Vol. 33 No. 3 Aug benign.com IP 1 benign.net IP 2 unknown.com IP 3 malicious.com 1 DNS : (malicious), (benign), (unknown) (Probabilistic Thre

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1 16 DNS DNS (Domain Name System) IP 2 (DNS ) (Probabilistic Threat Propagation) DNS DNS 69% 1 ( ) DNS 9% 40% DNS 2,170 DNS This paper proposes a method to estimate malicious domain names from a large scale DNS query response dataset. The key idea of the work is to leverage the use of DNS graph that is a bipartite graph consisting of domain names and corresponding IP addresses. We apply a concept of Probabilistic Threat Propagation (PTP) on the graph with a set of predefined benign and malicious node to a DNS graph obtained from DNS queries at a backbone link. The performance of our proposed method (EPTP) outperformed that of an original PTP method (9% improved) and that of a traditional method using N-gram (40% improved) in an ROC analysis. We finally estimated 2,170 of new malicious domain names with EPTP. 1 Domain Name System (DNS) ( ) IP DNS IP DNS DNS Detecting Malicious Domains with Probabilistic Threat Propagation on DNS Graph. Yuta Kazato, Toshiharu Sugawara,, Waseda University. Kensuke Fukuda, /, National Institute of Informatics / Sokendai., Vol.33, No.3(2016), pp [ ] IP IP C&C IP DNS ( )

2 Vol. 33 No. 3 Aug benign.com IP 1 benign.net IP 2 unknown.com IP 3 malicious.com 1 DNS : (malicious), (benign), (unknown) (Probabilistic Threat Propagation; PTP) DNS IP 2 (DNS ) ( 1) ( IP ) 3 DNS PTP EPTP (Extended PTP) 9% (N-gram ) 40% 2 (1) PTP EPTP (2) DNS DNS DNS DNS 1 DNS Top Level Domain (TLD) (.jp) Second Level Domain (SLD) (, co.jp) IP ( ) gtld (.com,.net) cctld (.cn,.jp) 60 85% [8] [10] [24] 50% TLD [12] gtld.com.net [20] cctld.cn [25] 90% gtld DNS 1 [13] TLD DDoS( ) Exposure [7] Kopis [3] Notos [2] Exposure [7] time to live (TTL) 15 3,000

3 18 Kopis [3] DDoS Notos [2] [14] DNS SVM [26] Domain Generation Algorithm (DGA) (N-gram) K-L DGA [15] Jaccard index C&C NX ( ) [27] [4] NX DGA [23] DGA DNS [28] DNS [22] Fast-flux DNS IP Flux (CDN ) [16] [6] NX DNS failure graph [16] 3G [19] [11] [9] Manadhata [19] HTTP DNS Polonium [11] Machine File 2 Probability Threat Propagation (PTP) [9] web proxy IP URL IP PTP PTP [9] DNS DNS (DNS) DNS DNS A IP

4 Vol. 33 No. 3 Aug DNS ( 1) G X E G = (X, E) IP IP IP i IP j i IP j (e ij E) 1 IP 1 IP ( CDN) 1 IP 1 IP ( ) DNS [5] IP 0 x j 1 O(N 2 ) 1 1 k 2 P k (x i) = w ij(p k 1 (x j) C k 1 (x i, x j)) j N(x i ) (2) P k (x i ) k x i P k 1 (x j) k 1 x i x j C k 1 (x i, x j ) k 1 x i x j C k 1 x i x j k x j x i 3. 2 EPTP PTP [9] (Malicious ) {malicious} P (x malicious) = γ PTP G x i 1 P (x i ; G) = w ij P (x j x i = 0; G) (1) j N(x i ) 1 N(x i ) x i w ij i j P (x j x i = 0; G) x i 2 k C k 1 3 C k 1 (x i, x j ) = w ji (P k 2 (x i )) (3) w ij 4 w ij = 1 N(x i ) e ij E 0 e ij E (4) PTP Alexa [1] DNS Alexa {benign}

5 20 Require: W, {malicious}, {benign}, γ, β 1: P α N, P (malicious) γ, P (benign) β, C 0 N N 2: repeat 3: T W diag(p ) 4: C T W C T 5: P < C, 1 > 6: C(malicious, ) 0, C(benign, ) 0 7: P (malicious) γ, P (benign) β 8: until P has converged 9: return P (A(i, j) B(i, j)) {malicious} P (malicious) γ {benign} P (benign) β 0 0 T W diag(p ) (diag(p ) P N N ) T W C T 2 2 Extended Probability Threat Propagation C C P =< C, 1 > {benign} Benign Malicious 1 PTP Malicious γ [0,1] 0 EPTP β > 0 γ < 0 α = γ+β 2 P (x) γ P (x) < α P (x) γ α < P (x) β P (x) β Benign β = 1 Malicious γ = β = 1 α = γ+β 2 = 0 ( 2) EPTP 2 N P R N (P (i) = P (x i )) W R N N T R N N (T (i, j) = W (i, j) P (j)) C R N N, C = T W C T A B 1 EPTP (<, > 1 N 1 ) P {malicious} {benign} P C x x {malicious}, {benign} EPTP 1 P P x k PTP DNS DNS 1 DNS tcpdump UDP port

6 Vol. 33 No. 3 Aug Total Number (x1000) IN domains OUT domains KEEP domains Total Number (x1000) IN domains OUT domains KEEP domains Total Number (x1000) IN domains OUT domains KEEP domains Total Number (x1000) IN domains OUT domains KEEP domains /05 11/12 11/19 11/ /05 11/12 11/19 11/ /05 11/12 11/19 11/ /11 11/18 11/25 Time (JST 2013) Time (JST 2013) Time (JST 2013) Time (JST 2013) (a) (b) Benign (c) Malicious (d) Suspicious 3 IN KEEP OUT 24 DNS (A ) IP DNS 1,348,547, IP 2,417,727 3,917, DNS (Malicious) malwaredomains.com [18] uribl.com [21] (Malicious ) (Benign) Alexa [1] (Alexa ) 30,000 30,653 Alexa DGA 5 DNS DNS 5. 1 DNS 1 DNS k k 1 KEEP k 1 k OUT k 1 k IN DNS (All) (Malicious) (Benign) DNS IN KEEP OUT 3 (a) (c) 30,000 30,653 KEEP IN OUT 1 65% KEEP KEEP ( 71.8%) IN OUT ( 51.4%)

7 22 1 KEEP IN OUT KEEP IN OUT ALL 65.9% 17.1% 17.1% Benign 71.8% 14.1% 14.1% Malicious 48.6% 25.8% 25.6% Suspicious 65.3% 17.4% 17.2% 2 DNS d = 1 d = 24 1,271,975 3,766, ,266 1,348,547 IP 603,709 2,417,727 1,199,612 3,917, , , DNS 2013/11/05 1 (d = 1) 2013/11/ (d = 24) 2 DNS d=1 DNS DNS d = 24 d=1 DNS IP DNS d= 24 DNS 4 75% IP 1 1 d=24 70% Frequency Number of nodes 3 10 (d = 24) No. IP Benign Malicious 1 778,279 1,832, , , , , , , , , , % (d=24) 1 IP IP 2 spmode.ne.jp pandaworld.ne.jp 1

8 Vol. 33 No. 3 Aug d=1 d=24 567,663 2,610, , ,279 IP 218,082 1,832, ,706 3,095, (%) (%) ave med B B 912, ,422, M M 13, , B M 343, ,911, U U 2,738, ,226, IP 4 DNS 20 2 Frequency Benign-Benign Malicious-Malicious Benign-Malicious Unknown-Unknown Node distance d=24 d= DNS (d= ) 3 d=1 d=24 2 (B B) (B M) 8.4 (M M) % IP (B B) 5. 3 d=1 Benign Malicious Unknown 5 5 B B, M M, B M, U U Benign Malicious Benign Malicious Unknown (B M) Unknown (U U) B M 6 EPTP EPTP PTP, N-gram EPTP DNS

9 24 Ratio TPR 0.0 FPR Threshold True Positive Rate cv-Original PTP 10-cv-Extended PTP cv-Original PTP 5-cv-Extended PTP 0.00 Bigram-based False Positive Rate ROC (k-fold cross validation) 6. 1 EPTP 2,000, 1,973 k-fold cross validation (CV) (k = 5, 10) DNS 1 10-fold CV P (x) TPR (True Positive Rate) FPR (False Positive Rate) 6 TPR τ = 0 TPR 90% FPR τ = 0 FPR TLD biz net info SLD DGA Receiver Operatorating Characteristic (ROC) (EPTP) (PTP) N-gram [17] [26] 7 10-fold CV ROC EPTP FPR PTP TPR Time (s) / Memory (MiB) Memory (MiB) Time (s) Number of nodes EPTP EPTP TPR fold CV 10-fold CV ROC 10-fold CV N-gram 40% 1,000 10, ,000 1,000,000 EPTP 8 (CPU: Intel Xeon X GHz; Memory 32GB) 10

10 Vol. 33 No. 3 Aug c4brcwmg.biz info-ezweb-ne-jp.info poohpoohhany.info bvncm-kdkdkgree.jp nomoguz.su kisjehmbga.jp google-play.jp IP EPTP IP 1 IP IP IP 7 9 ( :, : IP : ) 6. 2 DNS d=24 DNS EPTP 6 FPR 0 τ = IP 2,170 IP 12,884 6 EPTP (τ = 0.1) IN KEEP OUT 6. 2 EPTP Suspicious IN KEEP OUT ( 3 (d) 1) Suspicious ALL IN KEEP OUT IN KEEP OUT Suspicious Malicious DGA 6 Suspicious DGA DNS 5 5 2

11 26 PTP DNS DNS IP DNS, d= DNS DNS DNS 69% 1 IP IP 7. 3 EPTP [9] PTP Alexa 10-fold CV FPR (=0.016) 8% 90.4% ( 7) EPTP 6 τ = 0 DNS 8 10 DNS 5-fold CV 10-fold CV 6 τ 0.1 2,170 ( 6) DGA google-play.jp N-gram EPTP ( 9) IP IP

12 Vol. 33 No. 3 Aug DNS A PTP DNS 1 EPTP DNS 90.4% 2,170 Malicious Alaxa (15H02699) (EU) FP7 ( :NECOMA) [ 1 ] Alexa: Alexa [Online], topsites/. [ 2 ] Antonakakis, M., Perdisci, R., Dagon, D., Lee, W. and Feamster, N.: Building a Dynamic Reputation System for DNS, in Proceedings of USENIX security symposium, 2010, pp [ 3 ] Antonakakis, M., Perdisci, R., Lee, W., Vasiloglou II, N. and Dagon, D.: Detecting Malware Domains at the Upper DNS Hierarchy, in Proceedings of USENIX Security Symposium, 2011, pp [ 4 ] Antonakakis, M., Perdisci, R., Nadji, Y., Vasiloglou II, N., Abu-Nimeh, S., Lee, W. and Dagon, D.: From Throw-Away Traffic to Bots: Detecting the Rise of DGA-Based Malware, in Proceedings of USENIX Security Symposium, 2012, pp [ 5 ] Arkko, J., Cotton, M. and Vegoda, L.: IPv4 Address Blocks Reserved for Documentation, RFC 5737, January [ 6 ] Bar, A., Paciello, A. and Romirer-Maierhofer, P.: Trapping botnets by DNS failure graphs: validation, extension and application to a 3G network, in Proceedings of TMA 13, IEEE, 2013, pp [ 7 ] Bilge, L., Sen, S., Balzarotti, D., Kirda, E. and Kruegel, C.: EXPOSURE: a passive DNS analysis service to detect and report malicious domains, ACM Transactions on Information and System Security (TISSEC), Vol. 16, No. 4(2014), p. 14. [ 8 ] Brownlee, N., Claffy, K. and Nemeth, E.: DNS measurements at a root server, in Proceedings of GLOBECOM 01, Vol. 3, IEEE, 2001, pp [ 9 ] Carter, K. M., Idika, N. and Streilein, W. W.: Probabilistic threat propagation for malicious activity detection, in Proceedings of ICASSP 13, IEEE, 2013, pp [10] Castro, S., Wessels, D., Fomenkov, M. and Claffy, K.: A Day at the Root of the Internet, ACM SIGCOMM Computer Communication Review, Vol. 38, No. 5(2008), pp [11] Chau, D., Nachenberg, C., Wilhelm, J., Wright, A. and Faloutsos, C.: Polonium: Tera-scale graph mining and inference for malware detection, in Proceedings of SIAM International Conference on Data Mining, Vol. 2, [12] Gao, H., Yegneswaran, V., Chen, Y., Porras, P., Ghosh, S., Jiang, J. and Duan, H.: An empirical reexamination of global DNS behavior, in Proceedings of SIGCOMM 13, ACM, 2013, pp [13] Hao, S., Feamster, N. and Pandrangi, R.: Monitoring the initial DNS behavior of malicious domains, in Proceedings of IMC 11, ACM, 2011, pp [14] Ishibashi, K. and Sato, K.: Classifying DNS Heavy User Traffic by using Hierarchical Aggregate Entropy, in Proceedings of World Telecommunications Congress (WTC 12), 2012, pp [15] Ishibashi, K., Toyono, T., Hasegawa, H. and Yoshino, H.: Extending black domain name list by using co-occurrence relation between DNS queries, IEICE Transactions on Communications, Vol. 95, No. 3(2012), pp [16] Jiang, N., Cao, J., Jin, Y., Li, L. E. and Zhang, Z.-L.: Identifying suspicious activities through DNS failure graph analysis, in Proceedings of ICNP 10, IEEE, 2010, pp [17] Kazato, Y., Fukuda, K. and Sugawara, T.: To-

13 28 wards classification of DNS erroneous queries, in Proceedings of AINTEC 13, ACM, 2013, pp [18] Malware Domain Blocklist: DNS-BH Malware Domain Blocklist, com/. [19] Manadhata, P. K., Yadav, S., Rao, P. and Horne, W.: Detecting Malicious Domains via Graph Inference, in Proceedings of ESORICS 14, Springer, 2014, pp [20] Osterweil, E., McPherson, D., DiBenedetto, S., Papadopoulos, C. and Massey, D.: Behavior of DNS Top Talkers, a. com/. net View, in Proceedings of PAM 12, Springer, 2012, pp [21] P. Vixie: Traltime URI Blacklist, com/. [22] Perdisci, R., Corona, I., Dagon, D. and Lee, W.: Detecting malicious flux service networks through passive analysis of recursive DNS traces, in Proceedings of ACSAC 09, IEEE, 2009, pp [23] Schiavoni, S., Maggi, F., Cavallaro, L. and Zanero, S.: Phoenix: DGA-based botnet tracking and intelligence, in Proceedings of DIMVA 14, Springer, 2014, pp [24] Wessels, D. and Fomenkov, M.: Wow, that s a lot of packets, in Proceedings of PAM 03, [25] Xuebiao, Y., Xin, W., Xiaodong, L. and Baoping, Y.: DNS measurements at the.cn TLD servers, in Proceedings of FSKD 09, Vol. 7, 2009, pp [26] Yadav, S., Reddy, A., Reddy, A. and Ranjan, S.: Detecting algorithmically generated malicious domain names, in Proceedings of IMC 10, ACM, 2010, pp [27] Yadav, S. and Reddy, A. N.: Winning with DNS failures: Strategies for faster botnet detection, in Proceedings of SecureCom 12, Springer, 2012, pp [28],,, : DNS,. IOT, No. 21(2009), pp ( ( )) (2002) ( ; ) / ( ) 1982., ,.,.,,,,. ( ).,,,, ISOC IEEE, ACM, AAAI.

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