By David A. Elizondo, Agusti Solanas, Antoni Martinez-Balleste
The e-book is a set of invited papers on Computational Intelligence for privateness and safeguard. the vast majority of the chapters are prolonged types of works offered on the designated consultation on Computational Intelligence for privateness and safety of the foreign Joint convention on Neural Networks (IJCNN-2010) held July 2010 in Barcelona, Spain.
The publication is dedicated to Computational Intelligence for privateness and safety. It presents an summary of the newest advances at the Computational Intelligence options being constructed for privateness and safeguard. The e-book might be of curiosity to researchers in and lecturers and to post-graduate scholars drawn to the newest advances and advancements within the box of Computational Intelligence for privateness and defense.
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Additional info for Computational Intelligence for Privacy and Security
In addition, fuzzy rules can easily be contracted to detect common security attacks by using if-then rules. This way, attacks can be classified according to their degree of dangerousness . Fuzzy rules can be written by security administrators using their experience. The fuzzy expert can then verify that the rules cover all inputs . Therefore, attacks can easily be detected with fuzzy systems based on sets of rules derived from the expert knowledge of the security administrator. Application of Fuzzy Logic in Computer Security and Forensics 45 Computer viruses and malicious software can be detected by traditional antivirus software such as Kaspersky1 and Norton Antivirus2 .
In: Computer Phoenix, AZ, pp. 371–377. : Using Genetic Algorithm or Network Intrusion Detection. In: Proceedings of the United States Department of Energy Cyber Security Group 2004 Training Conference (CD ROM) CiteSeerX, p. 8. : Computational Intelligence towards Computer Security and Forensics. : ‘A Software Implementation of a Genetic Algorithm Based Approach to Network Intrusion Detection. In: Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks, pp.
Amro et al. produced by a large number of programmers . This can be achieved by using the three basic parts of GA, namely, genes, organisms and populations, as detailed next. First, genes provide the individual problems that will be optimised. A set of metrics active in the classifier is optimised in order to increase the precision of the classification. The classifier is interfaced to the genetic algorithm so that, instead of outputting a ranked list of authors for each project in both testing sets, the classifier produced a single list identifying the number of correct classifications at each level of precision.