Education, Training and Awareness (ETA) Four Dimensional cybercrime prevention model

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1 Education, Training and Awareness (ETA) Four Dimensional cybercrime prevention model Education in IT Security - Trends and Questions Askerniya Imran Third year PhD student in University of East London Prof. Hamid Jahankhani Position: Associate Dean - Academic and Collaborative Dr Sin Wee Lee Position: Senior Lecturer and Programme Leader for BSc (Hons) Business Information Systems Abstract: Ameer Al-Nemrat Position: Lecturer Victims of cybercrime around the world often find themselves with no means of resource at a local level. Police authorities have little or no means of investigating e-crime and budgetary controls ensures that only the most serious offences (usually terrorist related or those of a sexual connotation) will ever be investigated. Cross-border cybercrime poses a real threat to global security. Many countries do not have laws in place to combat it, and the international legal framework is patchy. By creating complex and difficult-to-trace internet layers, which cut across many national borders or, by tricking individuals into releasing their personal data; organised crime is often able to operate virtually undetected. Efforts to prevent the cyber crime activity have included such diverse strategies as installation of security tools and companies polices; training and education, improvement to the network architecture to increase the speed and level of security. These interventions differ along multiple dimensions including the population on which they focus, the specific risk and protective factors they address, and the theories underlying their mechanism of change. This research proposes a Four dimensional grid model for classifying cyber crime prevention measures. The proposed model is based on the crime prevention theories and techniques. Introduction Much of today s information is stored in digital form in computers. Information in the digital form is much more easily stored, duplicated and manipulated. Businesses have to use information effectively in order to remain competitive, and storing information in the digital form allows them to use information much more effectively. As most computers are always connected to the Internet, this allows the would-be attacker to gain (unauthorized) access to the computer through the network. Gaining access through the network from a 1

2 remote distance allows the attacker to maintain a safe physical distance from the object being attacked. Various different types of crime are committed on the computer networks. This has given rise to the new term, cyber crime. Pawan (2003) defines cyber crime by stating that Any criminal activity that uses a computer either as an instrumentality, target or a means for perpetuating further crimes comes within the ambit of cyber crime. Stephenson (2000) defines the cyber crime as all criminal activities committed using the computer or against a computer or network. More narrow definitions of the term may restrict the types of crime as those committed against a computer, using a computer network; nevertheless, the term is often used interchangeably without regard for the medium used to commit the crime. Cyber crime therefore has become a term used to denote a collection of different types of attacks on a computer. Cyber crime can be said to be a fact of life today. Bazelon et al (2006) list some of the types of cyber crimes, as; Spam, Viruses and Worms, Sniffers, Distributed denial of service attacks (DDOS), Phising, Auction fraud, Other forms of deception. Businesses are not the only victims of cyber crime. Often individuals also fall victim to the perpetrators of cyber crime. The individuals themselves may be the targets of these criminals, or just unwitting tools used by the criminals to attack other computers. Either way, it is important to ensure that the users have sufficient education and protection against cyber crime, especially in a country like Russia where the usage of Internet is rapidly expanding. Uneducated users are easy prey to the criminals online, and therefore education is an important step to prevent cyber crime. Yang and Hoffstadt (2006) point out that one of the main reasons that cyber crime is increasing is because there are a greater number of people with the technical expertise coming into existence, and also that these additional numbers of people with the requisite technical expertise create the tools that allow the non-technically savvy people to engage in cyber crime. Patterns of Cyber Crime One of the main aims of this work is to detect patterns among the Internet users in Moscow. It may also be pertinent to examine patterns in cyber crime, with a view of finding a correlation between the two. There have been a few attempts to distinguish patterns in cyber crime in current literature. Bagchi and Udo (2003) note that there is a lack of empirical research that analyses the differences between various security breaches and their growth patterns. They also state that the importance of finding these patterns arises from the enormous financial losses sustained as a result of cyber crime. Detecting patterns in cyber crimes is the first step in predicting and subsequently responding to cyber crime. As such it is very important to attempt to detect patterns in cyber crime. Rogers (2003) studies the growth of cyber crime from the point of view of innovation diffusion. He opines that literature on the diffusion of innovation usually only concentrates on good innovations, and thus misses out the study of crime and criminal activity. Bagchi and Udo (2003) study bad innovations using the modified Gompertz model to detect incidences of attack as well as the effect of deterrent activities. Bad innovations are for example the development of novel ways of overcoming computer security, which makes more people fall victim to cyber criminals. Their study shows that historical data on attacks can be used to infer rudimentary patterns of attacks such as that some attacks can be classified to be more serious and given greater attention. They also found that different attacks may show different growth rates similar to the sales of new products, there is a high growth rate in the initial periods, when new types of attacks are discovered. This growth rate tends to 2

3 decrease over time and finally tapers off as newer types of attacks are discovered. Bagchi and Udo also found that exponential or logistic curves are adequate for modelling purposes in the growth phase, but not the earlier stages; similar to chaos theory, a small error in modelling at the early stages can have a large effect on the forecasts. Empirical research in this area is however significantly hindered by the lack of data. Profiling of criminals has long been used in traditional crime fighting. Nykodym et al (2005) explain hat prospective profiling attempts to identify the specific characteristics of the criminal, in order to narrow down the pool of suspects and make the investigation easier. Retrospective profiling is used to link a specific person or persons to a specific crime that have already occurred. Profiling of victims can also help identify the characteristics of the criminal, as does profiling the crime scene. Nykodym et al (2005) explain that profiling criminals is important because not only does it help narrow down the pool of suspects in an investigation, it also helps to link related crimes, identify the potential for escalation of nuisance criminal behaviour to more serious crimes, focusing the investigation, etc. With regards to cyber crime, it appears that the identification of patterns may help in profiling. For example, an attacker may use a specific mode of operation, and target specific victims. Profiling can help identify the victim types, and assist in the identification and installation of sufficient deterrents. For example, in denial of service attacks, if a pattern is detected, such as the requests originating from a section of the Internet, requests from this section can be blocked to ensure that the effect of the attack is minimized. Crime Prevention Models One of the key objectives of the research is to develop a generic cybercrime prevention model based on traditional crime prevention theories. This section is looking at number these models and theories in details. A model (from V.L. modellus, dim. of L. modulus "measure, standard," dim. of modus "manner, measure" (Online Etymology Dic.) is a pattern, plan, representation (especially in miniature), or description designed to show the main object or workings of an object, system, or concept. Also, a model can be a list of operations, which should be executed to reach the success in any process. Tzu-Yen Wang et al. (2009), have presented the Virus prevention model, which is a new technique of identifying the virus activity. Figure below shows this model. In this model, the researchers identified the viruses, by their behaviour which is very different compared to the normal software behaviour. However, merely looking at codeblock (signature) is easily evaded by obfuscated viruses; but it is very hard to manage them. This is a good reason to detect viruses by their program behaviours rather than their 3

4 code signatures. Further, the Tzu-Yen Wang et al, explain the whole structure of the model, block by block. P. Hunton has presented the model of the logical interaction of the major components, which demonstrates how the model can be used in guiding both cybercrime investigation and the broader activities of criminal intelligence analysis. Hereby, we can see the globalised environment, which divided by blocks shows the data objects, exploitation tactics, attack methods and networking technology etc. S. Bressle(2009), called- The Impact of Crime on Business: A Model for Prevention, Detection & Remedy, declares the actual crime situation in the world and its impact on economy and business. In this white paper, crime is separated by two directions; crime committed by employees and crime committed by others. The list of crime, which is more often committed by employees, is listed below: Type of Crime Theft, or skimming of cash Theft of inventory, merchandise or equipment Writing company checks Falsifying revenue reports Processing fraudulent invoices Customer identity theft Money laundering Intellectual property theft Credit card fraud Overstated expense reports Payroll fraud 4

5 Further, the researcher shows the model, which consists of three columns, each of them has the list of measures, which should be taken for prevention, detection and remedies from the crime. As it can be seen here, this model has the recommendation direction and helps to prevent the criminal activity in organisations. In 1996 Robert White created the model, which prevents the crime in three political regimes- Conservative, Liberal and Radical. R. White declares that: from an analytical perspective, it means that the contextual nature of community crime prevention gets lost in terms of specific ideological content or political connotation. We are presented with a scenario that says this model and this technique are necessarily conservative, liberal or radical regardless of human intent, local conditions, history of an area or population group, or general political-economic circumstances. 5

6 Eyndea et al (2003), shows the model of crime prevention, which were created for the police and governments. This model, can help to reduce the crime in streets. The myriad of crime prevention programs that have been implemented can be categorised loosely under three groupings. 1. Situational crime prevention which reduces the opportunity of crime, by manipulation of the physical environment (examples include increased street lighting, security cameras, or introducing responsible serving of alcohol practices in licensed premises). 6

7 2. Community or social intervention which attempts to manipulate the social conditions or institutions that may influence criminal activity (examples include targeting at-risk adolescents, strengthening communities, ant bullying programs in schools). 3. Managerialist crime prevention techniques which are aimed at making the criminal justice and other governmental agencies function more effectively to reduce criminogenic features (National Crime Prevention, 1999; Veno, van den Eynde, & Hart, 2000). Albert D. Farrell et al (2005), describes four dimensional violence prevention grid model, which depends on the social system, level of risk in the target population and developmental stage. This model has been shown to reduce youth violence. (Farrell et al, 2005) discusses both the causes and the consequences of youth violence and also looks at the prevention strategies that have been developed in this context. These strategies are organised and categorised according to the level of social system that they attempt to change, the degree to which they are capable of adaptation for individuals at different levels of risk, the developmental stage of the participants focused on in the strategy, and the overall goals of the programme. Three case studies were used and evaluated. Classifications of youth violence prevention strategies, as they noted previously, have focused on the level of the social system that the programme is attempting to change, the range of individuals and their risks and needs, which are being focuses on, the developmental stage of the participants and the program goals. The conclusion notes that progress is being made in the area of preventing youth violence. The literature shows that different types of youth violence respond well to different types of intervention. The literature has also made progress in understanding the different risk factors and protection factors that either increase or reduce the risks of youths being involved in violence. At the theoretical level, the idea is gaining acceptance that the factors at play in determining youth violence and the preventive responses to youth violence are diverse and varied. This has assisted in the drawing up of new and more complex preventive strategies. Such strategies are being supported by increasing amounts of empirical evidence that finds in favour of certain preventive strategies and against others. The United National General Assembly took an early initiative to call for member states to become aware and prepare necessary legal and administrative procedures to prevent or prosecute cybercrime. Resolution (55/63) on combating the criminal misuse of information technologies adopted on 22 January 2001 is a strong weapon for the prevention of cybercrime. It calls on National governments to respond by adopting the following ten preventive measures (Ajala et al, 2007): 1. States should ensure that their laws and practices eliminate safe havens for those who criminally misuse information technologies. 2. Law enforcement cooperation in the investigation and prosecution of international cases of criminal misuse of information technologies should be coordinated among all concerned states. 3. Information should be exchanged between States regarding the problems that they face in combating the criminal misuse of information technologies. 4. Law enforcement personnel should be trained and equipped to address the criminal misuse of information technologies. 5. Legal systems should protect the confidentiality integrity and availability of data and computer systems from unauthorized impairment and ensure that criminal abuse is penalized. 6. Legal systems should permit the preservation of and quick access to electronic data pertaining to particular criminal investigations. 7

8 7. Mutual assistance regimes should ensure the timely investigation of the criminal misuse of information technologies and the timely gathering and exchange of evidence in such cases. 8. the general public should be made aware of the need to prevent and combat the criminal misuse of information technologies. 9. To the extent practicable, information technologies should be designed to help to prevent and detect criminal misuse, trace criminals and collect evidence. 10. The fight against the criminal misuse of information technologies requires the development of solutions taking into account both the protection of individual freedoms and privacy and the preservation of the capacity of governments to fight such criminal misuse. Perhaps the most useful classification (for practical rather than academic analysis) is the grid developed by Marnix Eysink Smeets (Williams, 2004) in Holland. He calls this classification "putting crime behind bars" and it is shown graphically below: The Foresight agency, developed the Cyber Trust and Crime Prevention Project, has presented the following model of cyber crime prevention strategy: 8

9 Source: Cyber Trust and Crime Prevention Defining the Project, The National Identity Security Strategy in Australia aims to maximise the effectiveness and interoperability of work across all levels of government to combat the misuse of stolen or assumed identities. Identity theft is a major invasion of privacy and a serious concern to the community. To combat identity crime and to better protect identities, Australian governments have all agreed to the development and implementation of the National Identity Security Strategy. The Strategy enhances identification processes through: improved registration and enrolment procedures, including a new system for the electronic verification of documents used as evidence of identity enhanced security features on identity documents issued by government agencies, reducing the risk of forgery ensuring accuracy in the identity information held by government agencies strong authentication standards, and Biometric interoperability, to confirm the identity of individuals. B. Collins et al (2004), has developed a model from a perspective that emphasises the relationships between cyber trust and crime prevention. Just as in other areas of crime prevention, it is necessary to assess whether cyberspace developments will give rise to new conjunctions of criminal opportunity.2 In order to do so, we need to examine features of the components of cyberspace to determine the extent to which people will have greater predispositions to crime, new resources available to them to commit crime and many other factors. At the same time, we need to assess the extent to which those who develop the new cyberspace systems have incentives to adopt measures that will make cyberspace less attractive for criminals and crime promoters. The researchers have underlined that; Crime prevention in the context of cyberspace means reducing the risk of the occurrence of crime and the potential seriousness of crime and disorder events that may occur either in the online or offline world. To achieve this, it is necessary to identify the problems and their causes. Given the relatively recent and rapid development of cyberspace it is not surprising that there are very substantial uncertainties about what future problems will emerge and how they can be tackled. It is clear, however, that cyberspace entails new opportunities for crime because its reliance on networks and communication is such that criminal events may be distributed across geographical space and through time in many new ways. It enables new computer systems and data capture methods that may be vulnerable to attack and, at the same time, offer 9

10 innovative means of responding to criminal activity. Just as the cyberspace system design itself is evolving and adaptive, giving rise to new forms of criminal opportunity, so are the potential offenders tactics and strategies. The solutions to the volutionary arms-race involving cyberspace technologies will undoubtedly lead to new technical design considerations, but their feasibility, in turn, will depend on changing social, cultural, political and economic priorities as well as on a number of crucial ethical considerations. Proposed Cybercrime prevention model A generic cybercrime prevention model is proposed based on the FOUR dimensional grid model of classifying youth violence prevention levels. This model consists of: 1. Education, Training and Awareness (ETA) program for Cyber crime prevention measures 2. Users level of risk 3. Cyber crime categories 4. Goals of the cybercrime prevention effort 10

11 In first dimension of the grid model, the researcher is structured the preventative measures of the model. Education Training. Awareness. According to the situational crime prevention theory, the crime prevention itself, was defined such as the use of measures directed at highly specific forms of crime, which involve the management, design or manipulation of the immediate environment in as systematic and permanent a way as possible (Geason et al, 1986) The Cornish and Clarke (1986), gave us the examples of reducing the crime, using the theory, such as 15% of decreasing the planes hijacking at the early 1970s, by improving the defensive measures in the airports or replacing the aluminium coin boxes by the steel ones, reduced the theft from the public phones in England. (Cornish and Clarke, 1986) The methods of situational crime prevention theory could operate at the different level, such as individual, community or the physical environment. (Bennett 1986). The main idea of the theory mentioned beyond to should follow up the criminals with the new solutions of making the crime as harder as possible. It means, the situational crime prevention theory does not have the boundaries. The theory develops, if the crime develops. In this case, the training is a part of the situational crime prevention theory, because it reduces the success of crime by increasing the skills and knowledge of the users. When someone hears the word training, his / she suppose the classes and teachers, who explain the topics as in the school or university. However, there are some different types of training. According to Taylor (2009), there are main types of training: On-the-job Training and Lectures Computer-Assisted Instruction (CAI) Audiovisual Techniques The awareness is the second item of our cyber crime prevention model. Nowadays, the term awareness is a very popular in the academia and the industry. It used massively in the different areas of computer sciences and the Internet security. The classic definition of the awareness is given in Barney et al. (1964). In his research, was singled out four different types of awareness. An open awareness means that each member of society is aware of the others' true identity and his or her identity in the among the others. A closed awareness means when someone does not know either the other's identity or the other's view of his identity. Further we have a suspicion awareness, which is a modification of the closed one: someone suspects the true identity of the other or the other's view of his own identity, or both. A pretence awareness is a modification of the open one: both interactants are fully aware but pretend not to be.( Barney et al,1964) However, in the Internet the awareness becomes simpler. The Liechti.& Sumi (2002) in their research Editorial: Awareness and the WWW formulate the definition of awareness in the Internet such as: True, awareness is often meant as awareness of other people, and refers to the 11

12 ability to maintain some knowledge about the situation and activities of others. Having a general idea of what is happening, or merely that something is happening, is often already very valuable.. In this definition the key words are something is happening. In the other words, the main idea of awareness in the Internet is to know if there is something unusual and be aware of the harm it may cause. In case of the cyber crime prevention model, we mean the awareness of cyber crime cases. The second dimension in the grid concerns the users levels of risk, which occurs during the online activity. Risk levels are taken from the model suggested by Gordon (1987), who had divided this level into three categories: universal, selective, and indicated. U- Universal means suitable for almost every user, the usual Internet activity (Checking the , accounts on the social network, weather forecast, horoscopes, any professional. articles in the scientific journal, BBC. etc.). The user in this category is not in high risk. S- Selective means any special needs of the users, the users behaviour is more risky, for example many online shopping, gambling, etc. These users are above the average level of risk, because the sensitive information can be intercept during their online activity. I- Indicated means, the users, who are at the high risk level, because of using internet very intensively. This group of users spent a lot of time in the Internet, chatting, downloading, shopping, etc. The third dimension in the grid concerns the types of cyber crime, which the user can be a victim of. There are some definitions of cyber crime categories in the literature. According to some different categorisation such The Business Software Alliance (BSA), Fafinski (2007), and Bernadette Hlubik Schell, Clemens Martin (2004) we categorised the cyber crime into; Financial fraud, E- Harassment or Cyber stalking, Piracy, Hacking, Cyber-terrorism, Online Pornography. Finally, the last fourth grid is representing the goals of the prevention effort, which can be identified by specific behaviours. Some of preventative measures are focused on the particular cyber crime, such as E- harassment or Piracy. Others are more general. The preventative measures are reflected in a variety of cyber crime, which tries to decrease the problem by focusing on the development of training and awareness level of the users. Evaluation of the model The model created in this research project is evaluated in two ways: The expert opinion: The researcher has interviewed two experts, whom one was from of the Russian law enforcement institution and the second from Kaspersky lab. The practical evaluation (questionnaires) Comparative analysis Based on the figures from the questionnaires, the users can be divided into three risk groups that are different by behaviour features, characteristics and information influence which is depends on their education. These groups pertain to the group S in the cyber crime prevention model. The third dimension from the cybercrime categories was selected as the financial fraud for this research. 12

13 Low Risk Group Includes users receiving technical education who assess their computer skills at the level of advanced users or consider themselves to be experts. This group is characterized by high activity on the Internet, use of both credit cards and e-money systems. High level of competence lets them withstand Internet threats, detect them at the right time, they are less exposed to risks than the others. This group makes 27.8% of sample. Medium Risk Group Includes users receiving arts education who assess their computer skills level at the level of beginners and intermediate users. This group is characterized by medium activity on the Internet, use of credit cards very often for doing Internet shopping, distrust towards e-money systems, increased attention towards Internet threats, but insufficient competence to detect and withstand them. This group makes 25.2% of sample. High Risk Group Includes users receiving arts education who assess their computer skills at the level of advanced users but are not such in fact. This group is characterized by high activity on the Internet, prefer e-money systems to credit cards and practically absolutely ignore existence of Internet and generally low level of competency. Such people likely to assess their computer skills not based on technical competence criteria/actual presence of skills but based on time spent behind computer using it for personal needs and for that reason their competence assessments are too subjective, they are less cautious while online and exposed to risks more than the others. It also includes users receiving technical education who assess their computer skills at the level of beginners and intermediate users. This group is characterized by medium activity on the Internet, use of both credit cards and e-money systems to make payments online, quite a high assessment of reliability level of payment systems, low level of cautiousness towards Internet threats, quite objective assessment of their skills. This group makes 46.9% of sample. Comparative analysis differences of users group receiving arts education Questionnaires position Beginning and advanced users experienced users Value of Chi-square 1 Have you used antispyware on your computer during the last 12 months? Yes 49,0% 60,0% No 51,0% 40,0% 1, Significance level higher than 3,8415 shows 95% level of authenticity of differences between users group, rate 2,7055 shows 90% of authenticity of difference. Additional information on the method of test of statistical differences between respondents answers provided in app

14 Have you ever heard about financial fraud on the Internet and harm it may cause? Yes, I know about it and follow all the preventive measures Yes, I know about such crime I hear it is dangerous but I do not know exactly I know nothing about such crime 6,1% 13,3% 71,4% 60,0% 18,4% 24,4% 4,1% 2,2% Do you know what phishing is? 0, Yes 37,5% 40,0% No 62,5% 60,0% 0, Do you think you have been a victim of cyber crime during the last 12 months? Yes, I am sure I have been a victim of a cyber crime I might have been a victim of a cyber crime I do not think I have ever been a victim of a cyber crime 6,1% 4,4% 12,2% 11,1% 59,2% 75,6% 0, Hard to say. 22,4% 8,9% During the last 12 months how many hours a week you averagely used your computer/laptop to access the Internet? Average 28,35 32,91 2, I have updated my computer security software several times for the last 12 month. Average 3,84 4,23 1,

15 At the same time the differences among the respondents with technical education are strongly pronounced. Comparative analysis differences of users group receiving art education Questionnaire position Beginning and advanced users experienced users Value of Chi-square Have you used antispyware on your computer during the last 12 months? Yes 37,2% 68,9% No 62,8% 31,1% 8, Have you ever heard about financial frauds on the Internet and damages they can cause?? Yes, I know about it and follow all the preventive measures Yes, I am aware of such crime I hear it is dangerous but I do not know exactly I know nothing about such crime 11,4% 40,0% 70,5% 48,9% 6,8% 11,1% 11,4% 0,0% Do you know what the phishing is? 8, Yes 29,5% 57,8% No 70,5% 42,2% 7, Do you think you have been a victim of cyber crime during the last 12 months? Yes, I am sure I have been a victim of a cyber crime I might have been a victim of a cyber crime I do not think I have ever been a victim of a cyber crime 2,3% 4,4% 9,1% 20,0% 72,7% 68,9% 3,

16 Hard to say. 15,9% 6,7% During the last 12 months how many hours a week averagely you used your computer/laptop to access the Internet? Average 26,50 39,38 6, I have updated my computer security software several times for the last 12 month. Average 3,50 4,16 8,65104 Besides, in case of real situations with internet threats even without statistically significant values we can say that tendency to negative values mostly observed with the experienced users. Comparative analysis differences of users group receiving art education Arts education For 12 months: Beginning and advanced users Experienced users Average rate Infections reported to the police 0,33 0,00 0,17 Total infections 1,55 0,40 1,00 Hours spent for elimination 8,93 11,12 9,98 Reported to antivirus companies 1,88 0,84 1,38 Conclusion This research has highlighted the importance of awareness as a tool to decrease/ prevent cyber crime. The next stage of research is to validate the Four dimensional model through data mining software, WEKA and create the decision tree which proves the requirement of Education, Training and awareness (ETA) decreasing the level of cyber crime and increase the users knowledge in Moscow region. Based on this information the researcher will develop the framework of the research. 16

17 5. 0 References 1. Albert D. Farrell,Daniel J. Flannery, Youth violence prevention: Are we there yet?, (Available online: %2F30%2F2006&_sk= &view=c&wchp=dGLbVzWzSkzS&md5=a585c0aa7b883a351995e f&ie=/sdarticle.pdf ), [Accessed: Feb. 2010]. 2. Bagchi, K. & Udo, G., An analysis of the growth of computer and Internet security breaches. Communications of the Association for Information Systems (Volume 12, 2003), 684, Bazelon, D.L., Choi, Y.J. & Conaty, J.F., Computer Crimes. American Criminal Law Review, 43, Bennett, W.W., Hess, K.M. & Orthmann, C.M., Criminal investigation, Wadsworth/Thomson Learning. 5. Bressle.S, The Impact of Crime on Business: A Model for Prevention, Detection & Remedy, (Available online: [Accessed: Feb. 2010] 6. Collins. B, Mansell. R, (2004), "Cyber Trust and Crime Prevention: A Synthesis of the State-of-the-Art Science Reviews" (Available online: ), [Accessed: Feb. 2010] 7. Cyber Trust and Crime Prevention Defining the Project, 2004,, (Available online: ct.asp ) [Accessed: Feb. 2010] 8. Hunton. P, The growing phenomenon of crime and the internet: A cybercrime execution and analysis mode, 2009, (Available online: 4&_user=132444&_coverDate=11%2F30%2F2009&_rdoc=1&_fmt=high&_orig=search &_sort=d&_docanchor=&view=c&_searchstrid= &_rerunorigin=google&_ac ct=c &_version=1&_urlversion=0&_userid=132444&md5=468ae9dc9a7a5d0c 22c28397cab93521 ) [Accessed: Feb. 2010] 9. Julie van den Eyndea, Arthur Venob, Alison Hart, They look good but don t work: a case study of global performance indicators in crime prevention, (Available online: %2F01%2F2003&_sk= &view=c&wchp=dGLbVlzzSkWA&md5=4a33e301ce61a18a08264aa687bb7e34&ie=/sdarticle.pdf ). [Accessed: Feb. 2010] 17

18 10. Nykodym, N., Taylor, R. & Vilela, J., Criminal profiling and insider cyber crime. Computer law & security report, 21(5), Rogers, D.E.M., Diffusion of Innovations, Fifth Edition, Simon & Schuster. 12. Tzu-Yen Wang, Chin-Hsiung Wu and Chu-Cheng Hsieh. "A Virus Prevention Model Based on Static Analysis and Data Mining Methods ", 2009.(Available online: 2010] 13. Van Velzen, M., Self-organizing maps. 14. White. R, Situating Crime Prevention: Models, Methods and Political Perspectives,1996 (Available online: ) [Accessed: Feb. 2010] 15. Williams. S, Crime Prevention Information & News, 2004, (Available online: [Accessed: Feb. 2010] 16. Yang, D.W. & Hoffstadt, B.M., Countering the Cyber-Crime Threat. Am. Crim. L. Rev., 43,

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