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Yıl 2019, , 199 - 212, 15.12.2019
https://doi.org/10.31796/ogummf.560747

Öz

Kaynakça

  • Ali, M. Q., Khan, H., Sajjad, A., & Khayam, S. A. (2009). On achieving good operating points on an ROC plane using stochastic anomaly score prediction. 16th ACM conference on Computer and communications security, Şikago, ABD.
  • Anscombe, F. J., & Guttman, I. (1960). Rejection of Outliers. Technometrics, Sayı(2), 123-147. doi: https://doi.org/10.2307/1266540
  • Axelsson, S. (2000). Intrusion detection systems: A survey and taxonomy.
  • Bace, R., & Mell, P. (2001). Intrusion Detection Systems Erişim adresi: https://apps.dtic.mil/dtic/tr/fulltext/u2/a393326.pdf
  • Bhatkar, S., Chaturvedi, A., & Sekar, R. (2006, 21-24 May 2006). Dataflow anomaly detection. 2006 IEEE Symposium on Security and Privacy (S&P'06) Sunulmuş Bildiri.
  • Borisaniya, B., & Patel, D. (2015). Evaluation of modified vector space representation using adfa-ld and adfa-wd datasets. Journal of Information Security, Sayı(3), 250. doi: https://doi.org/10.4236/jis.2015.63025
  • Cabrera, J. B. D., Lewis, L., & Mehra, R. K. (2001). Detection and classification of intrusions and faults using sequences of system calls. ACM SIGMOID Record, Sayı(4), 25-34. doi: https://doi.org/10.1145/604264.604269
  • Canali, D., Lanzi, A., Balzarotti, D., Kruegel, C., Christodorescu, M., & Kirda, E. (2012). A quantitative study of accuracy in system call-based malware detection. International Symposium on Software Testing and Analysis Sunulmuş Bildiri.
  • Canfora, G., Sorbo, A. D., Mercaldo, F., & Visaggio, C. A. (2015, 22-22 May 2015). Obfuscation Techniques against Signature-Based Detection: A Case Study. 2015 Mobile Systems Technologies Workshop (MST) Sunulmuş Bildiri.
  • CERT-UK. Code obfuscation. Erişim adresi: https://www.ncsc.gov.uk/content/files/protected_files/guidance_files/Code-obfuscation.pdf
  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, Sayı(3), 15.
  • Chen, W.-H., Hsu, S.-H., & Shen, H.-P. (2005). Application of SVM and ANN for intrusion detection. Computers and Operations Research, Sayı(10), 2617-2634. doi: https://doi.org/10.1016/j.cor.2004.03.019
  • Cohen, W. W. (1995). Fast Effective Rule Induction. A. Prieditis & S. Russell (Eds.), Machine Learning Proceedings 1995 (115-123). San Francisco (CA).
  • Creech, G. (2014). Developing a high-accuracy cross platform Host-Based Intrusion Detection System capable of reliably detecting zero-day attacks. University of New South Wales, Canberra, Avustralya.
  • Creech, G., & Hu, J. (2014). A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns. IEEE Transactions on Computers, Sayı(4), 807-819. doi: https://doi.org/10.1109/tc.2013.13
  • DARPA Intrusion Detection Evaluation Dataset. (1998). Erişim adresi: https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset
  • DARPA Intrusion Detection Evaluation Dataset. (1999). Erişim adresi: https://www.ll.mit.edu/r-d/datasets/1999-darpa-intrusion-detection-evaluation-dataset
  • Debar, H., Dacier, M., & Wespi, A. (2000). A revised taxonomy for intrusion-detection systems. Annales des télécommunications, Sayı(7-8), 361-378.
  • Deshpande, P., Sharma, S., Peddoju, S., & Junaid, S. (2018). HIDS: A host based intrusion detection system for cloud computing environment. International Journal of System Assurance Engineering Management, Sayı(3), 567-576. doi: https://doi.org/10.1007/s13198-014-0277-7
  • Du, M., Li, F., Zheng, G., & Srikumar, V. (2017). Deeplog: Anomaly detection and diagnosis from system logs through deep learning. 2017 ACM SIGSAC Conference on Computer and Communications Security Sunulmuş Bildiri.
  • Duessel, P., Gehl, C., Flegel, U., Dietrich, S., & Meier, M. (2017). Detecting zero-day attacks using context-aware anomaly detection at the application-layer. International Journal of Information Security, Sayı(5), 475-490. doi: https://doi.org/10.1007/s10207-016-0344-y
  • Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S. (2002). A Geometric Framework for Unsupervised Anomaly Detection. D. Barbará & S. Jajodia (Eds.), Applications of Data Mining in Computer Security (77-101). Massachusetts, ABD: Springer US. doi: https://doi.org/10.1007/978-1-4615-0953-0_4
  • Eskin, E., Lee, W., & Stolfo, S. J. (2001). Modeling system calls for intrusion detection with dynamic window sizes. DARPA Information Survivability Conference and Exposition II. DISCEX'01 Sunulmuş Bildiri.
  • Feng, L., Guan, X., Guo, S., Gao, Y., & Liu, P. (2004). Predicting the intrusion intentions by observing system call sequences. Computers and Security, Sayı(3), 241-252. doi: https://doi.org/10.1016/j.cose.2004.01.016
  • Forrest, S., Hofmeyr, S. A., Somayaji, A., & Longstaff, T. A. (1996, 6-8 May 1996). A sense of self for Unix processes. 1996 IEEE Symposium on Security and Privacy Sunulmuş Bildiri.
  • Ghosh, A. K., Schwartzbard, A., & Schatz, M. (1999). Learning Program Behavior Profiles for Intrusion Detection. Workshop on Intrusion Detection and Network Monitoring.
  • Grimmer, M., Röhling, M. M., Kricke, M., Franczyk, B., & Rahm, E. (2018). Intrusion Detection on System Call Graphs. 25. DFN-Konferenz "Sicherheit in vernetzten Systemen" Sunulmuş Bildiri, Hamburg, Almanya.
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SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ

Yıl 2019, , 199 - 212, 15.12.2019
https://doi.org/10.31796/ogummf.560747

Öz

Günümüzde
yaygın bir şekilde kullanılmakta olan imza tabanlı yaklaşımlar, özellikle sıfır
gün saldırıları gibi henüz tespit edilmemiş saldırı vektörlerine karşı
başarısız olmaktadırlar. Bu tip saldırılar genellikle en az bir sisteme zarar
verdikten sonra tespit edilmektedir. Saldırıya ilişkin imza yapılan analizin
ardından son kullanıcıların erişimine sunulur. Dolayısı ile bu süre zarfında
kullanıcılar bu tip saldırılara karşı savunmasız kalırlar. Kritik noktalardaki
bilgisayar sistemlerinin gerek güncelleme ve gerekse yeni uygulamaların
kurulmasının ardından sıfır gün saldırıları ile karşılaşma riski bulunmaktadır.
Bilindiği üzere, uygulamalar işletim sistemiyle sistem çağrı arayüzü üzerinden
etkileşim kurarlar. Dolayısı ile uygulamalardan ya da sistemin tümünden
toplanan sistem çağrı verisinde öğrenme sonrasında belirlenen anormal
davranışlar bir saldırının varlığını işaret ediyor olabilir. Bu çalışmada, anomali
tespit sistemleri için literatür taraması, kullanılabilecek veri kümeleri ve bunların
karşılaştırmalı analizleri sunulmuştur.




Kaynakça

  • Ali, M. Q., Khan, H., Sajjad, A., & Khayam, S. A. (2009). On achieving good operating points on an ROC plane using stochastic anomaly score prediction. 16th ACM conference on Computer and communications security, Şikago, ABD.
  • Anscombe, F. J., & Guttman, I. (1960). Rejection of Outliers. Technometrics, Sayı(2), 123-147. doi: https://doi.org/10.2307/1266540
  • Axelsson, S. (2000). Intrusion detection systems: A survey and taxonomy.
  • Bace, R., & Mell, P. (2001). Intrusion Detection Systems Erişim adresi: https://apps.dtic.mil/dtic/tr/fulltext/u2/a393326.pdf
  • Bhatkar, S., Chaturvedi, A., & Sekar, R. (2006, 21-24 May 2006). Dataflow anomaly detection. 2006 IEEE Symposium on Security and Privacy (S&P'06) Sunulmuş Bildiri.
  • Borisaniya, B., & Patel, D. (2015). Evaluation of modified vector space representation using adfa-ld and adfa-wd datasets. Journal of Information Security, Sayı(3), 250. doi: https://doi.org/10.4236/jis.2015.63025
  • Cabrera, J. B. D., Lewis, L., & Mehra, R. K. (2001). Detection and classification of intrusions and faults using sequences of system calls. ACM SIGMOID Record, Sayı(4), 25-34. doi: https://doi.org/10.1145/604264.604269
  • Canali, D., Lanzi, A., Balzarotti, D., Kruegel, C., Christodorescu, M., & Kirda, E. (2012). A quantitative study of accuracy in system call-based malware detection. International Symposium on Software Testing and Analysis Sunulmuş Bildiri.
  • Canfora, G., Sorbo, A. D., Mercaldo, F., & Visaggio, C. A. (2015, 22-22 May 2015). Obfuscation Techniques against Signature-Based Detection: A Case Study. 2015 Mobile Systems Technologies Workshop (MST) Sunulmuş Bildiri.
  • CERT-UK. Code obfuscation. Erişim adresi: https://www.ncsc.gov.uk/content/files/protected_files/guidance_files/Code-obfuscation.pdf
  • Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, Sayı(3), 15.
  • Chen, W.-H., Hsu, S.-H., & Shen, H.-P. (2005). Application of SVM and ANN for intrusion detection. Computers and Operations Research, Sayı(10), 2617-2634. doi: https://doi.org/10.1016/j.cor.2004.03.019
  • Cohen, W. W. (1995). Fast Effective Rule Induction. A. Prieditis & S. Russell (Eds.), Machine Learning Proceedings 1995 (115-123). San Francisco (CA).
  • Creech, G. (2014). Developing a high-accuracy cross platform Host-Based Intrusion Detection System capable of reliably detecting zero-day attacks. University of New South Wales, Canberra, Avustralya.
  • Creech, G., & Hu, J. (2014). A Semantic Approach to Host-Based Intrusion Detection Systems Using Contiguousand Discontiguous System Call Patterns. IEEE Transactions on Computers, Sayı(4), 807-819. doi: https://doi.org/10.1109/tc.2013.13
  • DARPA Intrusion Detection Evaluation Dataset. (1998). Erişim adresi: https://www.ll.mit.edu/r-d/datasets/1998-darpa-intrusion-detection-evaluation-dataset
  • DARPA Intrusion Detection Evaluation Dataset. (1999). Erişim adresi: https://www.ll.mit.edu/r-d/datasets/1999-darpa-intrusion-detection-evaluation-dataset
  • Debar, H., Dacier, M., & Wespi, A. (2000). A revised taxonomy for intrusion-detection systems. Annales des télécommunications, Sayı(7-8), 361-378.
  • Deshpande, P., Sharma, S., Peddoju, S., & Junaid, S. (2018). HIDS: A host based intrusion detection system for cloud computing environment. International Journal of System Assurance Engineering Management, Sayı(3), 567-576. doi: https://doi.org/10.1007/s13198-014-0277-7
  • Du, M., Li, F., Zheng, G., & Srikumar, V. (2017). Deeplog: Anomaly detection and diagnosis from system logs through deep learning. 2017 ACM SIGSAC Conference on Computer and Communications Security Sunulmuş Bildiri.
  • Duessel, P., Gehl, C., Flegel, U., Dietrich, S., & Meier, M. (2017). Detecting zero-day attacks using context-aware anomaly detection at the application-layer. International Journal of Information Security, Sayı(5), 475-490. doi: https://doi.org/10.1007/s10207-016-0344-y
  • Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S. (2002). A Geometric Framework for Unsupervised Anomaly Detection. D. Barbará & S. Jajodia (Eds.), Applications of Data Mining in Computer Security (77-101). Massachusetts, ABD: Springer US. doi: https://doi.org/10.1007/978-1-4615-0953-0_4
  • Eskin, E., Lee, W., & Stolfo, S. J. (2001). Modeling system calls for intrusion detection with dynamic window sizes. DARPA Information Survivability Conference and Exposition II. DISCEX'01 Sunulmuş Bildiri.
  • Feng, L., Guan, X., Guo, S., Gao, Y., & Liu, P. (2004). Predicting the intrusion intentions by observing system call sequences. Computers and Security, Sayı(3), 241-252. doi: https://doi.org/10.1016/j.cose.2004.01.016
  • Forrest, S., Hofmeyr, S. A., Somayaji, A., & Longstaff, T. A. (1996, 6-8 May 1996). A sense of self for Unix processes. 1996 IEEE Symposium on Security and Privacy Sunulmuş Bildiri.
  • Ghosh, A. K., Schwartzbard, A., & Schatz, M. (1999). Learning Program Behavior Profiles for Intrusion Detection. Workshop on Intrusion Detection and Network Monitoring.
  • Grimmer, M., Röhling, M. M., Kricke, M., Franczyk, B., & Rahm, E. (2018). Intrusion Detection on System Call Graphs. 25. DFN-Konferenz "Sicherheit in vernetzten Systemen" Sunulmuş Bildiri, Hamburg, Almanya.
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  • Kim, G., Yi, H., Lee, J., Paek, Y., & Yoon, S. (2016). LSTM-Based System-Call Language Modeling and Robust Ensemble Method for Designing Host-Based Intrusion Detection Systems. arXiv e-prints.
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  • Kruegel, C., Mutz, D., Valeur, F., & Vigna, G. (2003). On the Detection of Anomalous System Call Arguments. Computer Security – ESORICS 2003 Sunulmuş Bildiri, Berlin, Heidelberg.
  • Lanzi, A., Balzarotti, D., Kruegel, C., Christodorescu, M., & Kirda, E. (2010). Accessminer: using system-centric models for malware protection. 17th ACM conference on Computer and communications security Sunulmuş Bildiri.
  • Lee, W., Stolfo, S., & Chan, P. (1997). Learning Patterns from Unix Process Execution Traces for Intrusion Detection. AAAI Workshop on AI Approaches to Fraud Detection and Risk Management.
  • Lee, W., & Xiang, D. (2001). Information-theoretic measures for anomaly detection. IEEE Symposium on Security and Privacy (S&P 2001) Sunulmuş Bildiri, ABD.
  • Leslie, C., Eskin, E., & Noble, W. S. (2001). The spectrum kernel: A string kernel for SVM protein classification. Pacific Symposium on Biocomputing.
  • Liao, Y., & Vemuri, V. R. (2002). Use of k-nearest neighbor classifier for intrusion detection. Computers & Security, Sayı(5), 439-448. doi: https://doi.org/10.1016/s0167-4048(02)00514-x
  • Linux Programmer's Manual. (2017a). Linux man-pages project. Erişim adresi: http://man7.org/linux/man-pages/man2/fork.2.html
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  • Lippmann, R. P., Fried, D. J., Graf, I., Haines, J. W., Kendall, K. R., McClung, D., Weber, D., Webster, S.E., Wyschogrod, D., Cunningham, R. K., Zissman, M.A. (2000). Evaluating intrusion detection systems: The 1998 DARPA off-line intrusion detection evaluation. DARPA Information Survivability Conference and Exposition (DISCEX'00) Sunulmuş Bildiri.
  • Liţă, C. V., Cosovan, D., & Gavriluţ, D. (2018). Anti-emulation trends in modern packers: a survey on the evolution of anti-emulation techniques in UPA packers. Journal of Computer Virology Hacking Techniques, Sayı(2), 107-126. doi: https://doi.org/10.1007/s11416-017-0291-9
  • Liu, A., Jiang, X., Jin, J., Mao, F., & Chen, J. (2011). Enhancing System-Called-Based Intrusion Detection with Protocol Context. IARIA SECURWARE Sunulmuş Bildiri, Fransa.Maggi, F., Matteucci, M., & Zanero, S. (2010). Detecting Intrusions through System Call Sequence and Argument Analysis. IEEE Transactions on Dependable and Secure Computing, Sayı(4), 381-395. doi: https://doi.org/10.1109/tdsc.2008.69
  • Marceau, C. (2000). Characterizing the behavior of a program using multiple-length N-grams. 2000 Workshop on New security paradigms, Ballycotton, County Cork, Ireland.
  • Mouttaqi, T., Rachidi, T., & Assem, N. (2017). Re-evaluation of combined Markov-Bayes models for host intrusion detection on the ADFA dataset. 2017 Intelligent Systems Conference (IntelliSys) Sunulmuş Bildiri.
  • Murtaza, S. S., Khreich, W., Hamou-Lhadj, A., & Couture, M. (2013). A host-based anomaly detection approach by representing system calls as states of kernel modules. 2013 IEEE 24th International Symposium on Software Reliability Engineering (ISSRE) Sunulmuş Bildiri.
  • Mutz, D., Valeur, F., Vigna, G., Kruegel. (2006). Anomalous system call detection. ACM Transactions on Information and System Security (TISSEC), Sayı(1), 61-93. doi: https://doi.org/10.1145/1127345.1127348
  • Nauman, M., Azam, N., & Yao, J. (2016). A three-way decision making approach to malware analysis using probabilistic rough sets. Information Sciences, Sayı, 193-209. doi: https://doi.org/10.1016/j.ins.2016.09.037
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  • Pendleton, M., & Xu, S. A dataset generator for next generation system call host intrusion detection systems. MILCOM 2017 - 2017 IEEE Military Communications Conference (MILCOM) Sunulmuş Bildiri.
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  • Sarmah, A. (2001). Intrusion Detection Systems: Definition, Need and Challenges. SANS Institute Reading Room erişim adresi: https://www.sans.org/reading-room/whitepapers/detection/intrusion-detection-systems-definition-challenges-343
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  • Wagner, D., & Soto, P. (2002). Mimicry attacks on host-based intrusion detection systems. 9th ACM Conference on Computer and Communications Security Sunulmuş Bildiri.
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  • Xie, M., & Hu, J. (2013). Evaluating host-based anomaly detection systems: A preliminary analysis of adfa-ld. 6th International Congress on Image and Signal Processing (CISP) Sunulmuş Bildiri.
  • Xie, M., Hu, J., & Slay, J. (2014). Evaluating host-based anomaly detection systems: Application of the one-class SVM algorithm to ADFA-LD. 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) Sunulmuş Bildiri.
  • Xie, M., Hu, J., Yu, X., & Chang, E. (2015). Evaluating host-based anomaly detection systems: Application of the frequency-based algorithms to adfa-ld. International Conference on Network and System Security Sunulmuş Bildiri.
  • Yao, J., Zhao, S., & Fan, L. (2006). An Enhanced Support Vector Machine Model for Intrusion Detection. International Conference on Rough Sets and Knowledge Technology Sunulmuş Bildiri, Berlin, Almanya.
  • Ye, N., Li, X., Chen, Q., Emran, S. M., & Xu, M. (2001). Probabilistic techniques for intrusion detection based on computer audit data. IEEE Transactions on Systems, Man, Cybernetics-Part A: Systems Humans, Sayı(4), 266-274. doi: https://doi.org/10.1109/3468.935043
  • Yolaçan, E. N., Dy, J. G., & Kaeli, D. R. (2014). System Call Anomaly Detection Using Multi-HMMs. 2014 IEEE Eighth International Conference on Software Security and Reliability-Companion Sunulmuş Bildiri.
  • Ypma, A., & Duin, R. P. (1998). Support objects for domain approximation. International Conference on Artificial Neural Networks Sunulmuş Bildiri.
Toplam 92 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Araştırma Makaleleri
Yazarlar

Kerim Can Kalıpcıoğlu 0000-0003-4885-346X

Cengiz Toğay 0000-0001-5739-1784

Esra Nergis Yolaçan 0000-0002-1655-0993

Yayımlanma Tarihi 15 Aralık 2019
Kabul Tarihi 4 Eylül 2019
Yayımlandığı Sayı Yıl 2019

Kaynak Göster

APA Kalıpcıoğlu, K. C., Toğay, C., & Yolaçan, E. N. (2019). SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 27(3), 199-212. https://doi.org/10.31796/ogummf.560747
AMA Kalıpcıoğlu KC, Toğay C, Yolaçan EN. SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ. ESOGÜ Müh Mim Fak Derg. Aralık 2019;27(3):199-212. doi:10.31796/ogummf.560747
Chicago Kalıpcıoğlu, Kerim Can, Cengiz Toğay, ve Esra Nergis Yolaçan. “SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 27, sy. 3 (Aralık 2019): 199-212. https://doi.org/10.31796/ogummf.560747.
EndNote Kalıpcıoğlu KC, Toğay C, Yolaçan EN (01 Aralık 2019) SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 27 3 199–212.
IEEE K. C. Kalıpcıoğlu, C. Toğay, ve E. N. Yolaçan, “SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ”, ESOGÜ Müh Mim Fak Derg, c. 27, sy. 3, ss. 199–212, 2019, doi: 10.31796/ogummf.560747.
ISNAD Kalıpcıoğlu, Kerim Can vd. “SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 27/3 (Aralık 2019), 199-212. https://doi.org/10.31796/ogummf.560747.
JAMA Kalıpcıoğlu KC, Toğay C, Yolaçan EN. SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ. ESOGÜ Müh Mim Fak Derg. 2019;27:199–212.
MLA Kalıpcıoğlu, Kerim Can vd. “SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, c. 27, sy. 3, 2019, ss. 199-12, doi:10.31796/ogummf.560747.
Vancouver Kalıpcıoğlu KC, Toğay C, Yolaçan EN. SON KULLANICILAR İÇİN ANOMALİ SALDIRI TESPİT SİSTEMLERİ. ESOGÜ Müh Mim Fak Derg. 2019;27(3):199-212.

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