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: 分析了数据包调用检测函数匹配规则节点的平均次数计算函数，并用该函数对Snort系统规则树进行了优化，减少了数据包过滤匹配次数，极大提高了入侵检测效率-Analysis of packet matching rules called detection function of the average number of nodes the calculation function, and the system function of the Snort rules tree is optimized to reduce the number of packets matching filter, which greatly improved the efficiency of intrusion detection Platform: |
Size: 122880 |
Author:TTstone |
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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: 一个VC++ 网络入侵检测模块及程序代码，源代码完整，包括所需控件都在压缩包内，用VC编写网络软件的时候用得上。
CMainFrame* p_Frame //保持筐架类的指针
CEdit* m_pEdit //保存编辑框类的指针
CListCtrl* m_pList //保存列表类的指针
CTreeCtrl* m_pTree //保存树型控件的指针
CComboBox* m_pComboBox //保存组合筐的指针
以下的成员变量保存了最后一次分析的数据包的各种数据
ip_header* m_pih //ip头 保存最后一次分析的数据包的ip头
tcp_header* m_ptcph //tcp头 保存最后一次分析的数据包的tcp头
udp_header* m_pudph //udp头 保存最后一次分析的数据包的udp头-A VC network intrusion detection module and program code, complete source code, including the necessary controls are in the compressed package, prepared by VC when the lingua franca of network software.
CMainFrame* p_Frame // pointer to keep basket racks
CEdit* m_pEdit // Save pointer to edit box type
CListCtrl* m_pList // save the pointer to the class list
CTreeCtrl* m_pTree // save the pointer to the control tree
CComboBox* m_pComboBox // save pointer combination basket
The following member variable holds the last analysis, all data packets
ip_header* m_pih // ip header to save the last analysis the ip packet header
tcp_header* m_ptcph // tcp header to save the last analysis of the tcp packet header
udp_header* m_pudph // udp header to save the last analysis the udp packet header Platform: |
Size: 1563648 |
Author:云梦泽 |
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Description: 用决策树改进基于协议分析的入侵检测技术的实现-mprovement using decision tree based on protocol analysis, intrusion detection technology Platform: |
Size: 118784 |
Author:君 |
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Search - intrusion detection tree