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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.
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Size: 3072 |
Author: 丝琪儿 |
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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.
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Size: 312320 |
Author: keerthi |
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Description: For anomaly detection
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Size: 105472 |
Author: needs |
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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.
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Size: 3072 |
Author: 面积 |
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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.
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Size: 400384 |
Author: Sumit |
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