ABSTRACT
Recently, cell phone usage has increased incrementally to huge numbers. Statistics show that a total number of mobile phone users worldwide, from 2013 to 2019, is about 60 percent of the Earth's population. This reflects that the use of cell phones is the traditional way of communicating for most of the people in the world. Making calls and sending text messages are the main methods of communication used with cell phones. The purpose of this work is to present "CLogVis," a crime data analysis and visualization system that helps police departments and security agencies connect criminals and suspects by using their cell phone data. Cell phones contain a huge amount of information that helps agencies and police departments in various ways find relationships and connections between criminals. Moreover, information in cell phones will expand in an incremental way when the suspect uses the Internet through their cell phone. In our system, we will be looking to build relationships and connecttions between criminals by using phonebooks and call history (inbound and outbound) to find the relationships between suspects. Regarding the nature of crime organizations, which are built on networks, graph techniques are used to build connections throughout datasets gathered from arrested suspects, criminals, and Telecommunications Service Provider (TSP) log files.
- https://www.statista.com/statistics/274756/forecast-of-mobile-phone-users-in-finland/Google Scholar
- WILLIAM J. BUCHANAN. (2011). The Increasing Challenge of DIGITAL FORENSICS, 10--12. EDINBURGH NAPIER UNIVERSITY, UK.Google Scholar
- D. R. John Arquilla, Networks and Netwars:, www.rang.org, November 2001.Google Scholar
- E. Ferrara, P. De Meo, S. Catanese, and G. Fiumara, "Detecting criminal organizations in mobile phone networks," Expert Systems with Applications, vol. 41, pp. 5733--5750, 2014.Google Scholar
- S. A. Catanese and G. Fiumara, "A visual tool for forensic analysis of mobile phone traffic," in Proceedings of the 2nd ACM workshop on Multimedia in forensics, security, and intelligence, 2010, pp. 71--76. Google ScholarDigital Library
- Marturana, F., Me, G., Bertè, R., & Tacconi, S. (2011). A quantitative approach to triaging in mobile forensics. Proc. 10th IEEE Int. Conf. on Trust, Security and Privacy in Computing and Communications, TrustCom 2011, 8th IEEE Int. Conf. on Embedded Software and Systems, ICESS 2011, 6th Int. Conf. on FCST 2011, 582--588. Google ScholarDigital Library
- Didimo, W., Liotta, G., & Montecchiani, F. (2014). Network visualization for financial crime detection. Journal of Visual Languages and Computing, 25(4), 433--451. Google ScholarDigital Library
- S.Z. Li, A.K. Jain (Eds.), Handbook of Face Recognition, Springer, 2005. Google ScholarDigital Library
- Terrettaz-Zufferey, A. L., Ratle, F., Ribaux, O., Esseiva, P., & Kanevski, M. (2007). Pattern detection in forensic case data using graph theory: Application to heroin cutting agents. Forensic Science International, 167(2--3), 242--246.Google Scholar
- P. Chamikara, D. Yapa, R. Kodituwakku and J. Gunathilake, "SLSecureNet: intelligent policing using data mining techniques,"International Journal of Soft Computing and Engineering, vol. 2, no. 1, pp. 175--180, 2012Google Scholar
- Jayaweera, I., Sajeewa, C., Liyanage, S., Wijewardane, T., Perera, I., & Wijayasiri, A. (2015). Crime analytics: Analysis of crimes through newspaper articles. MERCon 2015 -- MoratuwaGoogle Scholar
- GrTSPos, G. (2010). Exploratory Comparison of Forensic Evidence Recovery Techniques for a Windows Mobile Smartphone, 8(September), 23--36 Google ScholarDigital Library
- Sgaras, C., Kechadi, M. T., & Le-Khac, N. A. (2015). Forensics acquisition and analysis of instant messaging and VoIP applications. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8915, 188--199Google Scholar
- Sai, D. M., Prasad, N. R. G. K., & Dekka, S. (2015). The Forensic Process Analysis of Mobile Device, 6(5), 4847--4850Google Scholar
- K. Ruohonen, Graph Theory, Tampere University of Technology, 2013Google Scholar
- Terrettaz-Zufferey, A. L., Ratle, F., Ribaux, O., Esseiva, P., & Kanevski, M. (2007). Pattern detection in forensic case data using graph theory: Application to heroin cutting agents. Forensic Science International, 167(2--3), 242--246.Google Scholar
Index Terms
- CLogVis: Crime Data Analysis and Visualization Tool
Recommendations
Investigating 'Internet Crimes Against Children' (ICAC) cases in the state of Florida
SAC '06: Proceedings of the 2006 ACM symposium on Applied computingThe purpose of this article is to highlight efforts by the Computer Crime Center at the Florida Department of Law Enforcement (FDLE) to prosecute ICAC cases under their jurisdiction. Section 1 presents an overview of the FDLE ICAC Initiative, a project ...
Cryptography, Law Enforcement, and Mobile Communications
In this issue's installment of Crypto Corner, we review the handset-related methods law enforcement agencies can use to gather evidence during criminal investigations. This article's goal is twofold: explain how law enforcement agencies gather handset-...
The Study of the Interrelation between Law Programs and Digital Forensics in UAE Academia
InfoSecCD '13: Proceedings of the 2013 on InfoSecCD '13: Information Security Curriculum Development ConferenceThe field of digital forensics is growing in the Middle East which is shown by the establishment of technical digital forensic programs in various universities. Even though these programs are important for the development and advancement of the field ...
Comments