DocumentCode :
660838
Title :
Maximizing Investigation Effectiveness in Digital Forensic Cases
Author :
Kalaimannan, Ezhil ; Gupta, Jatinder N. D. ; Seong-Moo Yoo
Author_Institution :
Dept. of Comput. Eng., Univ. of Alabama in Hutsville, Huntsville, AL, USA
fYear :
2013
fDate :
8-14 Sept. 2013
Firstpage :
618
Lastpage :
623
Abstract :
Forensic investigation refers to the use of science and technology in the process of investigating a crime scene so as to prove that the perpetrator has committed crime in a court of law. There is a need to collect and investigate evidences that are closely related to the nature of the crime in order to achieve the maximum overall effectiveness. There are two main approaches to crime scene investigation: Sequential and Parallel. In the former case, evidences are first collected from the crime scene and then sent to forensic laboratory for investigation while the latter approach deals with the simultaneous collection and investigation of evidences. In the previous work, sequential scenario involving a single investigator for time critical forensics cases has been solved. This paper deals with the sequential scenario involving multiple investigators. The problem of assigning the evidences to multiple investigators and finding their respective investigation times to maximize the overall effectiveness is formulated using a mixed integer linear programming (MILP) model. While the general problem is NP-hard, a heuristic algorithm is proposed to solve the general problem. Experimental results are shown to evaluate the effectiveness of the heuristic to find either optimal or near-optimal solutions. This paper concludes with a summary of findings and some suggestions for future research.
Keywords :
computational complexity; digital forensics; integer programming; law administration; linear programming; MILP; NP-hard problem; crime scene; digital forensic cases; evidence collection; evidence investigation; forensic investigation; forensic laboratory; investigation effectiveness; law; mixed integer linear programming model; near-optimal solutions; time critical forensics cases; Analytical models; Computational modeling; Digital forensics; Educational institutions; Heuristic algorithms; Mathematical model; Digital Evidence; Forensic Investigation; Heuristic Solution; Mixed Integer Programming; Multiple Investigators; NP-hardness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Social Computing (SocialCom), 2013 International Conference on
Conference_Location :
Alexandria, VA
Type :
conf
DOI :
10.1109/SocialCom.2013.93
Filename :
6693390
Link To Document :
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