Description: 在网络异常检测中,为了提高对异常状态的检测率，降低对正常状态的误判率,本文提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。- In order to improve the detection rate for anomaly state and reduce the false positive rate for normal state in the network anomaly detection, a novel method of network anomaly detection based on constructing wavelet neural network (WNN) using quantum-behaved particle swarm optimization (QPSO) algorithm was proposed. The WNN was trained by QPSO. Platform: |
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Author:liang |
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Description: 自1980年产生IDS概念以来，已经出现了基于主机和基于网络的入侵检测系统，出现了基于知识的模型识别、异常识别和协议分析等入侵检测技术，并能够对百兆、千兆甚至更高流量的网络系统执行入侵检测。-Since 1980, the concept of generated IDS has been a host-based and network-based intrusion detection system, a model of knowledge-based recognition, identification and protocol anomaly analysis, intrusion detection technology and be able to Fast, Gigabit and even higher flow of the implementation of intrusion detection systems. Platform: |
Size: 3072 |
Author:丝琪儿 |
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Description: 一篇关于DDOS异常检测，找到IP攻击源的论文。对学习网络安全的很有帮助-Anomaly detection on a DDOS to find the source IP attack on the papers. Very helpful for learning network security Platform: |
Size: 408576 |
Author:yihoumei |
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Description: Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today s commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98 detection rate (DR) in comparison with
other existing methods.-Recently, information security has become a key issue
in information technology as the number of computer security
breaches are exposed to an increasing number of security threats. A
variety of intrusion detection systems (IDS) have been employed for
protecting computers and networks from malicious network-based or
host-based attacks by using traditional statistical methods to new data
mining approaches in last decades. However, today s commercially
available intrusion detection systems are signature-based that are not
capable of detecting unknown attacks. In this paper, we present a
new learning algorithm for anomaly based network intrusion
detection system using decision tree algorithm that distinguishes
attacks from normal behaviors and identifies different types of
intrusions. Experimental results on the KDD99 benchmark network
intrusion detection dataset demonstrate that the proposed learning
algorithm achieved 98 detection rate (DR) in comparison with
other existing methods. Platform: |
Size: 312320 |
Author:keerthi |
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Description: This white paper aims at briefly describing the technologies currently used in
filter design in Network-based Intrusion Detection System (NIDS). We will consider
the advantages and drawbacks of using signature filters versus anomaly filters,
and more particularly protocol anomaly filters.
This is the result of research work done at Defcom Sweden, Stockholm. Platform: |
Size: 17408 |
Author:sinsin |
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Description: A taxonomy was developed by Axelsson to define the space of intrusion detection technology and classify IDSs.
The taxonomy categorizes IDSs by their detection principle and their operational aspects. The two main
categories of detection principles are signature detection and anomaly detection. The remainder of this paper
will compare the two categories of detection principles and describe a new type of anomaly detection based on
protocol standards. While the taxonomy applies to both host-based and network-based IDSs,
and more particularly protocol anomaly filters.
This is the result of research work done at Defcom Sweden, Stockholm. Platform: |
Size: 82944 |
Author:sinsin |
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Description: 本文从HMM的基本思想、概念出发，建立了基于网络的HMM异常检测原型。对原型在实际应用环境中产生的问题，提出了对观测对象（数据包头部）进行分段的改进办法，进而建立了具有可操作性的HMM异常检测模型。-From the basic thought and concept of HMM,the article establishes the prototype of HMM anomaly detection based on network.To resolve some problems which produced from the practical application environment,a improved means that partition the observed object to several fields is proposed to improve the prototype,by which we establish the feasible HMM of anomalous detection based on network. Platform: |
Size: 454656 |
Author:陈中 |
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Description: 自1980年产生IDS概念以来，已经出现了基于主机和基于网络的入侵检测系统，出现了基于知识的模型识别、、异常识别和协议分析等入侵检测技术，并能够对百兆、千兆甚至更高流量的网络系统执行入侵检测。 已通过测试。
-Since 1980 to produce the IDS concept, host-based and network-based intrusion detection systems, knowledge-based model identification, anomaly identification and protocol analysis, intrusion detection technology, and on Fast, Gigabit and even more high-traffic network system to perform intrusion detection. Has been tested. Platform: |
Size: 3072 |
Author:面积 |
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Description: 一篇关于DDOS异常检测，找到IP攻攻击源的论文。对学习网络安全的很有帮助
-One on DDOS anomaly detection to find the IP attack attack the source of the paper. Helpful to learn network security Platform: |
Size: 408576 |
Author:fjk2002 |
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Description: 在网络异常检测中,为了提高对异常状态的检测率，降低对正常状态的误判率,本文提出一种基于量子粒子群优化算法训练小波神经网络进行网络异常检测的新方法。利用用量子粒子群优化算法(QPSO)训练小波神经网络,将小波神经网络(WNN)中的参数组合作为优化算法中的一个粒子,在全局空间中搜索具有最优适应值的参数向量。
-This paper presents a new method of network anomaly detection based on quantum particle swarm optimization algorithm to train the wavelet neural network in order to improve the rate of detection of the abnormal state and reduce the false positive rate for normal state in the network anomaly detection. Quantum particle swarm optimization algorithm (QPSO) training wavelet neural network, the combination of parameters of the wavelet neural network (WNN) as a particle optimization algorithm search parameter vector with the best fitness value in the global space. Platform: |
Size: 1024 |
Author:zhou |
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Description: 基于Sharppcap的网络监控，可以解析数据包内容，还有基于熵估计的异常流量检测功能。-Based Sharppcap network monitoring, packet contents can be resolved, there are estimated based on entropy anomaly detection capabilities. Platform: |
Size: 381952 |
Author:tx |
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Description: Andrew Ng Cousera 机器学习 异常检测勇于服务器故障分析以及用于电影推荐的推荐系统的源代码和说明文档。(Andrew Ng Cousera's machine learning implement the anomaly detection algorithm and apply it to detect failing servers on a network. In the second part, you will use collaborative filtering to build a recommender system for movies.) Platform: |
Size: 1676288 |
Author:mark198033
|
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Description: 改进的遗传算法优化的BP神经网络，用于电厂数据的异常检测和故障诊断，已验证有效性。(The improved genetic algorithm optimized BP neural network has been validated for power plant data anomaly detection and fault diagnosis.) Platform: |
Size: 23231488 |
Author:十年12345
|
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Search - network Anomaly Detection