TY - GEN
T1 - SAFE
T2 - 15th International Conference on Innovations in Information Technology, IIT 2023
AU - Bakhshi, Taimur
AU - Ghita, Bogdan
AU - Kuzminykh, Ievgeniia
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Modern automotive infotainment systems offer a substantial source of evidence for digital forensic practitioners. However, due to lack of guidance and supporting validation tools, forensic analysts struggle with both data acquisition, analysis and reporting. There are general digital forensic frameworks and legislative acts that can be applied to automotive forensics. However, processing vehicles may prove challenging due to analysis of proprietary automotive systems and on-site crime scene dynamics, including cross-functional investigation with physical forensic teams. To gain an insight into emerging challenges, the present work surveyed current automotive forensics practices across law enforcement agencies (LEAs) in the EU, NA and AP region. The result of this survey enabled a qualitative evaluation, exposing an overall limited capability along with prevalence of invasive data retrieval methods and lack of standardized investigation trajectories. Based on this evaluation, a predominant set of recommendations were derived and streamlined in SAFE: A standardized automotive forensic engine. SAFE utilizes preliminary information from the crime-scene and presents a best-practice step-by-step investigation guide for front-line vehicle forensic analysts. The engine captures analyst rating on the validity of each investigation trajectory and KNN-based content filtering is employed to improve future recommendations. SAFE, therefore, aims to optimize vehicle forensic processing from initial crime scene to the courtroom.
AB - Modern automotive infotainment systems offer a substantial source of evidence for digital forensic practitioners. However, due to lack of guidance and supporting validation tools, forensic analysts struggle with both data acquisition, analysis and reporting. There are general digital forensic frameworks and legislative acts that can be applied to automotive forensics. However, processing vehicles may prove challenging due to analysis of proprietary automotive systems and on-site crime scene dynamics, including cross-functional investigation with physical forensic teams. To gain an insight into emerging challenges, the present work surveyed current automotive forensics practices across law enforcement agencies (LEAs) in the EU, NA and AP region. The result of this survey enabled a qualitative evaluation, exposing an overall limited capability along with prevalence of invasive data retrieval methods and lack of standardized investigation trajectories. Based on this evaluation, a predominant set of recommendations were derived and streamlined in SAFE: A standardized automotive forensic engine. SAFE utilizes preliminary information from the crime-scene and presents a best-practice step-by-step investigation guide for front-line vehicle forensic analysts. The engine captures analyst rating on the validity of each investigation trajectory and KNN-based content filtering is employed to improve future recommendations. SAFE, therefore, aims to optimize vehicle forensic processing from initial crime scene to the courtroom.
KW - automotive forensics
KW - digital forensics
KW - law enforcement
KW - machine learning
KW - vehicle forensics
UR - http://www.scopus.com/inward/record.url?scp=85182946412&partnerID=8YFLogxK
U2 - 10.1109/IIT59782.2023.10366498
DO - 10.1109/IIT59782.2023.10366498
M3 - Conference proceedings published in a book
AN - SCOPUS:85182946412
T3 - 2023 15th International Conference on Innovations in Information Technology, IIT 2023
SP - 196
EP - 201
BT - 2023 15th International Conference on Innovations in Information Technology, IIT 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 November 2023 through 15 November 2023
ER -