A Network Intrusion Detection Method Based on Improved K-means Algorithm

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1 Advanced Scence and Technology Letters, pp A Network Intruson Detecton Method Based on Improved K-means Algorthm Meng Gao 1,1, Nhong Wang 1, 1 Informaton and Computer Engneerng College, Northeast Forestry Unversty, Harbn, Chna gaomeng0916@126.com,wnh@mal.nefu.edu.cn Abstract. K-means algorthm could be used n ntruson detecton, and selecton of ntal cluster centers was one of the most mportant factor that nfluenced the clusterng performance, tradtonal method had a certan degree of randomness n dealng wth ths problem, therefore, nformaton entropy was ntroduced nto the process of cluster centers selecton, and a fuson algorthm combnng wth nformaton entropy and K-means algorthm was proposed, nformaton entropy value was used to measure the smlarty degree among records, t could help to choose a least smlar record to be a cluster center. Comparson results show that the detecton rato and false alarm rato of the proposed method s better than tradtonal K-means algorthm. Keywords: K-means; Informaton entropy; Intruson detecton 1 Introducton Network ntruson detecton s a process whch ncludes seres of actons, such as collectng data related to network status and behavors from key nodes, analyzng these data, dscoverng abnormal behavor as well as provdng early warnng [1-2], t can acheve the purpose of montorng network behavor and defendng network ntruson. As ntruson behavors tend to have uncertanly n some degree, so, t s of great sgnfcance to dentfy unknown behavors by extractng hdden nformaton n ntruson data [3-4]. L Wenhua proposed a FCM cluster network ntruson detecton model based on fuzzy c-means [5]; Zhang Guosuo proposed an mproved FCM cluster algorthm, t could solve the boundedness n dealng wth bg dataset by usng tradtonal FCM [6]; Reda M. Elbasony used random forests and weghed k-means algorthm to buld ntruson patterns and choose anomalous clusters [7]; Luo Mn researched on the non-supervsed ntruson detecton model based on K-means algorthm [8]; L Helng proposed the mproved K-means algorthm and carred out experments amng at the problem of uneven data dstrbuton [9]; Researches above focused on solvng the problem of data sze that the algorthm can deal wth, they 1 Meng Gao, female (1989- ), Ph.D., manly engaged n forestry nformatzaton and system securty, E-mal: gaomeng0916@126.com. ISSN: ASTL Copyrght 2014 SERSC

2 Advanced Scence and Technology Letters gnored the kernel of algorthm tself. Ths paper uses K-means algorthm to detect ntruson behavors, as selecton of ntal cluster centers s the key factor that nfluences the cluster results, the nformaton entropy technology s ntroduced to auxlary determne cluster centers, experments show that the mproved fuson algorthm has a good detecton rato and false alarm rato. 2 Algorthm Combng wth Informaton Entropy and K-means 2.1 K-means Algorthm Ths paper uses Eucldean dstance to measure the smlarty among records, and use formula (1) to evaluate the clusterng results. E k x d 2 x. x d 1 d x (1) where E s the sum of all objects mean squared error; d s a data cluster ; x s a data object; d s the number of objects n d ; k s the number of clusters; the smaller value of E, the better of the clusterng effect. Data clusterng process usng K-means algorthm can be descrbed as follows: (1) Defne the number k of clusters to be fnally generated; (2) Choose k records to be the ntal cluster centers; (3) Dvde the orgnal data nto the k clusters, and recalculate the center of each cluster; (4) Break the clusterng result n last stage, put object j nto the correspondng cluster accordng to the prncple of mnmum Eucldean dstance, then, form the new clusters, and calculate the value of E at the same tme; (5) Repeat stage (4) untl the new clusters are same as the prevous clusters. We can know that performance of the algorthm s manly determned by stage (1) and (2), cluster number k s often determned accordng to actual stuatons [10-11], therefore, selecton of ntal cluster centers s the key factor that nfluences the algorthm performance. 2.2 Informaton Entropy Informaton entropy s used to measure the uncertanty of a random varable nformaton, the bgger of t, the more dsordered of the data; otherwse, the more ordered and smlar of the data [11-12]. If usng nformaton entropy to evaluate clusterng effect, then the smaller of the entropy, the more smlar of data n a same cluster and better of the clusterng effect [13-14]. 430 Copyrght 2014 SERSC

3 Advanced Scence and Technology Letters Informaton entropy of a random varable X can be descrbed as:. (2) E ( X ) lo g ( p ( x )) x s ( x ) n Where S ( X ) s the possble value set of X ; p( X ) s the probablty functon of X. 2.3 Improved K-means algorthm based on nformaton entropy Assume that sample space M ncludes n records, frst, calculate the nformaton entropy value of each record, and then start from the frst record, compare the value of current record wth other records, fnally, regard the mnmum value as the nformaton entropy baselne of the current record, the comparson matrx s shown as Table 1. Table 1. Comparson matrx of nformaton entropy value j n Baselne 1 E(M 1,M 1) E(M 1,M 2) E(M 1,M 3) E(M 1,M n) mn E(M 1,M j) 2 E(M 2,M 1) E(M 2,M 2) E(M 2,M 3) E(M 2,M n) mne(m 2,M j) 3 E(M 3,M 1) E(M 3,M 2) E(M 3,M 3) E(M 3,M n) mne(m 3,M j) N E(M n,m 1) E(M n,m 2) E(M n,m 3) E(M n,m n) mne(m n,m j) Calculate the nformaton entropy baselne set ( ) { m n (, ), m n (, ),..., m n (, )},1, and order the B ase M E M M E M M E M M j n 1 2 j j n j baselne from bg to small, get the ordered baselne set SortB ase( M ), the bgger of the nformaton entropy value, the less smlar between the correspondng record and other records, and the more sutable to be center of the ntal cluster. Combne wth the cluster number k determned n stage (1) of K-means algorthm, choose the topk records correspondng wth the nformaton entropy values n SortB ase( M ) as the least smlar records, and these records can be regarded as the ntal cluster centers. 2.4 Network Intruson Detecton Algorthm Based on IE-K-means The process of detectng network ntruson usng IE-K-means algorthm can be descrbed as: (1) Defne the number k of clusters to be fnally generated, and set the nstance threshold n s tan cel n e of clusters; (2) Choose k records as the ntal cluster center C ( k ) usng IE-K-means algorthm, calculate the Eucldean dstance d (, j) between C and other records; 431 Copyrght 2014 SERSC

4 Advanced Scence and Technology Letters (4) Accordng to the mnmum d (, j), dvde each record nto clusters wth the mnmum Eucldean dstance, and generate new clusters, recalculate C of the new clusters, and record the nstance number In s tan ce of each cluster. (6) Break the clusterng result n last stage, and repeat stage (3)-(5) untl the current clusters are the same as the prevous clusters. (7) Record C and In s ta n c e of the each generated cluster; (8) If In s tan ce n s tan cel n e, mark C as the center of abnormal cluster C ; a b n o rm a l f In s tan ce n s tan cel n e, mark C as the center of normal cluster n o r m a l C ; (9) When new connecton s comng, calculate the Eucldean dstance d (C, C ) n e w between new connecton and each C, f d (C, C ) s closer wth C n ew a b n o rm a l, mark the new connecton as the abnormal ntruson; If d (C, C ) s closer wth C n e w n o r m a l, mark the new connecton as the normal ntruson. 3 Smulaton Experment and Analyss Use KDDCUP99 data packets to verfy the feasblty and effectveness of IE-Kmeans algorthm, choose 7200 DoS attack data, of whch 5500 records are used as tranng data for tranng model, and the other 1700 records are used as testng data for testng the effectveness of the ntruson detecton model. The experment adopts dfferent cluster number k, cluster the tranng data at frst to get the cluster center set, and then send the testng data nto the anomaly detecton system for ntruson detecton, calculate the D etectr a te and F a lsed etectr a te of each data set at the same tme, experment results are shown n Table 2. It can be seen that the network ntruson detecton model based on IE-K-means s feasble, and the mproved algorthm s better than tradtonal K-means algorthm n detecton rato and false alarm rato based on dfferent cluster amount. Table 2. Comparson experment results k K-means algorthm IE-K-means algorthm DetectRate/% FalseDetectRate/% DetectRate/% FalseDetectRate/% Conclusons Accordng to the characterstcs of network ntruson data, amng at the problems exsted n the current ntruson detecton researches, ths paper proposes up a network ntruson detecton method based on the fuson algorthm combnng wth nformaton entropy and K-means, experment results show that the fuson algorthm has mproved the detecton rato and reduced the false alarm rato compared wth 432 Copyrght 2014 SERSC

5 Advanced Scence and Technology Letters tradtonal K-means algorthm. However, the mplementaton of the fuson algorthm dd not consder the algorthm executon effcency, whch requres the further study. Acknowledgments. Ths work s supported by Specal Fund for Scentfc Research n the Publc Interest ( ) and The Fundamental Research Funds for the Central Unverstes ( AB22). References 1. Jonathan, J.D., Andrew, J.C.: Data Processng for anomaly based network ntruson detecton: A revew. Computers & Securty. 30, (2013) 2. Lao, S.H., Chu, P.H., Hsao, P.Y.: Data mnng technques and applcatons A decade revew from 2000 to Expert Systems wth Applcatons. 39, (2012) 3. Mohammand, S.A., Hamd, M., Jafar, H.: Desgn and analyss of genetc fuzzy systems for ntruson detecton n computer networks. Expert Systems wth Applcatons. 38, (2011) 4. Chen, X.H.: Intruson Detecton Method Baed on Data Mnng Algorthm. Computer Engneerng. 36, (2010) 5. L, W.H.: Network Intruson Detecton Model Based on Clusterng Analyss. Computer Engneerng. 37, (2011) 6. Zhang, G.S., Zhou, C.M., Le, Y.J.: Improved fuzzy C-means clusterng algorthm and ts applcaton to ntruson detecton. Journal of Computer Applcatons. 29, (2009) 7. Reda, M.E., Elsayed, A.S., Tarek, E.E., Mahmoud, M.F.: A hybrd network ntruson detecton framework based on random forests and weghed k-means. An Shames Engneerng Journal. 4, (2013) 8. Luo, M., Wang, L.N., Zhang, H.G.: An Unsupervsed Clusterng-Based Intruson Detecton Method. ACTA ELECTRONICA SINICA. 31, (2003) 9. L, H.L.: Study on Applcaton of data mnng n network ntruson detecton. JLn Unversty, JLn (2013) 10. L, Y.: Applcaton of K-means Clusterng Algorthm n Intruson Detecton. Computer Engneerng. 33, (2007) 11. Du, Q., Sun, M.: Intruson detecton system based on mproved clusterng algorthm. Computer Engneerng and Applcatns. 47, (2011) 12. Ye, Z.W.: The Research of Intruson Detecton Algorthms Based on the Clusterng of Informaton Entropy. Proceda Envronmental Scences. 12, (2012) 13. Feng, J., Su, Y.F., Cao, C.G.: An Informaton entropy-based approach to outler detecton n rough sets. Expert Systems wth Applcatons. 37, (2010) 14. Jn, C.X., L, F.C., L, Y.: A generalzed fuzzy ID3 algorthm usng generalzed nformaton entropy. Knowledge-Based Systems. 64, (2014) 433 Copyrght 2014 SERSC

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