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With the development of the web, online privacy abuse issues has become more and more serious; personal privacy information security has become a major focus. The P3P (Platform for Privacy Preferences) proposed by W3C is a method for protecting user privacy, however, it is not flexible enough to meet the needs of the interaction between users and sites, this technology has not received wide spread...
One of the central areas in network intrusion detection is how to build effective systems that are able to distinguish normal from intrusive traffic. In this paper we explore the use of Genetic Programming (GP) for such a purpose. Although GP has already been studied for this task, the inner features of network intrusion detection have been systematically ignored. To avoid the blind use of GP shown...
Artificial Immune System is a computational system which is inspired from vertebrate immune system. For the characteristics of adaptability and diversity, Artificial Immune System is used to solve engineering problems, especially computer security problems. Danger Model is a new research focus of Artificial Immune System which is inspired from innate immune system. Previous researches have proved...
A prediction method of network security situation based on gen expression programming (PCN_GEP) has been proposed. The main contribution include: (1) Propose cycle-weighted sliding window prediction method to deal with the cycle-changing network risk situation. (2) Fibonacci-weight is added to the preprocess of training dataset to ensure that the latest data have the most impaction to the future....
This paper deals with a combination of work in the fields of artificial intelligence and computer security. It describes a decision model based on a new genetic algorithm approach for intrusion response system (NGAA-IRS). A brief survey of intrusion detection and response system (IDRS), genetic algorithm (GA), and its application to IDRS are presented. Then, the proposed model, parameters and evolution...
Network Intrusion Detection Systems (NIDS) are computer systems which monitor a network with the aim of discerning malicious from benign activity on that network. With the recent growth of the Internet such security limitations are becoming more and more pressing. Most of the current network intrusion detection systems relay on labeled training data. An Unsupervised CA based anomaly detection technique...
This paper presents a novel cellular neural network (CNN) templates learning approach based on Tabu search (TS) for detecting network intrusions. The TS method was applied to CNN with symmetric templates and was verified by simulations. Simulation experiments on intrusion detection have shown that the TS-based template learning algorithm exhibits superior performance in computation time to find the...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training...
The wide spread use of computer networks in today's society, especially the sudden surge in importance of the World Wide Web, has made computer network security an international priority. Since it is not technically feasible to build a system with no vulnerabilities, intrusion detection (ID) has become an important area for researches. An intrusion that deviates only slightly from a pattern derived...
An immune method for real-time computer network security risk evaluation is proposed. The concepts of self, nonself, antigen and immunocyte of computer immune system are defined. The dynamic model of self, marrow model, clone selection, learning scheme, life span of immunocyte are built. A computational model of risk evaluation based on the antibody concentration of memory immunocytes for network...
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