Mohammad A. Tayebi

Co-offending Network Mining

We propose here a computational framework for co-offending network mining defined in terms of a process that combines formal data modeling with data mining of large crime and terrorism data sets as gathered and maintained by law enforcement and intelligence agencies. Our crime data analysis aims at exploring relevant properties of criminal networks in arrest-data and is based on 5 years of real-world crime data that was made available for research purposes. This data was retrieved from a large database system with several million data records keeping information for the regions of the Province of British Columbia. Beyond application of innovative data mining techniques for the analysis of the crime data set, we also provide a comprehensive data model applicable to any such data set and link the data model to the analysis techniques. We contend that central aspects considered in the work presented here carry over to a wide range of large data sets studied in intelligence and security informatics to better serve law enforcement and intelligence agencies.

Locating Central Actors in Co-offending Networks

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 model to bridge the conceptual gap between abstract 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.

CrimeWalker: a recommendation model for suspect investigation

Law enforcement and intelligence agencies have long realized that analysis of co-offending networks, networks of offenders who have committed crimes together, is invaluable for crime investigation, crime reduction and prevention. Investigating crime can be a challenging and difficult task, especially in cases with many potential suspects and inconsistent witness accounts or inconsistencies between witness accounts and physical evidence. We present here a novel approach to crime suspect recommendation based on partial knowledge of offenders involved in a crime incident and a known co-offending network. To solve this problem, we propose a random walk based method for recommending the top-K potential suspects. By evaluating the proposed method on a large crime dataset for the Province of British Columbia, Canada, we show experimentally that this method outperforms baseline random walk and association rule-based methods. Additionally, results obtained for public domain data from experiments for co-author recommendation on a DBLP co-authorship network are consistent with those on the crime dataset. Compared to the crime dataset, the performance of all competitors is much better on the DBLP dataset, confirming that crime suspect recommendation is an inherently harder task.

Spatially embedded co-offence prediction using supervised learning

Crime reduction and prevention strategies are essential to increase public safety and reduce the crime costs to society. Law enforcement agencies have long realized the importance of analyzing co-offending networks---networks of offenders who have committed crimes together---for this purpose. Although network structure can contribute significantly to co-offence prediction, research in this area is very limited. Here we address this important problem by proposing a framework for co-offence prediction using supervised learning. Considering the available information about offenders, we introduce social, geographic, geo-social and similarity feature sets which are used for classifying potential negative and positive pairs of offenders. Similar to other social networks, co-offending networks also suffer from a highly skewed distribution of positive and negative pairs. To address the class imbalance problem, we identify three types of criminal cooperation opportunities which help to reduce the class imbalance ratio significantly, while keeping half of the co-offences. The proposed framework is evaluated on a large crime dataset for the Province of British Columbia, Canada. Our experimental evaluation of four different feature sets show that the novel geo-social features are the best predictors. Overall, we experimentally show the high effectiveness of the proposed co-offence prediction framework. We believe that our framework will not only allow law enforcement agencies to improve their crime reduction and prevention strategies, but also offers new criminological insights into criminal link formation between offenders.

Organized Crime Structures in Co-offending Networks

This paper aims at a conceptual foundation for the development of advanced computational methods for analyzing co-offending networks to identify organized crime structures -- i.e., any static or dynamic characteristics of a co-offending network that potentially indicate organized crime or refer to criminal organizations. Specifically, we study networks derived from large real-world crime datasets using social network analysis and data mining techniques. Striving for a coherent and consistent framework to define the problem scope and analysis methods, we propose here a constructive approach that uses mathematical models of crime data and criminal activity as underlying semantic foundation. Organized crime has been defined in a variety of ways, although, so far, there is surprisingly little agreement about its meaning -- at least not at a level of detail and precision required for defining this meaning in abstract computational terms.

Crimetracer: activity space based crime location prediction

Crime reduction and prevention strategies are vital for policymakers and law enforcement to face inevitable increases in urban crime rates as a side effect of the projected growth of urban population by the year 2030. Studies conclude that crime does not occur uniformly across urban landscapes but concentrates in certain areas. This phenomenon has drawn attention to spatial crime analysis, primarily focusing on crime hotspots, areas with disproportionally higher crime density. In this paper we present CRIMETRACER, a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots. We propose a probabilistic model of spatial behavior of known offenders within their activity space. Crime Pattern Theory concludes that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes and serial violent crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our experiments on a large real-world crime dataset show that CRIMETRACER outperforms all other methods used for location recommendation we evaluate here.

Investigating Organized Crime Groups: A Social Network Analysis Perspective

In this paper, we analyze co-offending networks derived from a large real-world crime dataset for the purpose of identifying organized crime structures and their constituent entities. We focus on methodical and analytical aspects in using social network analysis methods and data mining techniques. The goal of our work is to promote computational co-offending network analysis as an effective means for extracting information about criminal organizations from large real-life crime datasets, specifically police-reported crime data. We contend that it would be virtually impossible to obtain such information by using traditional crime analysis methods. For our approach we provide an experimental evaluation with promising results.

Hidden Markov based anomaly detection for water supply systems

Considering the fact that fully immunizing critical infrastructure such as water supply or power grid systems against physical and cyberattacks is not feasible, it is crucial for every public or private sector to invigorate the detective, predictive, and preventive mechanisms to minimize the risk of disruptions, resource loss or damage. This paper proposes a methodical approach to situation analysis and anomaly detection in SCADA-based water supply systems. We model normal system behavior as a hierarchy of hidden semi-Markov models, forming the basis for detecting contextual anomalies of interest in SCADA data. Our experimental evaluation on real-world water supply system data emphasizes the efficacy of our method by significantly outperforming baseline methods.

Exploring the structural characteristics of social networks in a large criminal court database

Social network analysis refers to the study of structural aspects in networks to understand and interpret social entities and related patterns. This form of research has proven to be very useful in the study of illicit networks. To date, however, large criminal court datasets that include a comprehensive scope of cases have yet to be explored. The current work begins to explore this potential by applying social network analysis methods to CourBC-an extensive multi-year database of adult criminal court records in the Province of British Columbia, Canada. Through a variety of network analysis methods, the authors explore the topology and structure of the database. Results demonstrate that the structure of the dataset is similar to that of other large criminal justice datasets yet there are some notable differences. The potential for this type of data in illicit network research and some specific areas for continued research in the field are discussed.

Learning where to inspect: Location learning for crime prediction

Crime studies conclude that crime does not occur evenly across urban landscapes but concentrates in certain areas. Spatial crime analysis, primarily focuses on crime hotspots, areas with disproportionally higher crime density. Using Crime-Tracer, a personalized random walk based approach to spatial crime analysis and crime location prediction outside of hotspots, we propose here a probabilistic model of spatial behavior of known offenders within their activity space. Crime Pattern Theory states that offenders, rather than venture into unknown territory, frequently commit opportunistic crimes by taking advantage of opportunities they encounter in places they are most familiar with as part of their activity space. Our experiments on a large crime dataset show that CRIME TRACER outperforms all other methods used for location recommendation we evaluate here.