CISRI: A Crime Investigation System Using the Relative Importance of Information Spreaders in Networks Depicting Criminals Communications

Alzaabi, Mohammed; Taha, Kamal; Martin, Thomas A.

doi:10.1109/tifs.2015.2451073

Cited by 29 publications

(20 citation statements)

References 27 publications

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“…Identifying nodes with central role in information spread will have many potential applications in various fields. For example, by determining the most influential spreaders in criminal groups, the relevant investigation body can better manage crime and implement preventive strategies [144].…”

Section: Centrality Values and Their Influence On Information Spreadmentioning

confidence: 99%

Information cascades in complex networks

Jalili

Perc

2017

Journal of Complex Networks

143

120

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Information cascades are important dynamical processes in complex networks. An information cascade can describe the spreading dynamics of rumour, disease, memes, or marketing campaigns, which initially start from a node or a set of nodes in the network. If conditions are right, information cascades rapidly encompass large parts of the network, thus leading to epidemics or epidemic spreading. Certain network topologies are particularly conducive to epidemics, while others decelerate and even prohibit rapid information spreading. Here we review models that describe information cascades in complex networks, with an emphasis on the role and consequences of node centrality. In particular, we present simulation results on sample networks that reveal just how relevant the centrality of initiator nodes is on the latter development of an information cascade, and we define the spreading influence of a node as the fraction of nodes that is activated as a result of the initial activation of that node. A systemic review of existing results shows that some centrality measures, such as the degree and betweenness, are positively correlated with the spreading influence, while other centrality measures, such as eccentricity and the information index, have negative correlation. A positive correlation implies that choosing a node with the highest centrality value will activate the largest number of nodes, while a negative correlation implies that the node with the lowest centrality value will have the same effect. We discuss possible applications of these results, and we emphasize how information cascades can help us identify nodes with the highest spreading capability in complex networks.

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Section: Centrality Values and Their Influence On Information Spreadmentioning

confidence: 99%

Information cascades in complex networks

Jalili

Perc

2017

Journal of Complex Networks

143

120

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“…Alzaabi et al, proposed a forensic analysis system called CISRI that helps forensic investigators to determine the most influential members of a criminal group using the email exchanges and text messages for the purposes of investigation. It was stated that the proposed system can describe the structural relationships between the members of a criminal group in terms of a graph [17].…”

Section: Related Workmentioning

confidence: 99%

Rating Of The Relationship Between Users Using The Data From The Implemented Mobile Forensic Software

Berber¹,

Küçüksille²

2020

Pamukkale J Eng Sci

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ÖzDuring the digital forensic process, different software and hardware tools are used. The devices from which the digital evidences are collected have been varied in parallel with the developments in technology. The issue of identifying the mobile phone owner's friends and assessing his relationship with them with the help of digital evidences collected from the Android mobile phones has been studied in the literature and it is still under investigation. The software developed in this work enables accessing a variety of data that have evidential value in the court proceedings; these include physical and logical acquisition of images from mobile phones with Android operating system, extracting images for investigations, examining different file types in images, and databases. This software can collect and examine the evidences and then produce reports. At the same time, it can identify criminals or people with potentially have connections to those people whose accounts are under investigations by using developed analysis model which examines the relationships between social media applications` data, phone contacts and calling histories collected from the mobile devices. In this work, the evidences examined by using a novel software developed by the authors which performs multiple tasks using a single interface and the corresponding results are presented.Adli bilişim sürecinde çok çeşitli yazılımlar ve donanımlar kullanılmaktadır. Teknolojinin hızlı gelişimine paralel olarak dijital delillerin toplandığı cihazlar da hızla çeşitlenmektedir. Android mobil telefonlardan toplanan dijital deliller yardımı ile bu telefona sahip kişinin arkadaşlarının ve bu arkadaşları ile ilişkilerinin derecelendirilerek tespit edilmesi, literatürde çalışılmış ve üzerinde çalışılmaya devam eden konulardan biridir. Bu çalışmada geliştirilen yazılım, Android işletim sistemine sahip mobil cihazlardan fiziksel ve mantıksal imaj alma, imajın incelenmek üzere açılması, imaj içinde farklı dosya türlerinin incelenmesi, veri tabanı incelemeleri gibi dijital delil niteliği taşıyan birçok veriye erişilmesini sağlamaktadır. Delil elde etme, delilleri inceleme ve raporlama işlemlerini yapabilen bu yazılım aynı zamanda geliştirilen analiz modeliyle mobil cihazlardan elde edilen sosyal medya uygulama verileri, telefon rehberi ve görüşme kayıtları arasındaki ilişkileri inceleyerek suçlunun veya hesapları incelenen kişilerin, ilişkili olma ihtimali yüksek kişileri tespit edebilmektedir. Birçok işlemi tek bir ara yüzden yapabilmesi ve veri analiz yöntemi bakımından, özgün bir çalışma olarak gerçekleştirilen yazılımla incelenen deliller ve elde edilen bulgular bu çalışmada sunulmuştur.

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“…Some researchers focus on the relation between the members of a criminal group by using a graph theory. Reference [11] supports crime investigation system by using graph theory, which clarifies the relative importance of nodes in a graph with respect to a given set of query nodes. In such a graph, a member of a criminal group represents as a node, the relationship between two members of the group represents with an edge connecting two nodes, and the degree of the relationship between those two members represents by the weight of an edge.…”

Section: Review Of the Related Researchmentioning

confidence: 99%

Semantic Crime Investigation System

Elsayed¹

2018

JCP

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Fusion the direct and indirect evidences with crime knowledge base is important challenge in Automate the crime-solving processes. So, this paper proposed SCIS (Semantic Crime Investigation System), which supports investigator to take a certainty decision based on result of the fusion. The solution proposed in this paper (SCIS) used Ontology re-engineering to create Crime Universal Ontology which includes kinesics Ontology. Also SCIS applied sentiment analysis and image processing techniques. These concepts work together in SCIS in three phases to support the investigator by detailed collected report. The investigator can use SCIS as a mobile application. The proposed system is Automatic, mobile, general, scalable and fast system. Finally, in this paper there is a real case study.

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CISRI: A Crime Investigation System Using the Relative Importance of Information Spreaders in Networks Depicting Criminals Communications

Cited by 29 publications

References 27 publications

Information cascades in complex networks

Information cascades in complex networks

Rating Of The Relationship Between Users Using The Data From The Implemented Mobile Forensic Software

Semantic Crime Investigation System

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