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: |
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Author:yxm |
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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: |
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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 recent years and within the intrusion detection
domain, an increasingly evident trend has emerged.
The trend stands within the crossroads of multi-agent systems and data mining. The documents present some related works introducing distributed intrusion detection architectures using the multi-agent design methodology and the data mining techniques.-In recent years and within the intrusion detection
domain, an increasingly evident trend has emerged.
The trend stands within the crossroads of multi-agent systems and data mining. The documents present some related works introducing distributed intrusion detection architectures using the multi-agent design methodology and the data mining techniques. Platform: |
Size: 6524928 |
Author:i |
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