With the growing use of the internet worldwide, internet security becomes more and more important. There are many techniques available for intrusion detection. However, there remain various issues to be improved, such...
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ISBN:
(纸本)9783642550324;9783642550317
With the growing use of the internet worldwide, internet security becomes more and more important. There are many techniques available for intrusion detection. However, there remain various issues to be improved, such as detection rate, false positive rate, memory overhead, time overhead, and so on. In this paper, a new hybrid system for network intrusion detection system using principalcomponentanalysis and C4.5 is presented, which has a good detection rate and keeps false positive and false negative rate at an acceptable level for different types of network attacks. Especially, this system can effectively reduce the memory overhead and the time overhead of building the intrusion detection model. These claims are verified by experimental results on the KDD Cup 99 benchmark network intrusion detection dataset.
This paper presents a new method in order to perform the endmembers extraction with the same accuracy in the results that the well known Winter's N-Finder algorithm but with less computational effort. In particula...
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ISBN:
(纸本)9781628410617
This paper presents a new method in order to perform the endmembers extraction with the same accuracy in the results that the well known Winter's N-Finder algorithm but with less computational effort. In particular, our proposal makes use of the Orthogonal Subspace Projection algorithm, OSP, as well as the information provided by the dimensionality reduction step that takes place prior to the endmembers extraction itself. The results obtained using the proposed methodology demonstrate that more than half of the computing time is saved with negligible variations in the quality of the endmembers extracted, compared with the results obtained with the Winter's N-Finder algorithm. Moreover, this is achieved with independence of the amount of noise and/or the number of endmembers of the hyperspectral image under processing.
The analysis of human behavior and dynamics of groups from various datasets becomes a promising and challenging *** this paper, we present anapplication of principalcomponentanalysis (PCA) in modeling human daily ac...
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The analysis of human behavior and dynamics of groups from various datasets becomes a promising and challenging *** this paper, we present anapplication of principalcomponentanalysis (PCA) in modeling human daily activity based on a dataset from MIT Reality Mining ***, this paper compares real-time data from mobile phones with standard survey ***, group networksare inferred from different type of data mentioned above.
This study's purpose is to introduce eigen-based traffic sign recognition. This technique is based on invoking the principalcomponentanalysis (PCA) algorithm to choose the most effective components of traffic si...
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This study's purpose is to introduce eigen-based traffic sign recognition. This technique is based on invoking the principalcomponentanalysis (PCA) algorithm to choose the most effective components of traffic sign images to classify an unknown traffic sign. A set of weights are computed from the most effective eigen vectors of the traffic sign. By using the Euclidean distance, unknown traffic sign images are then classified. The approach was tested on two different databases of traffic sign's borders and speed limit pictograms that were extracted automatically from real-world images. A classification rate of 96.8 and 97.9% was achieved for these two databases. To check the robustness of this approach, non-traffic sign objects and occluded signs were invoked. A performance of 71% was achieved when occluded signs are used. When signs were rotated 10 degrees around their centre, the performance became 89% when traffic signs' outer shapes were used and for rotated speed limit pictograms the result was 80%.
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