＋ARM alliance dominates in Mobile Internet, the apps replace the PC client software as the major target of malicious usage. In this paper, to improve the security status of current mobile apps, we propose a methodology to evaluate mobile apps based on cloud computing platform and data mining. We also present a prototype system named MobSafe to identify the mobile app＇s virulence or benignancy. Compared with traditional method, such as permission pattern based method, MobSafe combines the dynamic and static analysis methods to comprehensively evaluate an Android app. In the implementation, we adopt Android Security Evaluation Framework （ASEF） and Static Android Analysis Framework （SAAF）, the two representative dynamic and static analysis methods, to evaluate the Android apps and estimate the total time needed to evaluate all the apps stored in one mobile app market. Based on the real trace from a commercial mobile app market called AppChina, we can collect the statistics of the number of active Android apps, the average number apps installed in one Android device, and the expanding ratio of mobile apps. As mobile app market serves as the main line of defence against mobile malwares, our evaluation results show that it is practical to use cloud computing platform and data mining to verify all stored apps routinely to filter out malware apps from mobile app markets. As the future work, MobSafe can extensively use machine learning to conduct automotive forensic analysis of mobile apps based on the generated multifaceted data in this stage.