基于攻击图的多源告警关联分析方法

doi:10.11959/j.issn.1000-436x.2015193

摘要/Abstract

摘要：

现有基于攻击图(attack graph)的告警关联分析方法难以全面处理告警关联关系，同时，漏报推断和告警预测带来大量冗余路径误报。针对以上问题提出了基于攻击图的多源告警关联分析算法，能够综合应用图关系和阈值限制进行联动推断和预测，达到更为全面解决攻击图中的告警漏报和减少误报数量的目的。同时，将告警处理算法并行化，提出了AG-PAP告警并行处理引擎。实验表明，该方法能够提升关联分析的有效性和性能表现。

关键词: 告警关联, 攻击图, 多源分析, 并行处理

Abstract:

Current attack graph-based alert correlation cannot deal with graph relation between alerts properly,and a large number of redundant attack paths may arise when trying to find out missing alerts and predict future attacks.A multi-source alert analyzing method was proposed,fully utilizing graph relation and threshold to correlate mapped alerts and eventually reduce false positive rate as well as true negative rate.To improve the speed of the algorithm,a parallel alert processing system (AG-PAP) was proposed.AG-PAP is tested on distributed environment which gets satisfied effec-tiveness and performance.

Key words: alert correlation, attack graph, multi-source analyzing, parallel processing

刘威歆,郑康锋,武斌,杨义先. 基于攻击图的多源告警关联分析方法[J]. 通信学报, 2015, 36(9): 135-144.

Wei-xin LIU,Kang-feng ZHENG,Bin WU,Yi-xian YANG. Alert processing based on attack graph and multi-source analyzing[J]. Journal on Communications, 2015, 36(9): 135-144.

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参考文献 21

[1]	VALEUR F , VIGNA G , KRUEGEL C , et al. A comprehensive ap-proach to intrusion detection alert correlation[J]. IEEE Transactions on Dependable and Secure Computing, 2004,1(3): 146-169.
[2]	ROSCHKE S , CHENG F , MEINEL C . An alert correlation platform for memory-supported techniques[J]. Concurrency and Computa-tion-practice &Experience, 2012,24(10): 1123-1136.
[3]	梅海彬，龚俭，张明华 . 基于警报序列聚类的多步攻击模式发现研究[J]. 通信学报， 2011,32(5): 63-69. MEI HB , GONG J , ZHANG MH . Research on discovering multi-step attack patterns based on clustering IDS alert sequences[J]. Journal on Communications, 2011,32(5): 63-69.
[4]	ROSCHKE S , CHENG F , MEINEL C . Using vulnerability informa-tion and attack graphs for intrusion detection[A]. Information Assur-ance and Security (IAS),2010 Sixth International Conference on IEEE[C]. 2010.68-73.
[5]	NOEL S , JAJODIA S . Advanced vulnerability analysis and intrusion detection through predictive attack graphs[A]. Critical Issues in C4I,Armed Forces Communications and Electronics Association (AFCEA) Solutions Series International Journal of Command and Control[C]. 2009.
[6]	JAJODIA S , NOEL S . Topological Vulnerability Analysis[M]. Springer, 2010.139-154.
[7]	OU X , BOYER W , MCQUEEN M . A scalable approach to attack graph generation[A]. ACM[C]. 2006.336-345.
[8]	GONZALEZ J E , LOW Y , GU H , et al. PowerGraph:distributed graph-parallel computation on natural graphs[A]. OSDI[C]. 2012,12(1): 2.
[9]	WANG L , LIU A , JAJODIA S . Using attack graphs for correlating,hypothesizing,and predicting intrusion alerts[J]. Computer Commu-nications, 2006,29(15): 2917-2933.
[10]	AHMADINEJAD S H , JALILI S , ABADI M . A hybrid model for correlating alerts of known and unknown attack scenarios and up-dating attack graphs[J]. Computer Networks, 2011,55(9): 2221-2240.
[11]	ROSCHKE S , CHENG F , MEINEL C . High-quality attack graph-based IDS correlation[J]. Logic Journal of the Igpl, 2013,21(4I): 571-591.
[12]	ROSCHKE S , CHENG F , MEINEL C . A new alert correlation algo-rithm based on attack graph[J]. Computational Intelligence in Security for Information Systems, 2011,6694:58-67.
[13]	DEAN J , GHEMAWAT S . MapReduce[J]. Communications of the ACM, 2008,51(1): 107.
[14]	LOW Y , BICKSON D , GONZALEZ J , et al. Distributed graphlab:a framework for machine learning and data mining in the cloud[J]. Pro-ceedings of the VLDB Endowment, 2012,5(8): 716-727.
[15]	MALEWICZ G , AUSTERN M H , BIK A J C , et al. Pregel:a system for large-scale graph processing[A]. Proceedings of the 2010 ACM SIGMOD International Conference on Management of data[C]. 2010.
[16]	LI K , GIBSON C , HO D , et al. Assessment of machine learning algo-rithms in cloud computing frameworks[Z]. IEEE, 2013.98-103.
[17]	GUO Y , BICZAK M , VARBANESCU A L , et al. Towards bench-marking graph-processing platforms[A]. The International Conference for High Performance Computing,Networking,Storage and Analy-sis[C]. 2013.
[18]	CHING A , KUNZ C . Giraph:large-scale graph processing infrastruc-ture on Hadoop[J]. Hadoop Summit, 2011,29(6).
[19]	LOW Y , GONZALEZ J , KYROLA A , et al. Graphlab:a new parallel framework for machine learning[A]. UAI[C]. 2010.340-349.
[20]	OU X , GOVINDAVAJHALA S , APPEL A W . MulVAL:a logic- based network security analyzer[A]. 14th USENIX Security[C]. 2005.1-16.
[21]	LOW Y . GraphLab:A Distributed Abstraction for Large Scale Ma-chine Learning[D]. University of California, 2013.

方法	攻击数	映射告警	输出告警	漏报数	误报数
Wang	8	3	14	0	6
Seyed(HT=3)	8	3	7	2	1
本文(ITH=3)	8	3	11	0	3

方法	告警数	总时间/ms	映射时间/ms	关联分析时间/ms
本文	50 480	1 187	21	1 166
Sebastian^[11]	43 485	52 617	108	52 509.5

方法	大规模图规模处理能力	实时图更新能力	分布式性能	关联预测
Wang[9]	弱	无	无	中
Seyed[10]	无	无	无	中
Roschke[11]	中	弱	中	弱
本文	强	强	强	强