Identifying a Criminal's Network of Trust

Tracing criminal ties and mining evidence from a large network to begin a crime case analysis has been difficult for criminal investigators due to large numbers of nodes and their complex relationships. In this paper, trust networks using blind carbon copy (BCC) emails were formed. We show that our new shortest paths network search algorithm combining shortest paths and network centrality measures can isolate and identify criminals' connections within a trust network. A group of BCC emails out of 1,887,305 Enron email transactions were isolated for this purpose. The algorithm uses two central nodes, most influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology & Internet-Based Systems (Presented at Third International Workshop on Complex Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27, November 2014

Shortlisting the influential members of criminal organizations and identifying their important communication channels

Low-level criminals, who do the legwork in a criminal organization are the most likely to be arrested, whereas the high-level ones tend to avoid attention. But crippling the work of a criminal organizations is not possible unless investigators can identify the most influential, high-level members and monitor their communication channels. Investigators often approach this task by requesting the mobile phone service records of the arrested low-level criminals to identify contacts, and then they build a network model of the organization where each node denotes a criminal and the edges represent communications. Network analysis can be used to infer the most influential criminals and most important communication channels within the network but screening all the nodes and links in a network is laborious and time consuming. Here we propose a new forensic analysis system called IICCC (Identifying Influential Criminals and their Communication Channels) that can effectively and efficiently infer the high-level criminals and short-list the important communication channels in a criminal organization, based on the mobile phone communications of its members. IICCC can also be used to build a network from crime incident reports. We evaluated IICCC experimentally and compared it with five other systems, confirming its superior prediction performance

SIIMCO: A forensic investigation tool for identifying the influential members of a criminal organization

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 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 novelweighting schemes for the edges of networks. We evaluated the quality of SIIMCO by comparing it experimentally with two other systems. Results showed marked improvement

Central Actor Identification of Crime Group using Semantic Social Network Analysis

The Police as law enforcers who authorize in terms of social protection are expected to do both the prevention and investigation efforts also the settlement of criminal cases that occurred in the society. This research can help police to identify the main actor faster and leads to solving crime-cases. The use of overall centrality is very helpful in determining the main actors from other centrality measures. The purpose of this research is to identify the central actor of crimes done by several people. Semantic Social Network Analysis is used to perform central actor identification using five centrality measurements, such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and overall centrality. As for the relationship between actors, this research used social relation such as friendship, colleague, family, date or lover, and acquaintances. The relationship between actors is measured by first four centrality measures then accumulated by overall centrality to determine the main actor. The result showed 80.39% accuracy from 102 criminal cases collected with at least 3 actors involved in each case

Методи виявлення та аналізу кримінальних мереж сформованих на основі білінгової інформації операторів мобільного зв’язку

Nowadays it is difficult to imagine a modern person without such means of communication as the Internet and mobile communications. Almost all modern crimes starting from preparation and to commitment are carried out by using electronic means of communication and leave heterogeneous traces in cyberspace. In accordance with the law some of these tracks are collected and accumulated by law enforcement agencies. Because of large volumes of data they can’t be processed manually. Not long ago a separate scientific direction – analysis of social networks, with analysis of criminal networks as a subdivision, appeared at the crossing of sociology and the theory of complex networks. This paper proposes the structure of expert system aimed at detection and analysis of organized criminal groups on the basis of automatic data processing of billing information of mobile operators. A method is proposed which allows identify criminal groups based on a pool of regular social contacts in telephone communication networks. The proposed method was tested in real social and criminal networks and results are given in this paper. Methods of effective destructive actions planning and implementation of active operational measures in relation to organized crime are described. In addition, the author proposes the method of disclosing internal structure of criminal networks, based on the modification of the famous search algorithm of relevant web pages.Современному человеку все сложней представить себя без использования таких средств коммуникации как Интернет и мобильная связь. Почти все преступления, включая стадию приготовления, совершаются с использованием электронных средств связи, которые оставляют гетерогенные следы в информационном пространстве. Отдельные из таких следов в установленном законом порядке собираются и аккумулируются правоохранительными органами. Большие объемы данных не позволяют обрабатывать их вручную. Не так давно на стыке социологии и теории сложных сетей возникло отдельное научное направление – анализ социального графа, и как его подвид - анализ криминальных сетей. В данной работе предложена архитектура экспертной системы по выявлению и анализу организованных преступных группировок на основе автоматической обработки биллинговой информации операторов мобильной связи. Предлагается метод, благодаря которому возникает возможность выявления преступных группировок из совокупности простых социальных контактов в сетях телефонной связи. Также, приводятся результаты использования разработанного метода на реальных социальных и криминальных сетях. Описываются методы планирования эффективного деструктивного воздействия и проведения активных мероприятий против организованной преступности. Кроме того, автором предлагается метод анализа внутренней структуры криминальных сетей, основанный на модификации известного алгоритма поиска релевантных веб-страниц.Сучасній людині все складніше уявити себе без використання таких засобів комунікації як Інтернет та мобільний зв’язок. Майже всі сучасні злочини зі стадії готування до вчинення здійснюються з використанням електронних засобів зв’язку та залишають гетерогенні сліди в інформаційному просторі. Окремі з таких слідів у встановленому законом порядку збираються та акумулюються правоохоронними органами. Великі об’єми цих даних не дають можливості їх неавтоматичної обробки. Не так давно на стику соціології та теорії складних мереж виник окремий науковий напрям – аналіз соціального графу, підвидом якого є аналіз кримінальних мереж. В даній роботі запропонована архітектура експертної системи з виявлення та аналізу організованих злочинних угрупувань на основі автоматизованої обробки білінгової інформації операторів мобільного зв’язку. До уваги пропонується метод, завдяки якому можливо ідентифікувати кримінальні угрупування із сукупності звичайних соціальних контактів в мережах телефонного зв’язку. Також, приводяться результати застосування розробленого методу на реальних соціальних та кримінальних мережах. Описуються методи планування ефективного деструктивного впливу та проведення активних заходів проти організованої злочинності. Окрім того, автором пропонується метод викриття внутрішньої структури кримінальних мереж, заснований на модифікації відомого алгоритму пошуку релевантних веб-сторінок

Few are as Good as Many: An Ontology-Based Tweet Spam Detection Approach

Due to the high popularity of Twitter, spammers tend to favor its use in spreading their commercial messages. In the context of detecting twitter spams, different statistical and behavioral analysis approaches were proposed. However, these techniques suffer from many limitations due to (1) ongoing changes to Twitter\u2019s streaming API which constrains access to a user\u2019s list of followers/followees, (2) spammer\u2019s creativity in building diverse messages, (3) use of embedded links and new accounts, and (4) need for analyzing different characteristics about users without their consent. To address the aforementioned challenges, we propose a novel ontology-based approach for spam detection over Twitter during events by analyzing the relationship between ham user tweets vs. spams. Our approach relies solely on public tweet messages while performing the analysis and classification tasks. In this context, ontologies are derived and used to generate a dictionary that validates real tweet messages from random topics. Similarity ratio among the dictionary and tweets is used to reflect the legitimacy of the messages. Experiments conducted on real tweet data illustrate that message-to-message techniques achieved a low detection rate compared to our ontology based approach which outperforms them by approximately 200%, in addition to promising scalability for large data analysis

Multi-objective community detection applied to social and COVID-19 constructed networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University LondonCommunity Detection plays an integral part in network analysis, as it facilitates understanding the structures and functional characteristics of the network. Communities organize real-world networks into densely connected groups of nodes. This thesis provides a critical analysis of the Community Detection and highlights the main areas including algorithms, evaluation metrics, applications, and datasets in social networks. After defining the research gap, this thesis proposes two Attribute-Based Label Propagation algorithms that maximizes both Modularity and homogeneity. Homogeneity is considered as an objective function one time, and as a constraint another time. To better capture the homogeneity of real-world networks, a new Penalized Homogeneity degree (PHd) is proposed, that can be easily personalized based on the network characteristics. For the first time, COVID-19 tracing data are utilized to form two dataset networks: one is based on the virus transition between the world countries. While the second dataset is an attributed network based on the virus transition among the contact-tracing in the Kingdom of Bahrain. This type of networks that is concerned in tracking a disease was not formed based on COVID-19 virus and has never been studied as a community detection problem. The proposed datasets are validated and tested in several experiments. The proposed Penalized Homogeneity measure is personalized and used to evaluate the proposed attributed network. Extensive experiments and analysis are carried out to evaluate the proposed methods and benchmark the results with other well-known algorithms. The results are compared in terms of Modularity, proposed PHd, and accuracy measures. The proposed methods have achieved maximum performance among other methods, with 26.6% better performance in Modularity, and 33.96% in PHd on the proposed dataset, as well as noteworthy results on benchmarking datasets with improvement in Modularity measures of 7.24%, and 4.96% respectively, and proposed PHd values 27% and 81.9%

A Survey of Social Network Forensics

Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks