Applying Data Mining Techniques to Analyze Alert Data

Shin, Moon-Sun; Moon, Hosung; Ryu, Keun-Ho; Kim, KiYoung; Kim, JinOh

doi:10.1007/3-540-36901-5_21

Cited by 5 publications

(3 citation statements)

References 6 publications

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“…Out them very important procedure for scenario detection is in [19]. Base on an algorithm by name CURE, offline clustering solution is proposed in [20]. The solution makes use of numeric attributes only.…”

Section: Related Workmentioning

confidence: 99%

Generation of Meta Alerts by Aggregating Intrusion Alerts

.N¹

2013

IOSR-JCE

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Section: Related Workmentioning

confidence: 99%

Generation of Meta Alerts by Aggregating Intrusion Alerts

.N¹

2013

IOSR-JCE

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“…The other two approaches rely on supervised learning techniques. In [18], an offline clustering solution on the CURE algorithm is presented. The solution is limited to numeric attributes.…”

Section: Related Workmentioning

confidence: 99%

Cluster based Statistical Anomaly Intrusion Detection for Varied Attack Intensities

Thangavel¹,

Thangaraj²

2011

IJCA

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In today's Internet paradigm, the type of intrusion attacks becomes crucial in presenting effective improvement to anomaly intrusion attacks. Anomaly Traffic hacker attacks combined with traditional network intruders was a serious threat to network security. The existing work on intrusion detection and prevention of traffic attacks take much time, before which the intruder is spread across the network. The sensing mechanism in addition to rejection of an attack against intruders and keeps no record of the cause of the attack and its effects. In same time the actual happening of the attack detection method was left over unnoticing. This motivates to develop an effective attack mechanism of the cluster based anomaly intrusion detection.

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“…Later, they constructed the first misuse detection system based on data mining�JAM system, in 2000 [2], [3]. Shin et al [4] utilized it in strategy-based network security management, offered an alarm analyzer using data mining mechanism which consists of association rule digger, frequent item digger and clustering digger, and finally realized the effective support of alarm analysis and high level analysis mmmg systems to security strategy management. Cabrera et al [5] used it to detect the DDOS attacks in MIB database and gained a good detecting result.…”

Section: Related Workmentioning

confidence: 99%

Research on the network security management based on data mining

Xiao

2010

2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE)

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Against the defect that the traditional computer network security management system processes a mass of data with low efficiency and accuracy, a novel network security management model based on data mining is proposed in this paper. It makes use of multi-source data collection strategy to acquisition and integrate the relevant data of different security products, and takes advantage of data mining technology for massive data to analyze them comprehensively and intelligently and response automatically. The experimental result reveals that the model performs well. It can identity the real attacks from a large number of security events with a good performance, so that the alarm information will be refined with low false or wrong alarms, the accuracy, intelligence and adaptability of network security management will be enhanced and the requirements of new security situation will be met.

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Applying Data Mining Techniques to Analyze Alert Data

Cited by 5 publications

References 6 publications

Generation of Meta Alerts by Aggregating Intrusion Alerts

Generation of Meta Alerts by Aggregating Intrusion Alerts

Cluster based Statistical Anomaly Intrusion Detection for Varied Attack Intensities

Research on the network security management based on data mining

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