Network Anomaly Detection Method for Intentional Attack

doi:10.12068/j.issn.1005-3026.2020.10.002

Journal of Northeastern University Natural Science ›› 2020, Vol. 41 ›› Issue (10): 1376-1381.DOI: 10.12068/j.issn.1005-3026.2020.10.002

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Network Anomaly Detection Method for Intentional Attack

ZHAO Hai¹， ZHENG Chun-yang^1,2， WANG Jin-fa²， SI Shuai-zong²

1. School of Computer Science & Engineering，Northeastern University，Shenyang 110169，China; 2. Beijing Key Laboratory of IoT Information Security Technology/Institute of Information Engineering， CAS， Beijing 100093， China.

Received:2019-07-18 Revised:2019-07-18 Online:2020-10-15 Published:2020-10-20
Contact: ZHENG Chun-yang
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Abstract

Abstract: The anomaly triggered by intentional attack in complex networks is common but most existing detection methods ignore the global topology mutation feature. To solve this problem， based on the abnormal evolution characteristics of the global network topology， a network path change coefficient (r) was proposed to quantify the change of the transmission path between nodes. The Fibonacci evolution region was derived from the Fibonacci sequence to distinguish normal and abnormal evolution. r was used as the core measurement parameter to construct the Fibonacci evolution region， form a network anomaly detection method， and realize the determination of anomalies. The results showed that the average accuracy of the detection method is more than 90%， which is higher than the accuracies of MCS (maximum common subgraph) and GED (graph edit distance)， which proves the effectiveness of the proposed detection method.

Key words: network anomaly detection, Fibonacci evolution region, path change coefficient (r), intentional attack, network science

CLC Number:

TP393

ZHAO Hai， ZHENG Chun-yang， WANG Jin-fa， SI Shuai-zong. Network Anomaly Detection Method for Intentional Attack[J]. Journal of Northeastern University Natural Science, 2020, 41(10): 1376-1381.

References

[1]Ishibashi K，Kondoh T，Harada S，et al.Detecting anomalous traffic using communication graphs［C］//Telecommunications:The Infrastructure for the 21st Century.Vienna，2010:1-7.
[2]Koutra D，Papalexakis E E，Faloutsos C.TensorSplat:spotting latent anomalies in time［C］//Proceedings of the 2012 16th Panhellenic Conference on Informatics.Washington:IEEE Computer Society，2012:144-149.
[3]Papalexakis E E，Faloutsos C，Sidiropoulos N D.ParCube:sparse parallelizable tensor decompositions［C］//European Conference on Machine Learning & Knowledge Discovery in Databases.Berlin:Springer-Verlag，2012:521-536.
[4]Heard N A，Weston D J，Platanioti K，et al.Bayesian anomaly detection methods for social networks［J］.The Annals of Applied Statistics，2010，4(2):645-662.
[5]Peel L，Clauset A.Detecting change points in the large-scale structure of evolving networks［C］//AAAI'15:Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence.Palo Alto:AAAI，2015:2914-2920.
[6]Akoglu L，Tong H，Koutra D.Graph based anomaly detection and description:a survey［J］.Data Mining & Knowledge Discovery，2015，29(3):626-688.
[7]Minot M，Ndiaye S N，Solnon C.A comparison of decomposition methods for the maximum common subgraph problem［C］//2015 IEEE 27th International Conference on Tools with Artificial Intelligence (ICTAI).Salerno:IEEE，2015:461-468.
[8]Riesen K.Structural pattern recognition with graph edit distance［M］.Berlin:Springer-Verlag，2016:29-44.
[9]Koutra D，Vogelstein J T，Faloutsos C.Deltacon:a principled massive-graph similarity function［J］.ACM Transactions on Knowledge Discovery from Data，2013，10(3):1-43.
[10]Albert R，Jeong H，Barabasi A L.Error and attack tolerance of complex networks［J］.Nature，2000，406(6794):378-382.
[11]Tan F，Xia Y，Zhang W，et al.Cascading failures of loads in interconnected networks under intentional attack［J］.EPL (Europhysics Letters)，2013，102(2):28009.
[12]Wang J，Zhao H，Xu J，et al.Using intuitionistic fuzzy set for anomaly detection of network traffic from flow interaction［J］.IEEE Access，2018，6:64801-64816.
[13]Xiao S，Xiao G，Cheng T H.Tolerance of intentional attacks in complex communication networks［J］.IEEE Communications Magazine，2008，46(1):146-152.
[14]Wang J F，Liu X，Zhao H，et al.Anomaly detection of complex networks based on intuitionistic fuzzy set ensemble［J］.Chinese Physics Letters，2018，35(5):156-160.
[15]Leskovec J，Kleinberg J，Faloutsos C.Graph evolution:densification and shrinking diameters［J］.ACM Transactions on Knowledge Discovery from Data，2007，1(1):2-43.
[15]关守平，房少纯.一种新型的区间-粒子群优化算法［J］.东北大学学报(自然科学版)，2012，33(10):1381-1384.(Guan Shou-ping，Fang Shao-chun.A new interval particle swarm optimization algorithm［J］.Journal of Northeastern University(Natural Science)，2012，33(10):1381-1384.)

Network Anomaly Detection Method for Intentional Attack

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