Results 251 to 260 of about 1,097,686 (283)

A Danger Feature Based Negative Selection Algorithm

2012
This paper proposes a danger feature based negative selection algorithm (DFNSA). The DFNSA divides the danger feature space into four parts, and reserves the information of danger features to the utmost extent, laying a good foundation for measuring the danger of a sample.
Pengtao Zhang, Ying Tan
openaire   +1 more source

Negative Selection Algorithm Based on Double Matching Rules

Advanced Materials Research, 2011
The theory of artificial immune had been widely used in the research of network intrusion detection. Nowadays, the existing detector generating algorithms based on negative selection usually use a certain matching rule, as a result, too many detectors may generate, and the false alarm rate will become more serious.
Yu Hu, Bin Li
openaire   +1 more source

Selective negative correlation learning algorithm for incremental learning

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence), 2008
Negative correlation learning (NCL) is a successful scheme for constructing neural network ensembles. In batch learning mode, NCL outperforms many other ensemble learning approaches. Recently, NCL is also shown to be a potentially powerful approach to incremental learning, while the advantage of NCL has not yet been fully exploited.
null Minlong Lin, null Ke Tang, null Xin Yao   +2 more
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A Novel Negative Selection Algorithm for Recognition Problems

International Journal of Hybrid Information Technology, 2015
In this paper, a novel negative selection algorithm for recognition problems was given. Compared with the traditional negative selection algorithm, a co-stimulation signal was added to start the detectors, which a key factor in immune response. Co-stimulation signal was calculated by the techniques of the statistics and the sliding window, which not ...
Yuan Tao, Min Hu, Yanlin Yu
openaire   +1 more source

A Feedback Negative Selection Algorithm to Anomaly Detection

Third International Conference on Natural Computation (ICNC 2007), 2007
Negative selection algorithm (NSA) lacks adaptability and needs a large number of self elements to build the profile of the system and train detectors. In order to overcome these limitations and build an appropriate profile of the system in a varying self and nonself condition, this paper presents a feedback negative selection algorithm, which is ...
Jinquan Zeng, Tao Li, Xiaojie Liu, Caiming Liu, Lingxi Peng, Feixian Sun   +5 more
openaire   +1 more source

A Matrix Negative Selection Algorithm for Anomaly Detection

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence), 2008
This paper presents a matrix negative selection algorithm for anomaly detection. The proposed algorithm is a twofold improvement over conventional negative selection algorithms. In matrix representation, characteristics of the self set are emerged by multiple vectors to distinctly express the boundary of self and non-self.
null Zhaoxiang Yi, null Xiaodong Mu, null Li Zhang, null Peng Zhao   +3 more
openaire   +1 more source

Anomaly Detection-Based Negative Selection Algorithm

2022
Hanane Chliah, Amal Battou, Omar Baz
openaire   +1 more source

A Algorithm of Detectors Generating Based on Negative Selection Algorithm

2016
There are a lot of redundancy and over all issues in the artificial immune system (AIS) because of using the traditional negative selection algorithm (NSA) to generate detectors. It is the main reason for the high false percentage and high missed percentage in the intrusion detection system (IDS).
Wu Renjie, Guo Xiaoling, Zhang Xiao
openaire   +1 more source

An Improved Artificial Immune Negative Selection Algorithm

2022 7th International Conference on Intelligent Computing and Signal Processing (ICSP), 2022
Xiaojun Zhou, Wei Tan
openaire   +1 more source

Efficient Negative Selection Algorithms by Sampling and Approximate Counting

2012
Negative selection algorithms (NSAs) are immune-inspired anomaly detection schemes that are trained on normal data only: A set of consistent detectors - i.e., detectors that do not match any element of the training data - is generated by rejection sampling.
openaire   +1 more source