Forensic data management and database systems in forensic investigations for cases of missing and unidentified persons in Brazil

Forensic investigations,especially those related to missing persons and unidentified remains,produce different types of data that must be managed and understood.The data collected and produced are extensive and originate from various sources:the police,non-governmental organizations(NGOs),medical examiner offices,specialised forensic teams,family members,and others.Some examples of information include,but are not limited to,the investigative background information,excavation data of burial sites,antemortem data on missing persons,and postmortem data on the remains of unidentified individuals.These complex data must be stored in a secured place,analysed,compared,shared,and then reported to the investigative actors and the public,especially the families of missing persons,who should be kept informed of the investigation.Therefore,a data management system with the capability of performing the tasks relevant to the goals of the investigation and the identification of an individual,while respecting the deceased and their families,is critical for standardising investigations.Data management is crucial to assure the quality of investigative processes,and it must be recognised as a holistic integrated system.The aim of this article is to discuss some of the most important components of an effective forensic data management system.The discussion is enriched by examples,challenges,and lessons learned from the erratic development and launching of databases for missing and unidentified persons in Brazil.The main objective of this article is to bring attention to the urgent need for an effective and integrated system in Brazil.

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.

Finding Dutch natives in online forums

Law enforcement agencies have a restricted area in which their powers apply,which is called their jurisdiction.These restrictions also apply to the Internet.However,on the Internet,the physical borders of the jurisdiction,typically country borders,are hard to discover.In our case,it is hard to establish whether someone involved in criminal online behavior is indeed a Dutch citizen.We propose a way to overcome the arduous task of manually investigating whether a user on an Internet forum is Dutch or not.More precisely,we aim to detect that a given English text is written by a Dutch native author.To develop a detector,we follow a machine learning approach.Therefore,we need to prepare a specific training corpus.To obtain a corpus that is representative for online forums,we collected a large amount of English forum posts from Dutch and non-Dutch authors on Reddit.To learn a detection model,we used a bag-of-words representation to capture potential misspellings,grammatical errors or unusual turns of phrases that are characteristic of the mother tongue of the authors.For this learning task,we compare the linear support vector machine and regularized logistic regression using the appropriate performance metrics f1 score,precision,and average precision.Our results show logistic regression with frequency-based feature selection performs best at predicting Dutch natives.Further study should be directed to the general applicability of the results that is to find out if the developed models are applicable to other forums with comparable high performance.