http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : Shi XueKui (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
-
Research on Computer Network Virus Defense Technology in Cloud Technology Environmen
Zhao Sheng,Han HuiShan,Shi XueKui 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.3
With the rapid development of the Internet, the antivirus software of the network is always emerging and constantly changing. Traditional detection methods can’t effectively kill the new viruses and malicious software, the complexity of which also makes itself easy to be attacked by malicious software. The emergence of cloud computing has changed the status quo. Therefore, the architecture model of virus malware detection based on cloud computing is proposed in this paper. Based on the combination of the method for detecting malicious software virus based on cloud computing and the algorithm analysis theory in machine learning, a new type of distributed CFO algorithm is proposed, and the closed environment of cloud computing virtual machine nodes is used to realize dynamic behavior monitoring to the virus malware, then the distributed fluctuations PIF algorithm is used to describe the process of dynamic analysis and analysis reporting, besides, the wave algorithm is carried out corresponding improvement based on the analysis of the environment. Experimental results show that the model can detect the conditional trigger behavior of virus malware, so as to find the conditions for triggering malicious behavior and the input data that satisfy these conditions and the performance of this monitoring system is greatly improved compared with the common single machine system.
-
Research on Data Intrusion Detection Technology based on Fuzzy Algorithm
Sheng Zhao,Huishan Han,Xuekui Shi 보안공학연구지원센터 2016 International Journal of Security and Its Applicat Vol.10 No.8
The computer system is becoming more complex and massive network data, which brings great difficulties to the traditional intrusion detection system. Intrusion detection system is an important part of the network and information security architecture, which is mainly used to distinguish the normal activities of the system and the suspicious and intrusion patterns. But the challenge is how to effectively detect network intrusion behavior in order to reduce the false alarm rate and false negative rate. Based on the shortcomings of existing intrusion detection methods, the fuzzy C- means clustering method is proposed to analyze the intrusion detection data, so as to find out the abnormal network behavior patterns. By testing the CUP99 data set, the results show that the IFCA is not only feasible but also accurate and efficient. The improved fuzzy clustering algorithm proposed in this paper can improve the detection rate of intrusion detection and reduce the false detection rate, and can be widely used in intrusion detection system.
연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
