The Z notation: a reference manual
The Z notation: a reference manual
Neural Fraud Detection in Mobile Phone Operations
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
An Architecture for Intrusion Detection Using Autonomous Agents
ACSAC '98 Proceedings of the 14th Annual Computer Security Applications Conference
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An immunological model of distributed detection and its application to computer security
An immunological model of distributed detection and its application to computer security
IPDPS '05 Proceedings of the 19th IEEE International Parallel and Distributed Processing Symposium (IPDPS'05) - Workshop 6 - Volume 07
Mutual information-based feature selection for intrusion detection systems
Journal of Network and Computer Applications
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Recent years have seen a growing interest in computational methods based upon natural phenomena with biologically inspired techniques, such as cellular automata, immune human systems, neural networks, DNA and molecular computing. Some of these techniques are classified under the realm of a general paradigm, called bio-computing. In this paper, we propose a security system for fraud detection of intruders and improper use of both computer system and mobile telecommunication operations. Our technique is based upon data analysis inspired by the natural immune human system. We show how immune metaphors can be used efficiently to tackle this challenging problem. We also describe how our scheme extracts salient features of the immune human system and maps them within a software package designed to identify security violations of a computer system and anusual activities according to the usage log files. Our results indicate that our system shows a significant size reduction of the logs file (i.e., registration of each log activity), and thereby the size of the report maintained by the computer system manager. This might help the system manager to monitor and observe unusual activities on the machine hosts more efficiently, as they happen, and can act accordingly before it is too late. Last but not least, we propose an intrusion and fraud detection model based upon immune human analogy for mobile phone operations. We discuss our model and present its specification using the Z Language.