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A REVIEW ON NETWORK BASED DATA LEAKAGE DETECTION AND PREVENTION

Yıl 2019, , 79 - 92, 29.12.2019
https://doi.org/10.33461/uybisbbd.611768

Öz

The main purpose of
information security systems is to take measures against unauthorized data
violations. For this reason, Data Leakage Prevention Systems (DLPS) which can
provide more effective solutions in detecting and preventing leakage of
confidential data in use, in motion or at rest, have been developed.  In this study, context-based, content-based
and content tagging methods that are used -especially with network based Data Leakage
Prevention (DLP) systems- in data leak detection are explained in detail. In
addition, prevention methods are also examined. In conclusion, the challenges
encountered by today's DLP systems have been discussed.

Kaynakça

  • Al-Sanabani, H. (2016). Eklentiler Kullanarak Veri Kaybını Engelleme. (Yüksek Lisans Tezi), Sakarya Üniversitesi, YÖK Ulusal Tez Merkezi.
  • Alneyadi, S., Sithirasenan, E., & Muthukkumarasamy, V. (2015). Detecting data semantic: a data leakage prevention approach. Paper presented at the Trustcom/BigDataSE/ISPA, 2015 IEEE.
  • Alneyadi, S., Sithirasenan, E., & Muthukkumarasamy, V. (2016). A survey on data leakage prevention systems. Journal of Network and Computer Applications, 62, 137-152.
  • Başak, C. D. (2016). Veri Sınıflandırılması ve Hassas Verinin Sızdırılması. Retrieved from https://www.platinbilisim.com.tr/TR/Medya/SiberBulten/siber-bulten-agustos-2016
  • Breitinger, F., & Baggili, I. (2014). File detection on network traffic using approximate matching.
  • Canbay, Y., & Sağıroğlu, Ş. (2016). Veri Kaçağı Tespitinde Yeni Bir Yaklaşım. Savunma Bilimleri Dergisi, 15(1), 149-177. doi:2148-1776
  • Canbay, Y., Yazici, H., & Sagiroglu, S. (2017). A Turkish language based data leakage prevention system. Paper presented at the Digital Forensic and Security (ISDFS), 2017 5th International Symposium on.
  • Cost of a Data Breach Study: Global Overview. (2018). Retrieved from https://databreachcalculator.mybluemix.net/assets/2018_Global_Cost_of_a_Data_Breach_Report.pdf
  • Farrell, C. (2017). Looking Under the Rock: Deployment Strategies for TLS Decryption. (Master), The SANS Institute. Retrieved from https://www.sans.org/reading-room/whitepapers/dlp/paper/38240
  • Global Data Leak Report 2017. Retrieved from https://infowatch.com/report2017
  • Gugelmann, D., Studerus, P., Lenders, V., & Ager, B. (2015). Can content-based data loss prevention solutions prevent data leakage in Web traffic? IEEE Security & Privacy, 13(4), 52-59.
  • Gupta, K., & Kush, A. (2017). A Review on Data Leakage Detection for Secure Communication. International Journal of Engineering and Advanced Technology (IJEAT), 7(1).
  • Gupta, V. (2013). File detection in network traffic using approximate matching. Institutt for telematikk.
  • Hauer, B. (2015). Data and information leakage prevention within the scope of information security. IEEE Access, 3, 2554-2565.
  • Hemalatha.N.C, Somasundaram.R, & Thirugnanam, M. (2016). Privacy Preserving Data Leak Detection in Large Scale Organizations. International Journal of Future Innovative Science and Engineering Research (IJFISER), 2(2). doi:2454- 1966
  • Huang, X., Lu, Y., Li, D., & Ma, M. (2018). A novel mechanism for fast detection of transformed data leakage. IEEE Access, 6, 35926-35936.
  • Katz, G., Elovici, Y., & Shapira, B. (2014). CoBAn: A context based model for data leakage prevention. Information Sciences, 262, 137-158.
  • Kaur, K., Gupta, I., & Singh, A. K. (2017). A Comparative Evaluation of Data Leakage/Loss prevention Systems (DLPS). Paper presented at the Proc. 4th Int. Conf. Computer Science & Information Technology (CS & IT-CSCP), Dubai, UAE.
  • Kleene, S. C. (1951). Representation of events in nerve nets and finite automata. Retrieved from
  • Liu, Y., Corbett, C., Chiang, K., Archibald, R., Mukherjee, B., & Ghosal, D. (2009). SIDD: A framework for detecting sensitive data exfiltration by an insider attack. Paper presented at the 2009 42nd Hawaii International Conference on System Sciences.
  • Matthee, M. H. (2016). Tagging Data to Prevent Data Leakage (Forming Content Repositories). Retrieved from SANS Institute InfoSec Reading Room:
  • Mogull, R., & Securosis, L. (2007). Understanding and selecting a data loss prevention solution. Technicalreport, SANS Institute, 27.
  • Oğuz, B., & Cevahir, H. K. (2010). BT Yönetiminde Bilgi Sızıntısı ve Ağ Tabanlı Çoklu Protokol Bilgi Sızıntısı Engelleme.
  • Pesen, M. M. (2015). DLP’de İçerik Analizi Yöntemleri. Retrieved from https://www.sibergah.com/veri-guvenligi/veri-sizintisi-onleme/dlp-de-icerik-analizi-yontemleri/
  • Ren, L. (2013). DLP Systems: Models, Architecture and Algorithms. Retrieved from https://www.researchgate.net/publication/304080339_DLP_Systems_Models_Architecture_and_Algorithms
  • Securosis, L. (2010). Understanding and Selecting a Data Loss Prevention Solution. Securosis, LLC,[Online]. Available: https://securosis. com/assets/library/reports/DLP-Whitepaper. pdf.
  • Shabtai, A., Elovici, Y., & Rokach, L. (2012). A Survey of Data Leakage Detection and Prevention Solutions. In P. N. Stan Zdonik, Shashi Shekhar (Series Ed.) (pp. 92). doi:10.1007/978-1-4614-2053-8
  • Shapira, Y., Shapira, B., & Shabtai, A. (2013). Content-based data leakage detection using extended fingerprinting. arXiv preprint arXiv:1302.2028.
  • Shu, X., Yao, D., & Bertino, E. (2015). Privacy-preserving detection of sensitive data exposure. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 10(5), 1092-1103.
  • Shu, X., Zhang, J., Yao, D. D., & Feng, W.-C. (2016). Fast detection of transformed data leaks.
  • Soumya, S. R., & Smitha, E. S. (2014). Data Leakage Prevention System By Context Based Keyword Matching And Encrypted Data Detection. International Journal of Advanced Research in Computer Science Engineering and Information Technology, 3(1), 375-384.
  • T.C. Resmi Gazete, Kişisel Verilerin Korunması Kanunu, 6698 C.F.R. (7 Nisan 2016).
  • Tahboub, R., & Saleh, Y. (2014). Data leakage/loss prevention systems (DLP). Paper presented at the Computer Applications and Information Systems (WCCAIS), 2014 World Congress on.
  • Trieu, L. Q., Tran, T.-N., Tran, M.-K., & Tran, M.-T. (2017). Document Sensitivity Classification for Data Leakage Prevention with Twitter-Based Document Embedding and Query Expansion. Paper presented at the 2017 13th International Conference on Computational Intelligence and Security (CIS).

AĞ TABANLI VERİ SIZINTISI TESPİTİ VE ÖNLENMESİ ÜZERİNE BİR İNCELEME

Yıl 2019, , 79 - 92, 29.12.2019
https://doi.org/10.33461/uybisbbd.611768

Öz



Bilgi
güvenliği sistemlerinin temel amacı yetkisiz kişilerce gerçekleştirilen veri
ihlallerine karşı önlem almaktır. Bu sebeple, kullanımda, hareket halinde veya
durağan durumda olan gizli/hassas verilerin sızıntılarının tespiti ve
önlenmesinde daha etkili çözümler sunabilen veri sızıntısı önleme sistemleri
(DLPS-Data Leakage Prevention System) geliştirilmiştir.  Bu çalışma kapsamında, özellikle ağ tabanlı veri
sızıntısı tespit (DLP-Data Leakage Prevention) sistemleri üzerinde durularak
veri sızıntısı tespitinde kullanılan bağlam tabanlı, içerik tabanlı ve içerik
etiketleme yöntemleri detaylı bir şekilde açıklanmıştır. Bunun yanı sıra önleme
yöntemleri de incelenmiştir. Son olarak günümüz DLP sistemlerinin yaygın olarak
karşılaştığı zorluklardan bahsedilmiştir.

Kaynakça

  • Al-Sanabani, H. (2016). Eklentiler Kullanarak Veri Kaybını Engelleme. (Yüksek Lisans Tezi), Sakarya Üniversitesi, YÖK Ulusal Tez Merkezi.
  • Alneyadi, S., Sithirasenan, E., & Muthukkumarasamy, V. (2015). Detecting data semantic: a data leakage prevention approach. Paper presented at the Trustcom/BigDataSE/ISPA, 2015 IEEE.
  • Alneyadi, S., Sithirasenan, E., & Muthukkumarasamy, V. (2016). A survey on data leakage prevention systems. Journal of Network and Computer Applications, 62, 137-152.
  • Başak, C. D. (2016). Veri Sınıflandırılması ve Hassas Verinin Sızdırılması. Retrieved from https://www.platinbilisim.com.tr/TR/Medya/SiberBulten/siber-bulten-agustos-2016
  • Breitinger, F., & Baggili, I. (2014). File detection on network traffic using approximate matching.
  • Canbay, Y., & Sağıroğlu, Ş. (2016). Veri Kaçağı Tespitinde Yeni Bir Yaklaşım. Savunma Bilimleri Dergisi, 15(1), 149-177. doi:2148-1776
  • Canbay, Y., Yazici, H., & Sagiroglu, S. (2017). A Turkish language based data leakage prevention system. Paper presented at the Digital Forensic and Security (ISDFS), 2017 5th International Symposium on.
  • Cost of a Data Breach Study: Global Overview. (2018). Retrieved from https://databreachcalculator.mybluemix.net/assets/2018_Global_Cost_of_a_Data_Breach_Report.pdf
  • Farrell, C. (2017). Looking Under the Rock: Deployment Strategies for TLS Decryption. (Master), The SANS Institute. Retrieved from https://www.sans.org/reading-room/whitepapers/dlp/paper/38240
  • Global Data Leak Report 2017. Retrieved from https://infowatch.com/report2017
  • Gugelmann, D., Studerus, P., Lenders, V., & Ager, B. (2015). Can content-based data loss prevention solutions prevent data leakage in Web traffic? IEEE Security & Privacy, 13(4), 52-59.
  • Gupta, K., & Kush, A. (2017). A Review on Data Leakage Detection for Secure Communication. International Journal of Engineering and Advanced Technology (IJEAT), 7(1).
  • Gupta, V. (2013). File detection in network traffic using approximate matching. Institutt for telematikk.
  • Hauer, B. (2015). Data and information leakage prevention within the scope of information security. IEEE Access, 3, 2554-2565.
  • Hemalatha.N.C, Somasundaram.R, & Thirugnanam, M. (2016). Privacy Preserving Data Leak Detection in Large Scale Organizations. International Journal of Future Innovative Science and Engineering Research (IJFISER), 2(2). doi:2454- 1966
  • Huang, X., Lu, Y., Li, D., & Ma, M. (2018). A novel mechanism for fast detection of transformed data leakage. IEEE Access, 6, 35926-35936.
  • Katz, G., Elovici, Y., & Shapira, B. (2014). CoBAn: A context based model for data leakage prevention. Information Sciences, 262, 137-158.
  • Kaur, K., Gupta, I., & Singh, A. K. (2017). A Comparative Evaluation of Data Leakage/Loss prevention Systems (DLPS). Paper presented at the Proc. 4th Int. Conf. Computer Science & Information Technology (CS & IT-CSCP), Dubai, UAE.
  • Kleene, S. C. (1951). Representation of events in nerve nets and finite automata. Retrieved from
  • Liu, Y., Corbett, C., Chiang, K., Archibald, R., Mukherjee, B., & Ghosal, D. (2009). SIDD: A framework for detecting sensitive data exfiltration by an insider attack. Paper presented at the 2009 42nd Hawaii International Conference on System Sciences.
  • Matthee, M. H. (2016). Tagging Data to Prevent Data Leakage (Forming Content Repositories). Retrieved from SANS Institute InfoSec Reading Room:
  • Mogull, R., & Securosis, L. (2007). Understanding and selecting a data loss prevention solution. Technicalreport, SANS Institute, 27.
  • Oğuz, B., & Cevahir, H. K. (2010). BT Yönetiminde Bilgi Sızıntısı ve Ağ Tabanlı Çoklu Protokol Bilgi Sızıntısı Engelleme.
  • Pesen, M. M. (2015). DLP’de İçerik Analizi Yöntemleri. Retrieved from https://www.sibergah.com/veri-guvenligi/veri-sizintisi-onleme/dlp-de-icerik-analizi-yontemleri/
  • Ren, L. (2013). DLP Systems: Models, Architecture and Algorithms. Retrieved from https://www.researchgate.net/publication/304080339_DLP_Systems_Models_Architecture_and_Algorithms
  • Securosis, L. (2010). Understanding and Selecting a Data Loss Prevention Solution. Securosis, LLC,[Online]. Available: https://securosis. com/assets/library/reports/DLP-Whitepaper. pdf.
  • Shabtai, A., Elovici, Y., & Rokach, L. (2012). A Survey of Data Leakage Detection and Prevention Solutions. In P. N. Stan Zdonik, Shashi Shekhar (Series Ed.) (pp. 92). doi:10.1007/978-1-4614-2053-8
  • Shapira, Y., Shapira, B., & Shabtai, A. (2013). Content-based data leakage detection using extended fingerprinting. arXiv preprint arXiv:1302.2028.
  • Shu, X., Yao, D., & Bertino, E. (2015). Privacy-preserving detection of sensitive data exposure. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 10(5), 1092-1103.
  • Shu, X., Zhang, J., Yao, D. D., & Feng, W.-C. (2016). Fast detection of transformed data leaks.
  • Soumya, S. R., & Smitha, E. S. (2014). Data Leakage Prevention System By Context Based Keyword Matching And Encrypted Data Detection. International Journal of Advanced Research in Computer Science Engineering and Information Technology, 3(1), 375-384.
  • T.C. Resmi Gazete, Kişisel Verilerin Korunması Kanunu, 6698 C.F.R. (7 Nisan 2016).
  • Tahboub, R., & Saleh, Y. (2014). Data leakage/loss prevention systems (DLP). Paper presented at the Computer Applications and Information Systems (WCCAIS), 2014 World Congress on.
  • Trieu, L. Q., Tran, T.-N., Tran, M.-K., & Tran, M.-T. (2017). Document Sensitivity Classification for Data Leakage Prevention with Twitter-Based Document Embedding and Query Expansion. Paper presented at the 2017 13th International Conference on Computational Intelligence and Security (CIS).
Toplam 34 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Cengiz Paşaoğlu 0000-0002-4583-5461

Habibe Güler Bu kişi benim 0000-0003-2607-4801

Masoma Jafari Bu kişi benim 0000-0003-0824-8601

Yayımlanma Tarihi 29 Aralık 2019
Yayımlandığı Sayı Yıl 2019

Kaynak Göster

APA Paşaoğlu, C., Güler, H., & Jafari, M. (2019). AĞ TABANLI VERİ SIZINTISI TESPİTİ VE ÖNLENMESİ ÜZERİNE BİR İNCELEME. International Journal of Management Information Systems and Computer Science, 3(2), 79-92. https://doi.org/10.33461/uybisbbd.611768