IEEE Transactions on Software Engineering - Special issue on computer security and privacy
C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Towards a taxonomy of intrusion-detection systems
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue on computer network security
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Mining system audit data: opportunities and challenges
ACM SIGMOD Record
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Data mining approaches for intrusion detection
SSYM'98 Proceedings of the 7th conference on USENIX Security Symposium - Volume 7
An overview of anomaly detection techniques: Existing solutions and latest technological trends
Computer Networks: The International Journal of Computer and Telecommunications Networking
A data mining approach to assessing the extent of damage of missing values in survey
International Journal of Business Intelligence and Data Mining
Missing Data Imputation Techniques
International Journal of Business Intelligence and Data Mining
Preprocessing enhancements to improve data mining algorithms
International Journal of Business Intelligence and Data Mining
A survey of data mining techniques for malware detection using file features
Proceedings of the 46th Annual Southeast Regional Conference on XX
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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Cybercrime detection solutions have recently received increased attention. Predicting the cybercrime potentiality of a request received by a server can reduce the risk of cybercrime. In this paper, we present an alternative solution to the current intrusion detection systems in that the socio-economic characteristics of IP geo-locations of a request are used to predict its crime potentiality. The IP address of a request is used to exploit its socio-economic characteristics. Using the IP address of a request, the physical location, from where the request has been sent, is identified. Socio-economic attributes of people living in that area are collected. These characteristics can specify the seriousness of a cybercrime associated with a request. Classification algorithms can be used to build a prediction model. We have conducted a case study in which we built a prediction model using a set of socio-economic attributes. Our results show the applicability of the proposed model.