计算机科学 ›› 2020, Vol. 47 ›› Issue (2): 287-293.doi: 10.11896/jsjkx.190100047
陈佳,欧阳金源,冯安琪,吴远,钱丽萍
CHEN Jia,OUYANG Jin-yuan,FENG An-qi,WU Yuan,QIAN Li-ping
摘要： 随着网络技术的快速发展,网络攻击带来了极大的负面影响,因此网络安全问题亟待解决。针对网络攻击中的拒绝服务(Denial of Service,DoS)攻击,提出了一种基于边缘计算框架的孤立森林网络异常检测方法。该方法根据每个边缘节点的特性实现对模型训练任务的合理分配,有效地提高了边缘节点的利用效率;同时,利用边缘计算的特点实现了对云中心模型训练任务的分流,从而更好地减少系统的耗时,减轻云中心的任务负担。为了验证所提方法的有效性,对10%-KDDCUP99网络数据集进行预处理,并提取部分数据用于实验。实验结果表明,与支持向量机(Support Vector Machine,SVM)和多层感知器(Multi-Layer Perceptron,MLP)方法相比,所提方法将系统建立时间分别缩短了90%和60%,且得出的曲线下面积(Area Under Curve,AUC)可达0.9以上,这证明该方法能够在确保较高异常检测性能条的件下有效减少异常检测系统的建立时间。
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| [1]PEDRO G T,JESUS D V,M GABRIEL M F,et al.Anomaly-based network intrusion detection:Techniques,systems and challenges [J].Computers & Security,2009,28(1／2):18-28. [2]TAN A P,CHEN H,WU B Q.Network Intrusion Intelligent Detection Algorithm Based on AdaBoost[J].Computer Scien-ce,2014,41(2):197-200. [3]CHEN J Y,XU X Y,SU M M.Research on Network Attack Detection Based on Self-adaptive Immune Computing [J].Computer Science,2018,45(S1):364-370. [4]WANG C,VISWANATHAN K,LAKSHMINARAYAN C, et al.Statistical techniques for online anomaly detection in data centers[C]∥Proceedings of the 12 IFIP/IEEE International Symposium on Integrated Network Management.IEEE,2011:385-392. [5]DING Z G,DU D J,FEI M R.An isolation principle based distributed anomaly detection method in wireless sensor networks [J].International Journal of Automation and Computing,2015,12(4):402-412. [6]KILLOURHY K S,MAXION R A.Comparing anomaly-detec-tion algorithms for keystroke dynamics[C]∥2009 IEEE/IFIP International Conference on Dependable Systems & Networks (DSN).IEEE Computer Society,2009:125-134. [7]YU X S,HAN D Z,DU Z X.DDoS Attack Detection System Based on Intelligent Bee Colony Algorithm[J].Computer Scien-ce,2018,45(12):123-129. [8]FEDERICO S W,JUAN I A P,PABLO C D L H,et al.Anomaly Detection in Network Traffic Based on Statistical Inference and alpha－Stable Modeling [J].IEEE Transactions on Dependable &Secure Computing,2011,8(4):494-509. [9]MENG W Z,LI W J,KWOK L F.Design of intelligent KNN-based alarm filter using knowledge-based alert verification in intrusion detection [M].Security and Communication Networks,2015,8(18):3883-3895. [10]ZHANG J,ZULKERNINE M,HAQUE A.Random-Forests-Based Network Intrusion Detection Systems [J].IEEE Transactions on Systems,Man,and Cybernetics,Part C:Applications and Reviews,2008,38(5):649-659. [11]XU D,WANG Y J,MENG Y L,et al.Improved Data Anomaly Detection Method Based on Isolation Forest[J].Computer Scien-ce,2018,45(10):155-159. [12]ZHANG Q,HU Y P,JI C,et al.Edge Computing Application:Real-Time Anomaly Detection Algorithm for Sensing Data [J].Journal of Computer Research and Development,2018,55(3):524-536. [13]LI H,WANG L.Online orchestration of cooperative defense against DDoS attacks for 5G MEC[C]∥Wireless Communications and Networking Conference.IEEE,2018:1-6. [14]LIU F T,TING K M,ZHOU Z H.Isolation Forest[C]∥Proceeding of the 2008 Eighth Eighth IEEE International Conference on Data Mining.IEEE Computer Society,2008:413-422. [15]HE Y,ZHU X,WANG G,et al.Predicting Bugs in Software Code Changes Using Isolation Forest[C]∥IEEE International Conference on Software Quality.IEEE,2017:296-305. [16]CALHEIROS R,RAMAMOHANARAO K,BUYYA R,et al.On the effectiveness of isolation-based anomaly detection in cloud data centers [J].Concurrency and Computation:Practice and Experience,2017:e4169. [17]DING Z,DU D,FEI M.An isolation principle based distributed anomaly detection method in wireless sensor networks [J].International Journal of Automation and Computing,2015,12(4):402-412. [18]HE H B,GARCIA E A.Learning from Imbalanced Data [J].IEEE Transactions on Knowledge & Data Engineering,2009,21(9):1263-1284. [19]SHI W S,SUN H,CAO J,et al.Edge Computing—An Emerging Computing Model for the Internet of Everything Era [J].Journal of Computer Research & Development,2017,54(5):907-924. [20]BRUNO R P.Data Structures and Algorithms with ObjectOriented Design Patterns in Java[M].Wiley,1999. [21]INGBER L.Simulated annealing:Practice versus theory[J]. Mathematical & Computer Modeling:An International Journal,1993,18(11):29-57. [22]WU J S,ZHANG W P,MA Y.Data analysis and study on KDDCUP99 data set [J].Computer Applications and Software,2014(11):321-325. [23]ADETUNMBI A,ADEOLA S,DARAMOLA O.Analysis of KDD’99 Intrusion Detection Dataset for Selection of Relevance Features [J].Lecture Notes in Engineering & Computer Scien-ce,2010,2186(1):1371-1379. [24]TREBAR M,STEELE N.Application of distributed SVM architectures in classifying forest data cover types [J].Computers and Electronics in Agriculture,2008,63(2):119-130. |
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