A quantitative approach to Triaging in Mobile Forensics

被引:26
|
作者
Marturana, Fabio [1 ]
Me, Gianluigi [1 ]
Berte, Rosamaria [1 ]
Tacconi, Simone [2 ]
机构
[1] Univ Roma Tor Vergata, Dept Comp Sci Syst & Prod, Rome, Italy
[2] Polizia Stato & Comunicaz, Rome, Italy
关键词
Triaging; Mobile Forensics; Data Mining; Knowledge Analysis; Machine Learning;
D O I
10.1109/TrustCom.2011.75
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Forensic study of mobile devices is a relatively new field, dating from the early 2000s. The proliferation of phones (particularly smartphones) on the consumer market has caused a growing demand for forensic examination of the devices, which could not be met by existing Computer Forensics techniques. As a matter of fact, Law enforcement are much more likely to encounter a suspect with a mobile device in his possession than a PC or laptop and so the growth of demand for analysis of mobiles has increased exponentially in the last decade. Early investigations, moreover, consisted of live analysis of mobile devices by examining phone contents directly via the screen and photographing it with the risk of modifying the device content, as well as leaving many parts of the proprietary operating system inaccessible. The recent development of Mobile Forensics, a branch of Digital Forensics, is the answer to the demand of forensically sound examination procedures of gathering, retrieving, identifying, storing and documenting evidence of any digital device that has both internal memory and communication ability [1]. Over time commercial tools appeared which allowed analysts to recover phone content with minimal interference and examine it separately. By means of such toolkits, moreover, it is now possible to think of a new approach to Mobile Forensics which takes also advantage of "Data Mining" and "Machine Learning" theory. This paper is the result of study concerning cell phones classification in a real case of pedophilia. Based on Mobile Forensics "Triaging" concept and the adoption of self-knowledge algorithms for classifying mobile devices, we focused our attention on a viable way to predict phone usage's classifications. Based on a set of real sized phones, the research has been extensively discussed with Italian law enforcement cybercrime specialists in order to find a viable methodology to determine the likelihood that a mobile phone has been used to commit the specific crime of pedophilia, which could be very relevant during a forensic investigation.
引用
收藏
页码:582 / 588
页数:7
相关论文
共 50 条
  • [1] Mobile Forensics
    Onofrei-Riza, Deniss Bogdan
    ROMANIAN JOURNAL OF INFORMATION TECHNOLOGY AND AUTOMATIC CONTROL-REVISTA ROMANA DE INFORMATICA SI AUTOMATICA, 2019, 29 (01): : 69 - 76
  • [2] Evaluating and Comparing Tools for Mobile Device Forensics Using Quantitative Analysis
    Saleem, Shahzad
    Popov, Oliver
    Appiah-Kubi, Oheneba Kwame
    DIGITAL FORENSICS AND CYBER CRIME, ICDF2C 2012, 2013, 114 : 264 - 282
  • [3] Lightweight Forensics Application: Lightweight Approach to Securing Mobile Devices
    Brumfitt, Helen Angela
    Askwith, Bob
    Zhou, Bo
    CIT/IUCC/DASC/PICOM 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND INFORMATION TECHNOLOGY - UBIQUITOUS COMPUTING AND COMMUNICATIONS - DEPENDABLE, AUTONOMIC AND SECURE COMPUTING - PERVASIVE INTELLIGENCE AND COMPUTING, 2015, : 795 - 800
  • [4] Mobile phone forensics - a systematic approach, tools, techniques and challenges
    Kumar, Manish
    INTERNATIONAL JOURNAL OF ELECTRONIC SECURITY AND DIGITAL FORENSICS, 2021, 13 (01) : 64 - 87
  • [5] Mobile device forensics
    Aljahdali, Asia
    Alsaidi, Nawal
    Alsafri, Maram
    Alsulami, Afnan
    Almutairi, Turkia
    ROMANIAN JOURNAL OF INFORMATION TECHNOLOGY AND AUTOMATIC CONTROL-REVISTA ROMANA DE INFORMATICA SI AUTOMATICA, 2021, 31 (03): : 81 - 96
  • [6] Mobile Forensics in Healthcare
    Justice, Connie
    Wu, Hunamei
    Walton, Evelyn
    EIGHTH INTERNATIONAL CONFERENCE ON MOBILE BUSINESS, PROCEEDINGS, 2009, : 55 - 55
  • [7] Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data
    Jones, G. Maria
    Winster, S. Godfrey
    Valarmathie, P.
    COMPUTER SYSTEMS SCIENCE AND ENGINEERING, 2022, 40 (03): : 963 - 978
  • [8] Integrated Approach to Detect Cyberbullying Text: Mobile Device Forensics Data
    Jones G.M.
    Winster S.G.
    Valarmathie P.
    Computer Systems Science and Engineering, 2021, 40 (03): : 963 - 978
  • [9] volGPT: Evaluation on triaging ransomware process in memory forensics with Large Language Model
    Oh, Dong Bin
    Kim, Donghyun
    Kim, Dong Hyun
    Kim, Huy Kang
    FORENSIC SCIENCE INTERNATIONAL-DIGITAL INVESTIGATION, 2024, 49
  • [10] Mobile device forensics: A snapshot
    Tassone, Christopher
    Martini, Ben
    Choo, Kim-Kwang Raymond
    Slay, Jill
    TRENDS AND ISSUES IN CRIME AND CRIMINAL JUSTICE, 2013, (460): : 1 - 7