Situational crime prevention moves away from "dispositional" theories of crime commission such as psychosocial factors or genetic makeup of a criminal. Rather than focus on the criminal, SCP focuses on circumstances that lend themselves to crime commission. This enables measures that alter the environmental factors to reduce opportunities for criminal behavior.
Application to cybercrimes It has been suggested that
cybercriminals be assessed in terms of their criminal attributes, which include skills, knowledge, resources, access and motives (SKRAM). These techniques can be specifically adapted to cybercrime as follows:
Increasing the effort Reinforcing targets and restricting access- the use of
firewalls,
encryption, card/password access to ID databases and banning
hacker websites and magazines.
Increasing the risk Reinforcing
authentication procedures and background checks for employees with database access,
tracking keystrokes of computer users, use of photo and thumb print for ID documents/credit cards, requiring additional ID for
online purchases, use of
cameras at
ATMs and at point of sale.
Reducing the rewards Removing targets and disrupting cyberplaces with methods such as monitoring Internet sites and incoming
spam, harsh penalties for hacking, rapid notification of stolen or lost credit cards, avoiding ID numbers on all official documents.
Reducing provocation and excuses Avoiding disputes and temptations helps with maintaining positive employee-management relations and increasing awareness of responsible use policy. Many of these techniques do not require a considerable investment in IT skills or knowledge. Effective utilization and training of existing personnel that is key. Situational crime prevention may be useful in improving
information systems security by decreasing the expected rewards of crime. Many businesses/organizations are heavily dependent on
information and communications technology. While digitised information enables easy access and sharing by users, cybercrime threatens such information, whether committed by an external
hacker or by a trusted member of an organization. After viruses, illicit access and theft of information form the highest percentage of financial loss from cybercrime. Despite many years of computer security research, huge investments in secure operations, and raised training requirements, there are frequent penetrations and data thefts from some of the most heavily protected computer systems in the world. Criminal behavior in
cyberspace is increasing with computers being used for numerous illegal activities, including
email surveillance,
credit card fraud and
software piracy. In the case of computer crime, even cautious companies or businesses that aim to create effective and comprehensive security measures may unintentionally produce an environment which helps provide opportunities because they are using inappropriate controls.
Situational crime prevention and fraud In computer systems that have been developed to design out crime from the environment, one of the tactics used is risk assessment, where business transactions, clients and situations are monitored for any features that indicate a risk of criminal activity.
Credit card fraud has been one of the most complex crimes worldwide. Fraud management techniques, include early warning systems, identifying signs and patterns of different
types of fraud, profiles of users and their activities. Fraud management is a challenging task, including a huge volume of data involved, requiring fast and accurate fraud detection without inconveniencing business operations, identifying evasion of existing techniques, and minimising the risk of false alarms. Generally, fraud detection techniques fall into two categories: statistical techniques and
artificial intelligence techniques. Important statistical data analysis techniques to detect fraud include: • grouping and classification to determine patterns and associations among sets of data, • matching algorithms to identify irregularities in the transactions of users compared to previous proof, and • data pre-processing techniques for validation, correction of errors and estimating incorrect or missing data. Important AI techniques for fraud management are: •
data mining – to categorize and group data and automatically identify associations and rules that may be indicative of remarkable patterns, including those connected to fraud; • specialist systems to program expertise for fraud detection in the shape of rules; •
pattern recognition to identify groups or patterns of behavior either automatically or to match certain inputs; • machine learning techniques to automatically detect the characteristics of fraud; and •
neural networks that can learn suspicious patterns and later identify them. == Application to sexual abuse ==