SIIMCO: A Forensic Investigation Tool for Identifying the Influential Members of a Criminal Organization

Abstract

Abstract-Members of a criminal organization, who hold central positions in the organization, are usually targeted by criminal investigators for removal or surveillance. This is because they play key and influential roles by acting as commanders who issue instructions or serve as gatekeepers. Removing these central members (i.e., influential members) is most likely to disrupt the organization and put it out of business. Most often, criminal investigators are even more interested in knowing the portion of these influential members, who are the immediate leaders of lower-level criminals. These lower-level criminals are the ones who usually carry out the criminal works; therefore, they are easier to identify. The ultimate goal of investigators is to identify the immediate leaders of these lower-level criminals in order to disrupt future crimes. We propose in this paper a forensic analysis system called SIIMCO that can identify the influential members of a criminal organization. Given a list of lower-level criminals in a criminal organization, SIIMCO can also identify the immediate leaders of these criminals. SIIMCO first constructs a network representing a criminal organization from either Mobile Communication Data that belongs to the organization or from crime incident reports. It adopts the concept space approach to automatically construct a network from crime incident reports. In such a network, a vertex represents an individual criminal and a link represents the relationship between two criminals. SIIMCO employs formulas that quantify the degree of influence/importance of each vertex in the network relative to all other vertices. We present these formulas through a series of refinements. All the formulas incorporate novel-weighting schemes for the edges of networks. We evaluated the quality of SIIMCO by comparing it experimentally with two other systems. Results showed marked improvement.

Survey on Crime Analysis and Prediction Using Data Mining Techniques

“…Taha et al [9] has developed a forensic investigation tool for identifying the influential members who create an impact in a criminal organization. The immediate leaders can also be identified in a criminal organization.…”

“…The communication based methods describe the identification of the leaders in a criminal organization may be a tedious process. Kamal Taha et al [9] produced an approach through the phone calls and other communication data such as call logs and records, the influential members on a crime organization can be tracked. Kevin Sheehy, Thomas Rehbreger, Andrew O'Shea, William Hammond, Charlotte Blais, Michael Smith K., Preston White, Jr., Neal Goodloe [10] introduced an approach to categorize and identify the mentally ill prisoners among the prisoners and keep them separate from other prisoners to avoid conflict and injuries between them.…”

R2BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

“…The index in (1) indicates that it is the output of the i th neuron of the network and the double bar denotes the Euclidean distance between vectors. Besides Euclidean distance, Mahalanobis distance could also be useful as it may give better results in some situations [27].…”

“…Keyboard dynamics refers to the process of identifying the unique patterns of an individual's behaviour with a computer based keyboard device. It is closely related to the study of behavioural biometrics in digital forensics [1] [2]. Examples include gait, speech patterns, signatures and keystrokes.…”

Supervised machine learning for classification mining and ranking of illegal web contents = Aprendizaje automático supervisado para la clasificación, extracción de conocimiento y ordenación de contenidos web ilegales

“…Those words are denominated Named Entities (NE), and several applications of NLP use them, such as text summarization (Aramaki et al, 2009), life events detection (Khodabakhsh et al, 2018), contents filtering and monitoring (Hidalgo et al, 2005), trends detection (Kontostathis et al, 2004), or even mining the Surface Web and the social networks (Ritter et al, 2011a;Whitelaw et al, 2008). In addition to the uses mentioned above, decision support systems exploit NER systems to identify fake news (Shaori Al-Ash and Wibowo, 2018), analyze crime patterns (Das and Das, 2018), detect criminal networks (Silvestre Castillo, 2019), and for detecting the influential members of a criminal organization (Taha and Yoo, 2016). NEs are considered as a cornerstone for building a knowledge graph that depicts the relationships between the recognized entities (Usbeck et al, 2014;Hasegawa et al, 2004).…”