6,943 research outputs found
An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection
The need to increase accuracy in detecting sophisticated cyber attacks poses
a great challenge not only to the research community but also to corporations.
So far, many approaches have been proposed to cope with this threat. Among
them, data mining has brought on remarkable contributions to the intrusion
detection problem. However, the generalization ability of data mining-based
methods remains limited, and hence detecting sophisticated attacks remains a
tough task. In this thread, we present a novel method based on both clustering
and classification for developing an efficient intrusion detection system
(IDS). The key idea is to take useful information exploited from fuzzy
clustering into account for the process of building an IDS. To this aim, we
first present cornerstones to construct additional cluster features for a
training set. Then, we come up with an algorithm to generate an IDS based on
such cluster features and the original input features. Finally, we
experimentally prove that our method outperforms several well-known methods.Comment: 15th East-European Conference on Advances and Databases and
Information Systems (ADBIS 11), Vienna : Austria (2011
Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection
There has been a recent emergence of sampling-based techniques for estimating
epistemic uncertainty in deep neural networks. While these methods can be
applied to classification or semantic segmentation tasks by simply averaging
samples, this is not the case for object detection, where detection sample
bounding boxes must be accurately associated and merged. A weak merging
strategy can significantly degrade the performance of the detector and yield an
unreliable uncertainty measure. This paper provides the first in-depth
investigation of the effect of different association and merging strategies. We
compare different combinations of three spatial and two semantic affinity
measures with four clustering methods for MC Dropout with a Single Shot
Multi-Box Detector. Our results show that the correct choice of
affinity-clustering combination can greatly improve the effectiveness of the
classification and spatial uncertainty estimation and the resulting object
detection performance. We base our evaluation on a new mix of datasets that
emulate near open-set conditions (semantically similar unknown classes),
distant open-set conditions (semantically dissimilar unknown classes) and the
common closed-set conditions (only known classes).Comment: to appear in IEEE International Conference on Robotics and Automation
2019 (ICRA 2019
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