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1 C&C 1,2, 1 1,2 2,3,a) 1, , Command and Control C&C 1 C&C C&C C&C C&C C&C C&C C&C C&C Evaluation of Machine Learning Techniques for C&C Traffic Classification Kazumasa Yamauchi 1,2, 1 Junpei Kawamoto 1,2 Yoshiaki Hori 2,3,a) Kouichi Sakurai 1,2 Received: December 8, 2014, Accepted: June 5, 2015 Abstract: With the spread of Internet, the number of damage from botnet is increasing. General botnet use Command and Control (C&C) server and detecting C&C server is one of the technique of botnet measures. However, it is hard to detect C&C server because of diversification of C&C protocol and changing of botnet configuration. In our work, we define a feature vector to detect C&C server and report the experiment result that is classification normal traffic and C&C session by using real network traffic. Finally we show the effectiveness as the method of detecting C&C server which use several kinds of protocols. Keywords: botnet, C&C server, anomaly detection, machine learning 1. Command & Control C&C 1 Kyushu University, Fukuoka , Japan 2 Institute of Institute of Systems, Information Technologies and Nanotechnologies (ISIT), Fukuoka , Japan 3 Saga University, Saga , Japan 1 Presently with NIPPON TELEGRAPH AND TELE- PHONE WEST CORPORATION a) [1] C&C C&C C&C C&C DDoS C&C C&C 1 C&C 1.1 C&C IRC HTTP P2P c 2015 Information Processing Society of Japan 1745

2 IRC IRC HTTP P2P HTTP P2P IRC 1993 [1] IRC C&C [2], [3] [2] IRC C&C 16 3 [3] IRC IRC / n-gram 2003 C&C P2P [1] P2P P2P PeerShark [4] [4] IRC P2P HTTP HTTP 2005 [1] HTTP IRC HTTP HTTP HTTP HTTP [5], [6] HTTP GET POST HTTP [5] [6] Artificial Immune System AIS [10] AIS HTTP 1.2 [3], [4], [5] 1 [5], [6] [3] IRC 2 C&C IRC HTTP C&C C&C C&C C&C C&C C&C C&C 2 3 C&C C&C 1 1 CCCDATASet C&C c 2015 Information Processing Society of Japan 1746

3 1 Table 1 Feature vector. V 1 V 2 V 3 V 4 V 5 V 6 V 7 PKT Byte PKT Byte s 1 Fig. 1 Time-chart of Botnet activity. 3 PC C&C 3 19 C&C C&C C&C C&C 3. C&C C&C CCCDataSet 09C09 CCCDataSetC10 PRACTICE 13P13 [7] IRC HTTP C&C 3.1 C&C 2 1 C&C IRC IRC C&C HTTP P2P HTTP IRC HTTP HTTP IRC 3.2 C&C [8] 36 [8] C&C IRC C&C C&C [5] HTTP C&C HTTP DNS P2P [2] IRC TCP 1 / TCP 1 V 6 V 7 V 1 V 2 C&C IRC HTTP 1 V 3 V 4 C&C c 2015 Information Processing Society of Japan 1747

4 情報処理学会論文誌 Vol.56 No (Sep. 2015) 図 2 C&C セッション分析 全結果 Fig. 2 C&C session analysis (All). 図 3 V1 V3 (IRC) Fig. 3 V1 V3 (IRC). 図 4 V1 V3 (HT T P ) Fig. 4 V1 V3 (HT T P ). 図 5 V6 V7 (HT T P ) Fig. 5 V6 V7 (HT T P ). データサイズの総数に関しては パケットのヘッダ情報を つとして考えることができ C&C セッションを検出する 基にセッションごとに含まれているパケットのデータサイ ことでボットネットによる攻撃を未然に防ぐことを可能に ズを合計したものである V5 はパケットのヘッダ情報に含 する 図 2 は IRC と HTTP の通信に関してそれぞれ特徴 まれるタイムスタンプを確認し セッション終了時刻から ベクトルを用いて解析を行った結果を示しており 通常の セッション開始時刻の差をとった時間である また V6 は HTTP または IRC セッションは青で C&C セッションは セッション中にクライアントがサーバへアクセスする回数 赤で示している また IRC に関しては セッション中に の合計を指し V7 はアクセス時間のばらつきを表している C&C サーバへの再接続を行わないので V6 V7 に関しては 考慮しない 図 2 から IRC の方がデータの分布範囲が狭 3.3 C&C セッション分析 本節では通常のセッションと C&C セッションが提案す る特徴ベクトルで分類可能であるか分析を行う 2 章より C&C サーバの通信はボットネットが攻撃を行う予兆の 1 c 2015 Information Processing Society of Japan いことが分かる これに対し HTTP ではデータの分布範 囲が広く IRC よりも通信の多様性が見られる 図 2 の結果において 特に 2 種類のデータを区別できた 結果に関して抜粋したものを図 3 図 4 図 5 に示す 図

5 2 IP Table 2 Number of unique IP address. Normal C&C C09 C10 P13 IRC HTTP , Table 3 Number of extracted session data. 6 Fig. 6 Experiment flow. IRC C&C HTTP C&C 5 10,000 HTTP 1 1 C&C HTTP C&C 4. C&C Linux tcpdump Normal C&C C09 C10 P13 IRC HTTP 1, , IRC 80 HTTP C&C C09 C10 P13 C&C IRC JOIN HTTP GET C&C JOIN GET 2 3 IP P13 IRC C&C TCP TCP c 2015 Information Processing Society of Japan 1749

6 20 TCP TCP 4 5 IRC HTTP 4 C&C IRC V 1 V 5 C10 V 1 V 5 C09 IRC V 6 =1 V 7 =0 5 C&C HTTP V 6 P13 V 1 V 7 C09 C10 V 4 C&C HTTP IRC i j ˆx i,j =(x i,j min(x n,j ))/ max(x m,j ) j n x n,j m x m,j i j 4 IRC Table 4 IRC session data analysis: Average (variance). Normal (IRC) C&C C09 C10 V 1 88 ( ) 6 (24) 5 (250) V 2 1,187 ( ) 67 ( ) 77 ( ) V 3 75 ( ) 2 (6.9) 3 (632) V 4 1,336 ( ) 177 ( ) 185 ( ) V ( ) 8 (75) 6 (111) V 6 1(0) 1(0) 1(0) V 7 0(0) 0(0) 0(0) x i,j ˆx i,j SVM [2] IRC C&C SVM IRC HTTP 3.3 C&C HTTP IRC R [13] SVM kernlab [14] glmnet [15] e1071 [16] SVM k( x, y) =exp x y 2 2σ 2 σ SVM 3 5 HTTP Table 5 HTTP session data analysis: Average (variance). Normal (HTTP) C&C C09 C10 P13 V 1 88 ( ) 60 ( ) 47 ( ) 4 (5.7) V 2 33,140 ( ) 194 ( ) 177 ( ) 126 ( ) V ( ) 50 (900) 35.4 ( ) 3.4 (74) V 4 33,671 ( ) 66,320 ( ) 42,212 ( ) 1,135 ( ) V ( ) 2.6 (2.8) 0.27 ( ) 1.7 (3.7) V ( ) 3.8 (0.13) 35.7 ( ) 1.1 (0.3) V ( ) 0.64 (2.3) 3.1 (6.67) 1.5 (0.5) c 2015 Information Processing Society of Japan 1750

7 2/3 1/ HTTP IRC HTTP [2] SVM 22.3% 7 LR 8.2% 7 NB 3.9% SVM 2.7% 14.9% 23.2% V 6 V 7 C&C C&C Web Ajax 1 Web IRC IRC V 6 =1 V 7 =0 V 1 V 5 SVM 17% / 6. 5 HTTP IRC SVM 6 7 LR 6 7 NB P13 C&C P13 HTTP P13 6 Table 6 Result of classifying every DataSet. Normal C&C IRC HTTP C09 C10 P13 IRC HTTP IRC HTTP HTTP SVM (Normal) SVM (Anomaly) LR (Normal) LR (Anomaly) NB (Normal) NB (Anomaly) (SVM) [%] , (SVM) [%] (SVM) [%] (LR) [%] (LR) [%] (LR) [%] (NB) [%] (NB) [%] (NB) [%] Table 7 7 Comparison of execution time of machine learning algorithms. 7 Fig. 7 Comparison between proposed vector and existing vector. SVM LR NB (s) (s) (s) c 2015 Information Processing Society of Japan 1751

8 SVM C&C IRC 17.0% C10 HTTP 13.9%IRC HTTP 25.8% 27.0% C09 HTTP 25% C10 HTTP 16.8% IRC 25.4% A SVM SVM C&C C&C C&C SVM 90% 4% 7. C&C C&C C&C SVM SVM DNS P2P [1] Vania, J., Meniya, A. and Jethva, H.B.: A Review on Botnet and Detection Technique, International Journal of Computer Trends and Technology, Vol.4, No.1, pp (2013). [2] Kondo, S. and Sato, N.: Botnet Traffic Detection Techniques by C&C Session Classification Using SVM, Proc. 2nd International Workshop on Security (IWSEC 2007 ), pp (2007). [3] Goebel, J. and Holz, T.: Rishi: Identify bot contaminated hosts by IRC nickname evaluation, Proc. 1st USENIX HotBots (2007). [4] Narang, P., Ray, S., Hota, C. and Venkatakrishnan, V.: PeerShark-Detecting Peer-to-Peer Botnets by Tracking Conversations, Proc. IEEE Security & Privacy Workshops (SPW 2014 ), pp (2014). [5] Ashley, D.: An Algorithm for HTTP Bot Detection, Research paper, University of Texas - Information Security Office (2011). [6] Tyagi, A.K. and Nayeem, S.: Detecting HTTP Botnet using Artificial Immune System, International Journal of Applied Information Systems, Vol.2, No.6, pp (2012). [7] 2014 MWS2014 about.html [8] AdaBoost Vol.53, No.9, pp (2012). [9] Gu, G., Perdisci, R., Zhang, J. and Lee, W.: BotSniffer: Detecting botnet command and control channels in network traffic, Proc. 15th Annual Network and Distributed System Security Symposium (NDSS 2008 ) (2008). [10] Castro, L.N. and Timmis, J.: Artificial Immune Systems, A New Computational Intelligence Approach, Springer (2002). [11] Schehlmann, L. and Baier, H.: COFFEE: A Concept based on OpenFlow to Filter and Erase Events of Botnet activity at high-speed nodes, Proc. INFORMATIK 2013, pp (2013). [12] Gu, G., Perdisci, R., Zhang, J. and Lee, W.: BotMiner: Clustering Analysis of Network Traffic for Protocoland Structure-Independent Botnet Detection, Proc. 17th USENIX Security Symposium (2008). c 2015 Information Processing Society of Japan 1752

9 [13] R project, available from (accessed ). [14] Package kernlab, available from org/web/packages/kernlab/kernlab.pdf (accessed ). [15] Package glmnet, available from org/web/packages/glmnet/glmnet.pdf (accessed ). [16] Package e1071, available from org/web/packages/e1071/e1071.pdf (accessed ) IPA ACM IEEE IEEE ACM ACM IEEE c 2015 Information Processing Society of Japan 1753

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