XSS Cross-Site Scripting [1] SQL [2], [3], [4] Web [5] Web XSS SQL 1 WAF Web Application Firewall [6] WAF Web Web HTTP WAF HTTPS WAF False Positive 1
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- さゆり みやくぼ
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1 WAF 1,a) , Web Web Cross-Site Scripting SQL Web 1 WAF WAF WAF SVM False Positive XSS SQL Web Apache Apache WAF ModSecurity ModSecurity ModSecurity Web Application Firewall WAF Support Vector Machine SVM N-gram Implementation and Evaluation of Anomaly Detection Using Support Vector Machine for WAF Yasushi Iha 1,a) Tomio Takara 2 Received: July 25, 2013, Accepted: January 6, 2014 Abstract: Coupled with the rapid development of the internet, use of Web applications has increased in various fields. Meanwhile, serious damage such as XSS attacks and SQL injection attacks targeted at Web applications has been reported. One way to protect Web applications from such attacks is to use WAF. But the problem with WAF is that the processing power is reduced because of the increase in rules and that it is difficult to deal with unknown attacks. Therefore, in this article, by using SVM for inspection of input value by WAF, we propose the method by which False Positive, the rate of misdetection of normal data, is declined and unknown attacks can be detected at the same time. And then we show its effectiveness by an experiment. Besides, the method proposed by us is implemented as a module of Apache. And then, to evaluate its performance, we conduct a comparative experiment with ModSecurity, which is the standard WAF of Apache. And from its result we discuss its effectiveness. It has been found from the result of the comparative experiment that it can carry out detection with recognition performance and processing performance exceeding those of ModSecurity. Keywords: Web Application Firewall (WAF), anomaly detection, Support Vector Machine (SVM), N-gram 1 Department of Media Information Engineering, Okinawa National College of Technology, Nago, Okinawa , Japan 2 Department of Information Engineering, University of the Ryukyus, Nishihara, Okinawa , Japan a) yasuc@okinawa-ct.ac.jp 1. WWW World Wide Web Web Web c 2014 Information Processing Society of Japan 1
![XSS Cross-Site Scripting [1] SQL [2], [3], [4] Web [5] Web XSS SQL 1 WAF Web Application Firewall [6] WAF Web Web HTTP WAF HTTPS WAF False Positive 1 Bolzoni N-gram SVM WAF Panacea RIPPER SVM [7] SQL](/docs-images/93/111839114/images/2-0.jpg "SVM [8], [9], [10], [11] Komiya XSS SQL SVM [12] N-gram SVM Web Apache Web Apache Web WAF ModSecurity [13] ModSecurity Web [14] False Positive ModSecurity Web ModSecurity Apache XSS SQL WAF Web")
2 XSS Cross-Site Scripting [1] SQL [2], [3], [4] Web [5] Web XSS SQL 1 WAF Web Application Firewall [6] WAF Web Web HTTP WAF HTTPS WAF False Positive 1 Bolzoni N-gram SVM WAF Panacea RIPPER SVM [7] SQL SVM [8], [9], [10], [11] Komiya XSS SQL SVM [12] N-gram SVM Web Apache Web Apache Web WAF ModSecurity [13] ModSecurity Web [14] False Positive ModSecurity Web ModSecurity Apache XSS SQL WAF Web N-gram SVM ModSecurity [15], [16] ModSecurity Apache N-gram SVM False Positive Mod- Security XSS SQL WAF ModSecurity 2 Web WAF SVM 3 SVM WAF Apache 4 5 ModSecurity 6 2. Web Web XSS SQL SVM 2.1 Web Web Web Web Web Web HTTP Hyper Text Transfer Protocol 1 Web Web Web Web HTTP HTTP HTTP Web Cookie 1 Web Fig. 1 Overview of Web application. c 2014 Information Processing Society of Japan 2
3 ID Web XSS SQL 2.2 XSS Cross-Site Scripting Web Web Web Web Web JavaScript XSS Cross-Site Scripting XSS XSS Cookie Cookie XSS XSS HTML XSS (1) <script>document.cookie= sid=root </script> (2) > <script> ><script>document.cookie= sid=root </script> (3) > HTML <script> ><script>document.cookie= sid=root </script> XSS XSS HTML HTML HTML HTML XSS Web Web 2.3 SQL Web SQL SQL SQL SQL OS SQL Web Web SQL SELECT uid FROM account table WHERE uid= ID AND pw= SQL ID ID ID ID OR1=1 SQL SELECT uid FROM account table WHERE uid= OR 1=1 AND pw= OR1=1 1 OR 1=1 uid c 2014 Information Processing Society of Japan 3
4 ID SQL SQL SQL SQL SQL SQL SQL XSS Web WAF XSS SQL ModSecurity ModSecurity Apache WAF ModSecurity Web Web hidden Cookie 2.4 WAF Web Application Firewall Web XSS SQL Web Web Web Web XSS SQL WAF Web Application Firewall WAF WAF 3 (1) (2) Web ( 3 ) hidden hidden Cookie WAF False Positive 2.5 SVM SVM Vapnik 2 SVM SVM 2 [17] SVM S (x 1,y 1 ), (x 2,y 2 ),,(x n,y n ) (1) i, x i R N,y i { 1, 1} x i i y i i +1 1 SVM 2 S (2) 2 SVM Fig. 2 Linear SVM. c 2014 Information Processing Society of Japan 4
5 f(x) =w x+b (2) w R N,b R w n (w x) w x SVM (x i x i ) (x i x j +1) d RBF Radial Basis Function exp{ x i x j 2 /2σ 2 } SVM SVM 3. SVM WAF WAF 2 1 Web N-gram SVM XSS SQL N-gram 2 SVM N-gram SVM SVM 3.2 N-gram SVM 3 SVM WAF Fig. 3 Anomaly detection using SVM for WAF. 4 unigram Fig. 4 Examples of converting to unigram. 1 XSS SQL Table 1 Data of XSS and SQL injection. XSS C. Snake: XSS (Cross Site Scripting) Cheat Sheet SQL Unixwiz: SQL Injection Attacks by Example XSS & SQL HTTP DATASET CSIC ,100 N-gram N-gram N-gram N-gram ASCII UNICODE UNICODE N-gram SVM N-gram N-gram N-gram N 1 unigram N 2 bigram N 3 trigram 4 unigram ,400 XSS 1,400 SQL 1,000 c 2014 Information Processing Society of Japan 5
6 2 Table 2 Data of normal message Global NAVITIME New York twitter The Top 500 Worst Passwords of All Time /the-top-500-worst-passwords-of-all-time HTTP DATASET CSIC ,400 Fig. 5 5 Difference in the recognition result from training data. 3 Table 3 Environment of experimental. OS Fedora 14 CPU Intel R Core TM i GHz RAM 4GB Web Apache SVM Lib SVM 3.11 WAF ModSecurity ModSecurity ModSecurity SVM ModSecurity 2 7,000 Web 5 N-gram SVM [15] 5 MIT DARPA IDS Evaluation Data Sets 1999 UCI KDD Cup 1999 Data WAF HTTP DATASET CSIC 2010 [18] N-gram LibSVM LibSVM svm-train svm-predict N-gram N 1 3 Accuracy F F β=1 Accuracy Accuracy = F c 2014 Information Processing Society of Japan 6
7 Fig. 6 6 Results of preliminary experiment. 8 Apache hook Fig. 8 Implementation using a hook function to Apache. Fig. 7 7 Overview of the implementation module F F β=1 (3) F (β=1) = 2 Recall P recision Recall + P recision (3) (3) Recall Precision Recall = Precison = 6 C-SVM F unigram 0.98 bigram 0.95 trigram 0.92 unigram Accuracy 99.3% unigram False Positive 0% False Negative 4%Accuracy F SVM 3.5 Apache 7 Apache mod SVM Apache Apache hook hook HTTP hook ap hook access checker hook SVM 8 hook SVM Apache Apache HTTP ap hook access checker Apache HTTP reqest rec request rec GET Apache request rec args GET args & POST Apache2 POST bucket brigade bucket brigade bucket bucket request rec input filters bucket brigade bucket EOS bucket POST & Apache SVM N-gram SVM Web 403 Forbidden c 2014 Information Processing Society of Japan 7
8 Apache SVM LibSVM [19] Solver Type C-SVM SVM RBF SVM ,400 1,200 1, ,000 3,500 3,500 ModSecurity ModSecurity XSS SQL 4.1 mod SVM ModSecurity Apache mod SVM ModSecurity Perl HTTP N-gram N=1 N=2 Accuracy mod SVM N=1 N=2 4 Closed Test 5 Open Test mod SVM N =2 Closed Test 100% ModSecurity 99.85% Open Test N= % F ModSecurity 99.81% F Closed Test Table 4 Result of Closed Test. ModSecurity svm N =1 svm N =2 Accuracy 99.85% 99.89% % Precision 99.42% % % Recall % 99.58% % F-measure Open Test Table 5 Result of Open Test. ModSecurity svm N =1 svm N =2 Accuracy 99.81% 99.60% 99.98% Precision 99.26% 99.33% % Recall % 99.08% 99.92% F-measure Fig. 9 Result of the processing time. N =2 mod SVM ModSecurity XSS SQL ModSecurity 100% mod SVM 4.2 mod SVM ModSecurity Apache No WAF Apache mod SVM ModSecurity Perl HTTP 1,000 OS time 10 9 c 2014 Information Processing Society of Japan 8
![10 1 Fig. 10 Processing time of 1 normal access. ModSecurity mod SVM [N = 2] 4.67 XSS SQL 3.](/docs-images/93/111839114/images/9-2.jpg "38 mod SVM [N = 2] WAF 1.13 1.12 ModSecurity WAF 5.28 3.78 ModSecurity mod SVM N=2 N =1 1.")
9 10 1 Fig. 10 Processing time of 1 normal access. ModSecurity mod SVM [N = 2] 4.67 XSS SQL 3.38 mod SVM [N = 2] WAF ModSecurity WAF ModSecurity mod SVM N=2 N = Apache ab Apache Bench 100 1, mod SVM ModSecurity 4.3 WAF WAF XSS CGI Perl mod SVM XSS 11 1 Fig. 11 Processing time of 1 malicious access. 12 XSS Fig. 12 Page with XSS. 13 XSS Fig. 13 XSS success. 14 XSS Fig. 14 XSS failure. 12 XSS mod SVM <script>alert( hoge );</script> 13 mod SVM Forbidden c 2014 Information Processing Society of Japan 9
10 6 SecRule Table 6 Argument of SecRule. / VARIABLES HTTP OPERATOR ACTIONS OPERATOR 5. ModSecurity XSS SQL 5.1 ModSecurity ModSecurity 5 Phase:1 Request headers REQUEST HEADERS Phase:2 Request body REQUEST BODY Phase:3 Response headers RESPONSE HEADERS Phase:4 Response body RESPONSE BODY Phase:5 Logging LOGGING 5 Apache HTTP post-read-request Phase:1 URI translation Header parsing access control authentication authorization MIME type checking fixups Phase:2 RESPONSE Phase:3, Phase:4 logging Phase:5 XSS SQL Phase:1 SecRule SecRule 6 3 OPERATOR OPERATOR ACTIONS 5.2 XSS SQL ModSecurity XSS ModSecurity Core Rule Set CRS base rules modsecurity crs 41 xss attacks.conf SecRule 113 XSS Filters - Category 11 XSS Filters - Category 21 XSS Filters - Category 31 XSS69 Detect tags3 Detect event handler names1 Detect usage of common URI attributes 3 JavaScript fragments1 CSS attack fragments2 Misc5 XSS Filters from IE26 SQL modsecurity crs 41 sql injection attacks.conf SecRule 55 Detect SQL Comment Sequences1 SQL Hex Evasion Methods1 String Termination/Statement Ending Injection Testing1 SQL Operators1 SQL Tautologies1 Detect DB Names1 SQL Keyword Anomaly Scoring18 Blind SQL injection1 SQL injection6 SQL Injection Character Anomaly Usage 2 PHPIDS - Converted SQLI Filters22 SecRule OPERATOR ModSecurity ModSecurity c 2014 Information Processing Society of Japan 10
11 15 Fig. 15 ModSecurity Access string and log of ModSecurity. 5.3 N-gram XSS SQL Web N-gram N-gram N-gram N-gram N=1 N=4 char long SVM ModSecurity ModSecurity 5.4 SVM WAF XSS SQL SVM [9] N-gram XSS SQL Precision = 98.04% TPR = FPR = TPR = TP/(TP + FN) FPR = FP/(FP + TN) TP FN FP TN [12] XSS SQL SQL Accuracy = 99.16% Precision = Recall = 1.00 XSS Accuracy = 98.95% Precision = Recall = N-gram XSS SQL Accuracy = 99.98% Precision = 100.0% Recall = 99.92% TPR = FPR = 0 ModSecurity XSS SQL N-gram ModSecurity 15 ModSecurity c 2014 Information Processing Society of Japan 11
12 ID ModSecurity pwd= 04or2ativ1 04or2 SQL or classic SQL injection or False Positive SVM N-gram False Positive Open Test SVM ModSecurity ModSecurity 100% ModSecurity Apache WAF ModSecurity WAF 2 6. Web WAF Web N-gram SVM Apache Apache WAF ModSecurity Accuracy = 99.98% Precision = 100.0% Recall = 99.92% F = False Positive ModSecurity ModSecurity XSS SQL [1] OWASP: Cross-site Scripting (XSS) OWASP, available from Scripting (XSS) (2011). [2] Anley, C.: Advanced SQL injection in SQL Server applications, available from papers/advanced sql injection.pdf (2002). [3] Anley, C.: (more) advanced SQL injection, available from advanced sql injection.pdf (2002). [4] OWASP: SQL Injection - OWASP, available from Injection (2012). [5] security/vuln/report/vuln2012q3.html (2012). [6] 4 websecurity.html (2012). [7] Bolzoni, D. and Etalle, S. and Hartel, P.H.: Panacea: Automating Attack Classification for Anomaly-based Network Intrusion Detection Systems, Recent Advances in Intrusion Detection (RAID), pp.1 20 (2009). [8] Pinzon, C., Herrero, A., de Paz, J.F., Corchado, E. and Bajo, J.: CBRid4SQL: A CBR Intrusion Detector for SQL Injection Attacks, Hybrid Artificial Intelligence Systems, 5th International Conference, Vol.6077, pp (2010). [9] Choi, J., Kim, H., Choi, C. and Kim, P.: Efficient Malicious Code Detection Using N-Gram Analysis and SVM, Proc th International Conference on Network- Based Information Systems, NBIS 11, pp , IEEE Computer Society (online), (2011). [10] Rawat, R., Zodape, M.: URLAD (URL attack detection) - using SVM, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, No.1 (2012). [11] Rawat, R. and Shrivastav, S.: SQL injection attack Detection using SVM, International Journal of Computer Applications, Vol.42, No.13, pp.1 4 (2012). [12] Komiya, R., Paik, I. and Hisada, M: Classification of malicious web code by machine learning, Awareness Science and Technology (icast ), rd International Conference on pp (2011). [13] ModSecurity: Open Source Web Application Firewall, available from [14] Ways to improve performance of your server in ModSecurity 2.5, available from article/ways-improve-performance-server-inmodsecurity2.5. c 2014 Information Processing Society of Japan 12
![[15] SVM WAF 73 pp.445 447 (2011). [16] SVM WAF 74 Vol.3, pp.445 447 (2012). [17] Cristianini, N. and Shawe-Taylor, J. (2005).](/docs-images/93/111839114/images/13-0.jpg "[18] Spanish Scientific Research Council (C.S.I.C.): HTTP DATASET CSIC 2010, available from http://iec.csic. es/dataset/ (2010). [19] Chang, C.-C.")
13 [15] SVM WAF 73 pp (2011). [16] SVM WAF 74 Vol.3, pp (2012). [17] Cristianini, N. and Shawe-Taylor, J. (2005). [18] Spanish Scientific Research Council (C.S.I.C.): HTTP DATASET CSIC 2010, available from es/dataset/ (2010). [19] Chang, C.-C. and Lin, C.-J.: LIBSVM: A library for support vector machines, ACM Trans. Intelligent Systems and Technology, 2:27:1 27:27 (2011), available from cjlin/libsvm IEEE c 2014 Information Processing Society of Japan 13
29 jjencode JavaScript
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