检索结果-内蒙古大学图书馆

negative selection algorithm for Unsupervised Anomaly Detection

APPLIED SCIENCES-BASEL 2024年第23期14卷 11040页

作者： Bereta, Michal Cracow Univ Technol Dept Comp Sci PL-31155 Krakow Poland

In this work, we present a modification of the well-known negative selection algorithm (NSA), inspired by the process of T-cell generation in the immune system. The approach employs spherical detectors and was initially developed in the context of semi-supervised anomaly detection. The novelty of this work lies in proposing an adapted version of the NSA for unsupervised anomaly detection. The goal is to develop a method that can be applied to datasets that may not only represent self-data but also contain a small percentage of anomalies, which must be detected without prior knowledge of their locations. The proposed unsupervised algorithm leverages neighborhood sampling and ensemble methods to enhance its performance. We conducted comparative tests with 11 other algorithms across 17 datasets with varying characteristics. The results demonstrate that the proposed algorithm is competitive. The proposed algorithm performs well across multiple metrics, including accuracy, AUC, precision, recall, F1 score, Cohen's kappa, and Matthews correlation coefficient. It consistently ranks among the top algorithms for recall, indicating its effectiveness in scenarios where detecting all existing anomalies is critical, even at the expense of some increase in false positives. Further research is possible and may focus on exploring normalization procedures, improving threshold automation, and extending the method for more detailed anomaly confidence assessments.

关键词： anomaly detection negative selection algorithm unsupervised anomaly detection artificial immune systems outlier detection unsupervised negative selection algorithm

来源：评论

学校读者我要写书评

暂无评论

negative selection algorithm for dengue outbreak detection

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第Sup2期21卷 2345-2356页

作者： Mousavi, Maryam Abu Bakar, Azuraliza Zainudin, Suhaila Awang Long, Zalizah Sahani, Mazrura Vakilian, Mohammadmahdi Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Univ Kuala Lumpur Malaysia Inst Informat Technol Kuala Lumpur Malaysia Univ Kebangsaan Malaysia Fac Hlth Sci Bangi 43600 Selangor Malaysia

Dengue is a critical communicable and vector-borne disease and is becoming a serious concern in Malaysia. It is important to have an early detection system that could provide immediate action, such as the control of dengue transmission at a specific location. However, the available strategy and action may give long-term effects to the community since inaccurate decision making or prediction may lead to other circumstances. Moreover, the need to have a system that can detect the outbreak in a reasonable amount of time is critical. In this study, a nature-inspired computing technique, the artificial immune system (AIS), is used for dengue outbreak detection. One of the variants of the AIS algorithms, called the negative selection algorithm (NSA), has been widely applied in anomaly detection and fault detection. This study aims to employ the NSA for dengue outbreak detection.

关键词： Dengue outbreak artificial immune system negative selection algorithm

来源：评论

学校读者我要写书评

暂无评论

negative selection algorithm Based on Antigen Density Clustering

引用

IEEE ACCESS 2020年 8卷 44967-44975页

作者： Yang, Chao Jia, Lin Chen, Bing-Qiu Wen, Hai-Yang Hubei Univ Sch Comp Sci & Informat Engn Wuhan 430062 Peoples R China Hubei Educ Informatizat Engn Technol Res Ctr Wuhan 430062 Peoples R China Hubei Univ Fac Math & Stat Hubei Key Lab Appl Math Wuhan 430062 Peoples R China

The negative selection algorithm (NSA) is one of the basic algorithms of the artificial immune system. In the traditional negative selection algorithm, candidate detectors are randomly generated without considering the uneven distributions of self-antigens and nonself-antigens, thereby resulting in many redundant detectors, and it is difficult for these detectors to fully cover the area of nonself-antigens. To overcome the problem of low detector generation efficiency, a negative selection algorithm that is based on antigen density clustering (ADC-NSA) is proposed in this paper. The algorithm divides the process of detector generation into three steps: the first step is to calculate the density of the antigens by using the method of antigen density clustering to select nonself-clusters. The second step is to prioritize the abnormal points (nonself-antigens that are not clustered) as the centers of candidate detectors and to generate the detectors via calculation. The third step is to generate the detectors via the traditional algorithm. Detector generation via these three steps can reduce the randomness of the detector generation in the traditional algorithm, thereby improving the efficiency of detector generation. The experimental results demonstrate that on the BCW and KDD-Cup datasets, the negative selection algorithm that is based on antigen density clustering can effectively increase the detection rate while reducing the false-positive rate compared with the traditional negative selection algorithm (RNSA) and two improved algorithms at the same expected coverage.

关键词： Detectors Clustering algorithms Immune system Classification algorithms Prediction algorithms Licenses Artificial immunity negative selection algorithm antigen density clustering detector

来源：评论

学校读者我要写书评

暂无评论

negative selection algorithm with constant detectors for anomaly detection

引用

APPLIED SOFT COMPUTING 2015年 36卷 618-632页

作者： Li, Dong Liu, Shulin Zhang, Hongli Shanghai Univ Sch Mech Engn & Automat Shanghai 200072 Peoples R China Changzhou Univ Sch Petr Engn Changzhou 213164 Peoples R China

In the paper, two novel negative selection algorithms (NSAs) were proposed: FB-NSA and FFB-NSA. FB-NSA has two types of detectors: constant-sized detector (CFB-NSA) and variable-sized detector (VFB-NSA). The detectors of traditional NSA are generated randomly. Even for the same training samples, the position, size, and quantity of the detectors generated in each time are different. In order to eliminate the effect of training times on detectors, in the proposed approaches, detectors are generated in non-random ways. To determine the performances of the approaches, the experiments on 2-dimensional synthetic datasets, Iris dataset and ball bearing fault data were performed. Results show that FB-NSA and FFB-NSA outperforms the other anomaly detection methods in most cases. Besides, CFB-NSA can detect the abnormal degree of mechanical equipment. To determine the performances of CFB-NSA, the experiments on ball bearing fault data were performed. Results show that the abnormal degree based on the CFB-NSA can be used to diagnose the different fault types with the same fault degree, and the same fault type with the different fault degree. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial immune system negative selection algorithm Anomaly detection Bearing

来源：评论

学校读者我要写书评

暂无评论

negative selection algorithm Using Natural Frequency for Novelty Detection under Temperature Variations

Negative Selection Algorithm Using Natural Frequency for Nov...

引用

International Conference on Structures and Building Materials

作者： Li, Miao Ren, Wei-Xin Cent S Univ Dept Civil Engn Changsha 410075 Hunan Peoples R China

ISBN: (纸本)9780878492060

The vibration features are affected by damage in structure and environmental conditions while the bridges are in the operation. Environment effects should not be ignored in making correct diagnoses of structures. negative selection algorithm inspired by immune system has the capability for self-nonself discrimination. Temperature effect on natural frequency is analyzed in the paper, and the algorithm based on Euclidean distance is applied to natural frequencies of structures under temperature variations. The results indicate that negative selection algorithm using natural frequency passes the false-positive tests, and effectively detect the anomalous condition of structure under varying temperature.

关键词： Artificial Immune System negative selection algorithm Temperature Variatio Temperature Effect Novelty Detection Natural Frequency

来源：评论

学校读者我要写书评

暂无评论

negative selection algorithm Based Intrusion Detection Model 20

Negative Selection Algorithm Based Intrusion Detection Model

引用

20th IEEE Mediterranean Eletrotechnical Conference (IEEE MELECON)

作者： Tosin, Salau-Ibrahim Taofeekat Gbenga, Jimoh Rasheed Al Hikmah Univ Dept Comp Sci Ilorin Nigeria Univ Ilorin Fac Commun & Informat Sci Ilorin Nigeria

ISBN: (纸本)9781728152004

The ever-growing security challenges have been a hindrance to the success of Information Technology Innovations due to multifaceted network intrusions. Hence, it becomes imperative to provide tools that can address without compromising integrity, confidentiality and availability of network resources. This paper presents a model for detecting intrusion in a network using negative selection algorithm. negative selection which is Human Immune System (HIS) inspired has been used for anomaly detection due to its self-non-self-discrimination potential. However, it suffers from high rate of false positives and scalability issues. This paper addresses the issues using feature selection to reduce the dimensionality of the dataset. The intrusion detection model is evaluated using NSL-KDD dataset. The results obtained using the benchmark dataset showed that the scalability issue reduced in the proposed approach.

关键词： Intrusion Detection System Artificial Immune System negative selection algorithm Feature selection

来源：评论

学校读者我要写书评

暂无评论

negative selection algorithm Based Unknown Malware Detection Model 10th

Negative Selection Algorithm Based Unknown Malware Detection...

引用

10th International Conference on Bio-Inspired Computing - Theories and Applications (BIC-TA)

作者： Zeng, Jinquan Tang, Weiwen Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Sichuan Commun Res Planning & Designing Co Ltd Chengdu 610041 Peoples R China

ISBN: (纸本)9783662490143;9783662490136

Nowadays, malwares have become one of the most serious security threats for computer systems and how to detect malwares is a difficult task, especially, unknown malwares. Artificial immune systems (AIS) is spired by biological immune system (BIS) and it is a relatively novel field. AIS is used to detect malwares and gets some exciting results. The most known AIS model is negative selection algorithm (NSA) and it can only use normal samples to train. The traditional NSAs generate detectors in the training phase and then detect anomaly elements in the testing phase. There are some drawbacks in the traditional NSAs. Firstly, the real applications often change, normal can change to anomalous, and vice versa. The traditional NSAs easily produce many of false alarm and false negative in the real applications. Secondly, the traditional NSAs lack continuous learning ability in the testing phase and it is costly to generate enough detectors to cover the total non-self space in the training. In order to overcome the drawbacks of the traditional NSAs, a new scheme with online adaptive learning is introduced to NSA, and it includes that constructing the appropriate profile of the system, generating new detectors cover the holes of the non-self space, deleting these detectors which lie in the self-space decreases false alarms and amending these detectors which cover partly self-space decreases false alarm and increase detecting rate.

关键词： Artificial immune systems negative selection algorithm Adaptive learning Malware

来源：评论

学校读者我要写书评

暂无评论

A boundary-fixed negative selection algorithm with online adaptive learning under small samples for anomaly detection

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2016年 50卷 93-105页

作者： Li, Dong Liu, Shulin Zhang, Hongli Shanghai Univ Sch Mechatron Engn & Automat Shanghai 200072 Peoples R China Changzhou Univ Sch Petr Engn Changzhou 213164 Peoples R China

The traditional negative selection algorithm (NSA) lacks online adaptive learning ability, and this restricts its application range. A new NSA, boundary-fixed negative selection algorithm with online adaptive learning under small samples (OALFB-NSA), is proposed in this paper. Boundary-fixed negative selection algorithm (FB-NSA) generates a layer of detectors, which are around the self space. These detectors are only related to the training samples, and have nothing to do with the training times. OALFB-NSA detectors can adapt themselves to real-time variety of self space during the testing stage. Experimental comparison among FB-NSA, V-detector and other anomaly detection algorithms on Iris data sets and biomedical dataset shows that the FB-NSA can obtain the higher detection rate and lower false alarm rate in most cases. The experimental comparison between OALFB-NSA, interface detector with online adaptive learning under small training samples (OALI-detector) and V-detector on Iris data sets shows that when overfitting does not occur, the OALFB-NSA can obtain the higher detection rate and lower false alarm rate, even if only one self sample is used for training. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Artificial immune system negative selection algorithm Anomaly detection Online adaptive learning

来源：评论

学校读者我要写书评

暂无评论

A negative selection algorithm based on hierarchical clustering of self set

引用

Science China(Information Sciences) 2013年第8期56卷 203-215页

作者： CHEN Wen LI Tao LIU XiaoJie ZHANG Bing College of Computer Science Sichuan University National Computer Network Emergency Response Technical Team Coordination Center of China

negative selection algorithm(NSA) is an important method of generating artificial immune ***,the traditional NSAs aim at eliminating the self-recognized invalid detectors,by matching candidate detectors with the whole self *** matching process results in extremely low generation efficiency and significantly limits the application of *** this paper,an improved NSA called CB-RNSA,which is based on the hierarchical clustering of self set,is *** CB-RNSA,the self data is first preprocessed by hierarchical clustering,and then replaced by the self cluster centers to match with candidate detectors in order to reduce the distance calculation *** the detector generation process,the candidate detectors are restricted to the lower coverage space to reduce the detector *** the paper,probabilistic analysis is performed on non-self coverage of ***,termination condition of the detector generation procedure in CB-RNSA is *** is more reasonable than that of traditional NSAs,which are based on predefined detector *** theoretical analysis shows the time complexity of CB-RNSA is irrelevant to the self set ***,the difficult problem,in which the detector training cost is exponentially related to the size of self set in traditional NSAs,is resolved,and the efficiency of the detector generation under a big self set is also *** experimental results show that:under the same data set and expected coverage,the detection rate of CB-RNSA is higher than that of the classic RNSA and V-detector algorithms by 12.3% and 7.4% ***,the false alarm rate is lower by 8.5% and 4.9% respectively,and the time cost of CB-RNSA is lower by 67.6% and 75.7% respectively.

关键词： artificial immune system negative selection algorithm detector cluster

来源：评论

学校读者我要写书评

暂无评论

Clustered negative selection algorithm and fruit fly optimization for email spam detection

引用

JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2019年第1期10卷 143-152页

作者： Chikh, Ramdane Chikhi, Salim Setif 1 Univ Fac Technol Setif Algeria Constantine 2 Univ MISC Lab Constantine Algeria

At present, spam is an actual and increasing problem that compromises email communications across the world. Thus, several solutions have been proposed to stop or reduce the amount of this threat. However, methods based on negative selection algorithm (NSA) lack continuous adaptability and suffer from low detection performance. Moreover, these methods require a large number of detectors to cover all non-self spaces. Thus, this study proposes a new e-mail detection approach based on an improved NSA called combined clustered NSA and fruit fly optimization (CNSA-FFO). The system combines actual NSA with k-means clustering and FFO to enhance the efficiency of classic NSA. Experiments results in spam benchmark show that the performance of CNSA-FFO is better than the classic NSA and NSA-PSO, especially in terms of detection accuracy, positive prediction, and computational complexity.

关键词： Artificial immune system negative selection algorithm E-mail spam Fruit fly optimization K-means clustering

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：