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33
CrimeNet explorer: a framework for criminal network knowledge discovery
- ACM Transactions on Information Systems (TOIS
, 2005
"... Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not ..."
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Cited by 43 (7 self)
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Knowledge about the structure and organization of criminal networks is important for both crime investigation and the development of effective strategies to prevent crimes. However, except for network visualization, criminal network analysis remains primarily a manual process. Existing tools do not provide advanced structural analysis techniques that allow extraction of network knowledge from large volumes of criminal-justice data. To help law enforcement and intelligence agencies discover criminal network knowledge efficiently and effectively, in this research we pro-posed a framework for automated network analysis and visualization. The framework included four stages: network creation, network partition, structural analysis, and network visualization. Based upon it, we have developed a system called CrimeNet Explorer that incorporates several advanced techniques: a concept space approach, hierarchical clustering, social network analysis methods, and multidimensional scaling. Results from controlled experiments involving student subjects demonstrated that our system could achieve higher clustering recall and precision than did untrained subjects when detecting subgroups from criminal networks. Moreover, subjects iden-tified central members and interaction patterns between groups significantly faster with the help of structural analysis functionality than with only visualization functionality. No significant gain in
Automatically Detecting Deceptive Criminal Identities
, 2004
"... sed Content Analysis) [7]. Police officers are trained to detect lies by observing nonverbal behaviors, analyzing verbal cues, and/or examining physiological variations. Some are also trained as polygraph examiners. Because of the complexity of deception, there is no universal method to detect all t ..."
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Cited by 35 (9 self)
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sed Content Analysis) [7]. Police officers are trained to detect lies by observing nonverbal behaviors, analyzing verbal cues, and/or examining physiological variations. Some are also trained as polygraph examiners. Because of the complexity of deception, there is no universal method to detect all types of deception. Some methods, such as physiological monitoring and behavioral cues examination, can only be conducted while the deception is occurring. Also, there is little research on detecting deception in data where few linguistic patterns exist (for example, profiles containing only names, addresses, and so on). Therefore, existing deception detection techniques developed for applications in communication and physiology are not suitable for discovering deception in identity profiles. It is a common practice for criminals to lie about the particulars of their identity, such as name, date of birth, address, and Social Security number, in order to deceive a police investigator. For a c
Visualizing Criminal Relationships: Comparison of a Hyperbolic Tree and a Hierarchical List
, 2004
"... In crime analysis, law enforcement officials have to process a large amount of criminal data and figure out their relationships. It is important to identify different associations among criminal entities. In this paper, we propose the use of a hyperbolic tree view and a hierarchical list view to vis ..."
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Cited by 16 (4 self)
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In crime analysis, law enforcement officials have to process a large amount of criminal data and figure out their relationships. It is important to identify different associations among criminal entities. In this paper, we propose the use of a hyperbolic tree view and a hierarchical list view to visualize criminal relationships. A prototype system called COPLINK Criminal Relationship Visualizer was developed. An experiment was conducted to test the effectiveness and the efficiency of the two views. The results show that the hyperbolic tree view is more effective for an bidentifyQ task and more efficient for an bassociateQ task. The participants generally thought it was easier to use the hierarchical list, with which they were more familiar. When asked about the usefulness of the two views, about half of the participants thought that the hyperbolic tree was more useful, while the other half thought otherwise. Our results indicate that both views can help in criminal relationship visualization. While the hyperbolic tree view performs better in some tasks, the users' experiences and preferences will impact the decision on choosing the visualization technique.
Automatically detecting criminal identity deception: An adaptive detection algorithm
- IEEE Transactions on Systems, Man and Cybernetics (Part A
, 2005
"... Abstract—Identity deception, specifically identity concealment, is a serious problem encountered in the law enforcement and intelligence communities. In this paper, the authors discuss techniques that can automatically detect identity deception. Most of the existing techniques are experimental and c ..."
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Cited by 10 (2 self)
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Abstract—Identity deception, specifically identity concealment, is a serious problem encountered in the law enforcement and intelligence communities. In this paper, the authors discuss techniques that can automatically detect identity deception. Most of the existing techniques are experimental and cannot be easily applied to real applications because of problems such as missing values and large data size. The authors propose an adaptive detection algorithm that adapts well to incomplete identities with missing values and to large datasets containing millions of records. The authors describe three experiments to show that the algorithm is significantly more efficient than the existing record comparison algorithm with little loss in accuracy. It can identify deception having incomplete identities with high precision. In addition, it demonstrates excellent efficiency and scalability for large databases. A case study conducted in another law enforcement agency shows that the authors ’ algorithm is useful in detecting both intentional deception and unintentional data errors. Index Terms—Efficiency, identity deception, missing value, scalability. I.
Crime Data Mining: An Overview and Case Studies
, 2003
"... The concern about national security has increased significantly since the 9/11 attacks. However, information overload hinders the effective analysis of criminal and terrorist activities. Data mining applied in the context of law enforcement and intelligence analysis holds the promise of alleviating ..."
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Cited by 9 (1 self)
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The concern about national security has increased significantly since the 9/11 attacks. However, information overload hinders the effective analysis of criminal and terrorist activities. Data mining applied in the context of law enforcement and intelligence analysis holds the promise of alleviating such problems. In this paper, we review crime data mining techniques and present four case studies done in our ongoing COPLINK project.
Link Analysis of Higher-Order Paths in Supervised Learning Datasets
- In the Proceedings of the Workshop on Link Analysis, Counterterrorism and Security, 2006 SIAM Conference on Data Mining
, 2006
"... Due to recent concerns with security and terrorism there has been an increasing focus on techniques that discover links and relations in data. Several efforts that employ “data mining” techniques have contributed to this field, but few focus on discovering patterns in sets of higher-order links, whi ..."
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Cited by 7 (4 self)
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Due to recent concerns with security and terrorism there has been an increasing focus on techniques that discover links and relations in data. Several efforts that employ “data mining” techniques have contributed to this field, but few focus on discovering patterns in sets of higher-order links, which can reveal hidden or indirect relationships in data. In this work we focus on the discovery and analysis of higher-order path patterns in a supervised learning dataset. We first analyze higher-order links in the leaf nodes of a decision tree and find evidence for distinguishing between nodes of different classes. Based on these results we next focus on the training data itself used to build the tree. Our results indicate that classes of instances in labeled training data may be separable based on the characteristics of higher-order paths. This technique has potential applications in cybersecurity and cyberforensics, as well as text mining and analytics.
Criminal Identity Deception and Deception Detection in Law Enforcement
"... Criminals often falsify their identities intentionally in order to deter police investigations. In this paper we focus on uncovering patterns of criminal identity deception observed through a case study performed at a local law enforcement agency. We define criminal identity deception based on an un ..."
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Cited by 6 (0 self)
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Criminals often falsify their identities intentionally in order to deter police investigations. In this paper we focus on uncovering patterns of criminal identity deception observed through a case study performed at a local law enforcement agency. We define criminal identity deception based on an understanding of the various theories of deception. We interview a police detective expert and discuss the characteristics of criminal identity deception. A taxonomy for criminal identity deception was built to represent the different patterns that were identified in the case study. We also discuss methods currently employed by law enforcement agencies to detect deception. Police database systems contain little information that can help reveal deceptive identities. Thus, in order to identify deception, police officers rely mainly on investigation. Current methods for detecting deceptive criminal identities are neither effective nor efficient. Therefore we propose an automated solution to help solve this problem.
Criminal networks: Who is the key player
- CEPR Discussion Paper
"... We develop a key-player model by allowing for link formation so that when a person is removed from a network the other individuals can form new links while still optimally providing crime effort. We then put our model to the test, using data on adolescent delinquents in the United States, and provid ..."
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Cited by 6 (1 self)
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We develop a key-player model by allowing for link formation so that when a person is removed from a network the other individuals can form new links while still optimally providing crime effort. We then put our model to the test, using data on adolescent delinquents in the United States, and provide new results regarding the identification of peer effects. This is done by a structural estimation and simulation of our model. Compared to a policy that removes randomly delinquents from the network, a key player policy engenders a crime reduction that can be as large as 35 percent. We discuss how to implement the key-player policy in the real world, primarily within criminal networks, but also within financial, R&D, development, political and tax-evasion networks.
Locating Central Actors in Co-offending Networks
"... Abstract—A co-offending network is a network of offenders who have committed crimes together. Recently different researches have shown that there is a fairly strong concept of network among offenders. Analyzing these networks can help law enforcement agencies in designing more effective strategies f ..."
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Cited by 5 (1 self)
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Abstract—A co-offending network is a network of offenders who have committed crimes together. Recently different researches have shown that there is a fairly strong concept of network among offenders. Analyzing these networks can help law enforcement agencies in designing more effective strategies for crime prevention and reduction. One of the important tasks in co-offending network analysis is central actors identification. In this paper, firstly we introduce a data model, called unified crime data level and co-offending network mining level. Using this data model, we extract the co-offending network of five years real-world crime data. Then we apply different variations of centrality methods on the extracted network and discuss how key player identification and removal can help law enforcement agencies in policy making for crime reduction. I.
Summarization of broadcast news video through link analysis of named entities
- AAAI Workshop on Link Analysis
, 2005
"... This paper describes the use of connections between named entities for summarization of broadcast news. We first extract named entities from a transcript of a news story, and find related entities nearby. In the context of a query, a link graph of relevant entities is rendered in an interactive disp ..."
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Cited by 4 (0 self)
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This paper describes the use of connections between named entities for summarization of broadcast news. We first extract named entities from a transcript of a news story, and find related entities nearby. In the context of a query, a link graph of relevant entities is rendered in an interactive display, allowing the user to manipulate, browse and examine the components, including the ability to play back video clips that mention with interesting relationships. An evaluation of the approach shows that completely automatic summaries from a year of broadcast news can reflect almost 50 % of the entities in a manually created reference summary found on the web. Locations are the most accurate aspect of summarization.
