Description: 基于数据挖掘的入侵检测系统的设计与实现
基于数据挖掘的入侵检测系统的设计与实现-Based on Data Mining Intrusion Detection System Design and Implementation Based on Data Mining Intrusion Detection System Design and Implementation Platform: |
Size: 59540 |
Author:Jihong |
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Description: 入侵检测中的数据挖掘方法研究.pdf-A Research into Data Mining Technology in Intrusion Detection. file type pdf Platform: |
Size: 613768 |
Author:赵艳 |
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Description: 入侵检测技术已经成为网络安全领域的研究热点。本文介绍了入侵检测的分类以及应用在入侵检测中的数据挖掘方法，并阐述了构建的基于数据挖掘技术的入侵检测系统的设计与实现。-Intrusion Detection Technology network security has become a hot topic. This paper introduces the intrusion detection and the application of the classification in Intrusion Detection Data Mining Methods and expounded on the data mining technology based Intrusion Detection System Design and Implementation. Platform: |
Size: 6753 |
Author:李显 |
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Description: 入侵检测中的数据挖掘方法研究.pdf-A Research into Data Mining Technology in Intrusion Detection. file type pdf Platform: |
Size: 613376 |
Author:赵艳 |
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Description: 基于数据挖掘的入侵检测系统的设计与实现
基于数据挖掘的入侵检测系统的设计与实现-Based on Data Mining Intrusion Detection System Design and Implementation Based on Data Mining Intrusion Detection System Design and Implementation Platform: |
Size: 59392 |
Author:Jihong |
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Description: 一篇论文,介绍有关基于数据挖掘的入侵检测系统设计方面的内容-A paper on data mining-based intrusion detection system design aspects Platform: |
Size: 154624 |
Author:sifhay |
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Description: 关联规则挖掘的研究工作主要包括：Apriori算法的扩展、数量关联规则挖掘、关联规则增量式更新、无须生成候选项目集的关联规则挖掘、最大频繁项目集挖掘、约束性关联规则挖掘以及并行及分布关联规则挖掘算法等，其中快速挖掘与更新频繁项目集是关联规则挖掘研究的重点，也是多种数据挖掘应用中的技术关键，已用于分类规则挖掘和网络入侵检测等方面的研究。研究者还对数据挖掘的理论进行了有益的探索，将概念格和粗糙集应用于关联规则挖掘中，获得了显著的效果。到目前为止，关联规则的挖掘已经取得了令人瞩目的成绩，包括：单机环境下的关联规则挖掘算法；多值属性关联规则挖掘；关联规则更新算法；基于约束条件的关联规则挖掘；关联规则并行及分布挖掘算法等。-Association rule mining research work include: Apriori algorithm for the expansion of the number of association rules mining, incremental updating of association rules, there is no need to generate candidate itemsets of association rule mining, maximal frequent itemsets mining, association rule mining binding, as well as parallel and Distribution of association rule mining algorithm, one of the rapid mining frequent itemsets and updating of association rules mining are the focus of the study, but also a variety of data mining technology in key applications, has been used in classification rules mining and network intrusion detection studies. The researchers also carried out the theory of data mining has made useful explorations, to concept lattice and rough sets in association rule mining applied to obtain significant results. So far, the mining association rules has made remarkable achievements, including: stand-alone environment for mining association rules algorithm many associatio Platform: |
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Author:henry |
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Description: 基于数据挖掘技术的入侵检测系统的设计思路与模型，以及改进思路-Based on Data Mining Intrusion Detection System Design and model, as well as ideas to improve Platform: |
Size: 261120 |
Author:CCLK |
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Description: 关联规则论文：
GP在入侵检测规则提取中的适应度函数设计.pdf
采用数据挖掘的入侵检测技术研究.pdf
分类规则挖掘算法综述.pdf
-Articles of Association Rules: GP in intrusion detection rule extraction in the design of fitness function. Pdf intrusion detection using data mining technology research. Pdf Classification Rule Mining Algorithm. Pdf Platform: |
Size: 1308672 |
Author:yxm |
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Description: 本文提出一种基于数据挖掘的入侵检测模型,其主要思想是利用数据挖掘的方法,从经预处理的包含网络连接信息的
审计数据中提取能够区分正常和入侵的规则,并用来检测入侵行为。对Apriori 算法中求频繁集时扫描数据库I/O 负载惊人
的问题提出了一种改进办法。为验证该算法的可行性,文章最后实现了该入侵检测模型的知识库中正常连接规则的挖掘。实
验表明该模型能提取特征生成新规则,并证明了方法的可行性和有效性。-In this paper an intrusion detection system based on data mining is proposed, and its main idea is to apply data mining
methods to learn rules that can capture normal and intrusion activities from pre- processed audit data that contain network connection
information. Put forward a method to improve the Apriori algorithm, whose I/O is quite surprising when scanning the database.
To improve the method is feasible the normal rules in the knowledge database in IDS are mined. And the experiment indicates that
the model can produce new rules, which approve the validity and the feasibility of the IDS. Platform: |
Size: 207872 |
Author:youby |
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Description: An innovative knowledge-based methodology for terrorist detection by using Web traffic content as the audit
information is presented. The proposed methodology learns the typical behavior of terrorists by
applying a data mining algorithm to the textual content of terror-related Web sites. The resulting profile is used
by the system to perform real-time detection of users suspected of being engaged in terrorist activities. The
Receiver-Operator Characteristic (ROC) analysis shows that this methodology can outperform a commandbased
intrusion detection system Platform: |
Size: 217088 |
Author:keerthi |
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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: In Information Security, intrusion detection is the act
of detecting actions that attempt to compromise the confidentiality,
integrity or availability of a resource. Intrusion detection does not,
in general, include prevention of intrusions. In this paper, we are
mostly focused on data mining techniques that are being used for
such purposes. We debate on the advantages and disadvantages
of these techniques. Finally we present a new idea on how data
mining can aid IDSs. Platform: |
Size: 372736 |
Author:keerthi |
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Description: There are two main approaches for implementing IDS Host based and Network based. While the former is implemented in
form of software deployed on a host, the latter, usually is built as a
hardware product with its own hardware platform (IDS appliance).
In this paper, a host based intrusion detection system, that uses the
idea of tracing system calls, is introduced. As a program runs, it
uses the services of the underlying operating system to do some
system calls. This system does not exactly need to know the
program codes of each process. Normal and intrusive behaviors are
collected with gathering the sequences of system calls for each
process. Analysis of data is done via data mining and fuzzy
techniques. Data mining is used to extract normal behaviors
(normal unique rules) and Fuzzy to enhance intelligence of the
System. The proposed system is shown to improve the
performance, and decrease size of database, time complexity, and
rate of false alarms.-There are two main approaches for implementing IDS Host based and Network based. While the former is implemented in
form of software deployed on a host, the latter, usually is built as a
hardware product with its own hardware platform (IDS appliance).
In this paper, a host based intrusion detection system, that uses the
idea of tracing system calls, is introduced. As a program runs, it
uses the services of the underlying operating system to do some
system calls. This system does not exactly need to know the
program codes of each process. Normal and intrusive behaviors are
collected with gathering the sequences of system calls for each
process. Analysis of data is done via data mining and fuzzy
techniques. Data mining is used to extract normal behaviors
(normal unique rules) and Fuzzy to enhance intelligence of the
System. The proposed system is shown to improve the
performance, and decrease size of database, time complexity, and
rate of false alarms. Platform: |
Size: 710656 |
Author:keerthi |
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Description: 基于数据挖掘的智能入侵检测系统研究，提出了一个基于数据挖掘、Agent技术的入侵检测系统框架-Intelligent data mining-based intrusion detection system, proposed based on data mining, Agent Intrusion Detection System Platform: |
Size: 101376 |
Author:shenren |
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Description: 针对现有入侵
检测系统的不足,对数据挖掘技术和智能检测代理应用于入侵检测系统进行了研究,提出一个基于数据挖掘技
术的智能入侵检测系统模型-Intrusion detection system for the existing shortage of data mining and intelligent detection agents used in intrusion detection systems have been studied, proposed a data mining technology based on Intelligent Intrusion Detection System Platform: |
Size: 246784 |
Author:shenren |
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Description: The problem of intrusion detection has been studied and received a lot of attention in
machine learning and data mining in the literature survey. The existing techniques are not
effective to improve the classification accuracy and to reduce high false alarm rate.
Therefore, it is necessary to propose new technique for IDS. In this work, we propose a
new K-means clustering method with a different Preprocessing and Genetic Algorithm
for identifying intrusion and classification for both anomaly and misuse.
The experiments of the proposed IDS are performed with KDD cup’99 data set. The
experiments will clearly results the proposed method provides better classification
accuracy over existing method. Platform: |
Size: 400384 |
Author:Sumit |
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Description: This paper gives the description of various data mining techniques that are used to enhance the performance of the Intrusion detection system. Platform: |
Size: 97280 |
Author:deepa |
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