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At present, the focus of network security research mainly centers on the increase of detection ability of a single detection tool. For example, improve the accuracy and detection efficiency of firewall and intrusion detection system but despise the defense ability of the whole network system. This paper puts forward the concept of network active defense system and emphatically analyzes its architecture,...
For assessing the security and optimal strengthening of large enterprise networks, this paper proposes a new approach uses configuration information on firewalls and vulnerability information on all network devices to build defense graphs that show the attack and defense strategy. Some models including a defense graph model, attack-defense taxonomy and cost quantitative model, and Attack-Defense Game...
With the progression of time, we have been blessed with the gifts of science. Computer networks are one of those gifts. But as the network proceeded, intrusions and misuses followed. Consequently, network security has come to the fore front and has become one of the most important issues. Now-a-days intrusion detection systems have become a standard component in security infrastructures. Intrusions...
This paper describes a forensic logging system that collects fine-grained evidence from target servers and networks. For the logging system, we developed a TCSEC-B1 level secure operating system and a dedicated network processor that collects network traffic. The logging system is also capable of protecting servers from malicious attacks as well as allowing security managers to obtain forensic evidences...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
In this paper, we study the problem of anomaly detection in high-dimensional network streams. We have developed a new technique, called Stream Projected Outlier deTector (SPOT), to deal with the problem of anomaly detection from high-dimensional data streams. We conduct a case study of SPOT in this paper by deploying it on 1999 KDD Intrusion Detection application. Innovative approaches for training...
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