Data Mining Analytics for Crime Security Investigation and Intrusion Detection

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

Data Mining Techniques for Distributed Denial of Service Attacks Detection in the Internet of Things

This chapter reviews the evolution of the traditional internet into the Internet of Things (IoT). The characteristics and application of the IoT are also reviewed, together with its security concerns in terms of distributed denial of service attacks. The chapter further investigates the state-of-the-art in data mining techniques for Distributed Denial of Service (DDoS) attacks targeting the various infrastructures. The chapter explores the characteristics and pervasiveness of DDoS attacks. It also explores the motives, mechanisms and techniques used to execute a DDoS attack. The chapter further investigates the current data mining techniques that are used to combat and detect these attacks, their advantages and disadvantages are explored. Future direction of the research is also provided.

Automated Identification of Child Abuse in Chat Rooms by Using Data Mining

Providing a safe environment for juveniles and children in online social networks is considered as one of the major factors of improving public safety. Due to the prevalence of the online conversations, mitigating the undesirable effects of child abuse in cyber space has become inevitable. Using automatic ways to combat this kind of crime is challenging and demands efficient and scalable data mining techniques. The problem can be casted as a combination of textual preprocessing in data/text mining and pattern classification in machine learning. This chapter covers different data mining methods including preprocessing, feature extraction and the popular ways of feature enrichment through extracting sentiments and emotional features. A brief tutorial on classification algorithms in the domain of automated predator identification is also presented through the chapter. Finally, the discussion is summarized and the challenges and open issues in this application domain are discussed.

Crime Hotspot Detection

Given a set of crime locations, a statistically significant crime hotspot is an area where the concentration of crimes inside is significantly higher than outside. The motivation of crime hotspot detection is twofold: detecting crime hotspots to focus the deployment of police enforcement and predicting the potential residence of a serial criminal. Crime hotspot detection is computationally challenging due to the difficulty of enumerating all potential hotspot areas, selecting an interest measure to compare these with the overall crime intensity, and testing for statistical significance to reduce chance patterns. This chapter focuses on statistical significant crime hotspots. First, the foundations of spatial scan statistics and its applications (i.e. SaTScan) to circular hotspot detection are reviewed. Next, ring-shaped hotspot detection is introduced. Third, linear hotspot detection is described since most crimes occur along a road network. The chapter concludes with future research directions in crime hotspot detection.

Visual Analytics for Crime Analysis and Decision Support

Today, the amount of digital data increases exponentially due to the rapid growth of the Internet, mobile, and sensory data. Crime data are arriving from multiple sources and formats. The major challenge for crime analysis is to store, manipulate, manage, and analyze data efficiently. To gain useful insight from a great amount of raw data, visual analytics techniques have been drawn attention to law enforcement agencies and researchers. The visual analytics applications do not erase the need for crime analysts' insight. To make better predictions and smarter decisions, data mining, text mining, information visualization, human-computer interaction, and analytics techniques are important to explore. This book chapter provides an overview of different types of crime data, discusses how to analyze and visualize different types of data, and explores popular visualization toolkits that have been used for crime analysis.

Forensic Investigation of Digital Crimes in Healthcare Applications

Healthcare applications are increasingly being used due to the safety and convenience brought to patients' life and healthcare professionals, respectively. Nevertheless, the use of weak authentication techniques and vulnerable communication protocols makes these applications threatened by specific classes of security attacks and e-crimes. The latter threaten the privacy, the safety and even the life of the persons using these applications, due to the fact that they handle sensitive information and implement complex and critical features. This chapter focuses on postmortem investigation of crimes on healthcare applications. After classifying crimes targeting healthcare applications, the requirements for the design of appropriate postmortem investigation system, are discussed. A literature review of proposals related to the investigation of crimes in healthcare applications together with a discussion of the advanced issues are also provided in this chapter.

On the Use of Bayesian Network in Crime Suspect Modelling and Legal Decision Support

Predictive policing (Pp) relates to identifying potentially related offences, similar criminal attributes and potential criminal activity, in order to take actionable measures in deterring crime. Similarly, Legal Decision Making Process (LDMP) considers some level of probabilistic reasoning in deriving logical evidence from crime incidents. Bayesian Networks (BN) have great potential in contributing to the area of Pp and LDMP. Being based on probabilistic reasoning, they can assess uncertainty in crime related attributes and derive useful evidence based on crime incident observations or evidential data. For example, in a particular context of crime investigation, BN based inference could help collect useful evidence about a crime scenario or incident. Such evidence promotes effective legal decision making process and can assist public safety and security agencies in allocating resources in an optimal fashion. This chapter reports on various application areas of BN in the crime domain, highlights the potential of BN and presents “thought experiments” on how offender characteristics could be modelled for decision support in legal matters. The chapter further reports on the performance of empirical analysis in the legal decision support process, in order to elucidate the practical relevance and challenges of using BN in the crime domain.

Visual Data Mining

Current generation criminal justice relies mostly on manual procedures and processes which are time-consuming and error-prone. A polygraph test consists of only “yes” or “no” questions and depends several physiological responses in subjects. It's effectiveness and accuracy have been questioned due to the possibility of swaying the examiner by individuals that are capable of controlling their physical reactions in order to defeat the lie detection exercise. The criminal justice of the future is expected to be empowered by the most modern information and communication technologies to provide various participants in the justice system with a rich set of services such as virtual court presence and hearing participation through visual sensor networks. This chapter revisits the issue of deception detection by proposing visual data mining as a non-invasive alternative to deception detection in next generation criminal justice. Image processing and machine learning techniques are used to accurately detect facial micro-expressions which have been shown to be strong indicators of deception.

A Classification Framework for Data Mining Applications in Criminal Science and Investigations

The importance of data analysis across different domains is growing day by day. This is evident in the fact that crucial information is retrieved through data analysis, using different available tools. The usage of data mining as a tool to uncover the nuggets of critical and crucial information is evident in modern day scenarios. This chapter presents a discussion on the usage of data mining tools and techniques in the area of criminal science and investigations. The application of data mining techniques in criminal science help in understanding the criminal psychology and consequently provides insight into effective measures to curb crime. This chapter provides a state-of-the-art report on the research conducted in this domain of interest by using a classification scheme and providing a road map on the usage of various data mining tools and techniques. Furthermore, the challenges and opportunities in the application of data mining techniques in criminal investigation is explored and detailed in this chapter.

On the Advancement of Using Data Mining for Crime Situation Recognition

The primary role of intelligence organisations and public safety agencies encompasses protecting the lives and property of citizens. However, the urban population growth rate tends to overshadow the available security resources. Thus, the security agencies appear to be more reactive than proactive. Public safety agencies usually have a plethora of under-utilised crime incident reports at their disposal, which if efficiently analysed could reveal some previously unknown useful information. Such information reveals insights into a range of functions in a crime investigation, which can assist in determining criminal trends and in knowledge-driven decision support. This chapter provides an overview of data mining techniques and existing applications used in this domain of interest. Features of existing applications and techniques, such as exploratory basis, model selection, algorithm advancement and result summary, are compared. Future potential of crime data mining, and open research issues, are also discussed.