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An Ontology Development for Anomaly Detection in File Integration Domain

Yıl 2019, Cilt: 12 Sayı: 3, 239 - 252, 31.07.2019
https://doi.org/10.17671/gazibtd.487373

Öz

Nowadays, there has been an enormous increase in
the variety of data storage and software development technologies. Integration
and diversity in collaborative organizations emerge as a fundamental problem
due to the rapidly evolving and changing technologies. In this context, file
integration comes out as an effective solution in order to integrate data
between different business platforms. Thus, routine business processes and
business logic of different electronic systems could be automated.
Anomaly detection is a data analysis process
that detects abnormal situations in systems. Anomaly detection provides an
awareness for the unexpected situations in information based systems and the
fulfillment of necessary actions against anomalies that do not comply with the
expected behavior. Therefore, anomaly detection is an important data analysis
process to detect anomalies that occur in file integrations. In this study, an
ontology based approach is presented in order to detect anomalies in file
integration systems. Anomaly detection in file integrations is important in
terms of availability which is one of the component of information security triad
(confidentiality, integrity, availability). Most of the anomalies in integrations
are oriented to data integrity and these anomalies can be detected from the
transfer time or the incoming file size. In the proposed ontological approach,
the file integrations made to a sample system are being queried and anomalies that
occur in the integration processes are being detected. The proposed approach is
intended to provide an ontology-based solution to data integrity and
availability (anomalies that can stop the file flow) in the file integration
systems
.

Kaynakça

  • V. Chandola, A. Banerjee, V. Kumar, “Anomaly Detection : A Survey”, ACM Computing Surveys (CSUR), 41(3), Article No 15, 2009.
  • A. H. Hamamoto, L. F. Carvalho, L. D. H. Sampaio, T. Abrão, M. L. Proença Jr., “Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic”, Expert Systems with Applications, 92(C), 390-402, 2018.
  • M. Ahmeda, A. N. Mahmooda, Md. R. Islam, “A survey of anomaly detection techniques in financial domain”, Future Generation Computer Systems, 55(C), 278-288, 2016.
  • M. Hauskrecht, M. Valko, B. Kveton, S. Visweswaran G. F. Cooper, “Evidence-based Anomaly Detection in Clinical Domains”, AMIA Annual Symposium Proceedings/AMIA Symposium, 319-323, 2017.
  • Internet: W3C, Extensible Markup Language (XML), https://www.w3.org/XML/ , 23.11.2018.
  • Internet: F. Arnaboldi, OWASP - XML Security Cheat Sheet, , https://www.owasp.org/index.php/XML_Security_Cheat_Sheet, 23.11.2018.
  • İ. Üzüm, Ö. Can, “An anomaly detection approach for enterprise file integration”, 6th International Symposium on Digital Forensic and Security (ISDFS 2018), Antalya, Turkey, March 22-25, 2018.
  • İ. Üzüm, Ö. Can, “An anomaly detection system proposal to ensure information security for file integrations”, 26th Signal Processing and Communications Applications Conference (SIU 2018), Izmir, Turkey, 1-4, 2-5 May, 2018.
  • Ö. Can, M. Ünalır, “Ontoloji Tabanlı Bilgi Sistemlerinde Politika Yönetimi”, Bilişim Teknolojileri Dergisi, 3(2), 1-16, 2010.
  • Ö. Gümüş, Ö. Gürcan, O. Dikenelli, “Anlamsal Servis Aracılığı İçin Bir Çok Etmenli Sistem ve Aracılık Etkileşim Protokolü”, Bilişim Teknolojileri Dergisi, 5(2), 9-24, 2012.
  • Ö. Öztürk, “Petrol, Gaz ve Madencilik Endüstrisinde Bilgi Gösterimi için Ontoloji Temelli bir Yaklaşım”, Bilişim Teknolojileri Dergisi, 12(2), 147-158, 2019.
  • F. Abdoli, M. Kahani, “Ontology Based Distributed Intrusion Detection System”, In 14th International CSI Computer Conference, Tehran, Iran, 65-70, 20-21 Oct., 2009.
  • C. Hsieh, R. Chen, Y. Huang, “Applying an Ontology to a Patrol Intrusion Detection System for Wireless Sensor Networks”, International Journal of Distributed Sensor Networks, 10(1), doi: 10.1155/2014/634748, 2014.
  • S. Hung, D. S. Liu, “A user-oriented ontology-based approach for network intrusion detection”, Computer Standards & Interfaces, 78-88, 2008.
  • O. Can, O., M. O. Unalir, E. Sezer, O. Bursa, B. Erdogdu, “An Ontology Based Approach For Host Intrusion Detection Systems”, In: 11th International Conference on Metadata and Semantic Research (MTSR 2017), Garoufallou E., Virkus S., Siatri R., Koutsomiha D. (eds), Communications in Computer and Information Science, Springer, Cham, Tallinn, Estonia, 755, 80-86, November 28 – December 1, 2017.
  • G. Kolaczek, K.Juszczyszyn, “Attack pattern analysis framework for multiagent intrusion detection system”, International Journal Of Computational Intelligence Systems, 1(3), 215-224, 2008.
  • H. A. Karande, S. S. Gupta, S., S., “Ontology based Intrusion Detection System for Web Application Security”, In: International Conference On Communication Networks (lCCN), IEEE, Gwalior, India, 228-232, 19-21 November, 2015.
  • E. Pardo, D. Espes, P. Le-Parc, “A Framework for Anomaly Diagnosis in Smart Homes Based on Ontology”, Procedia Computer Science, 83, 80-86, 2016.
  • J. Raad, W. Beek, F. van Harmelen, N. Pernelle, F. Sais, “Detecting Erroneous Identity Links on the Web Using Network Metrics”, In: International Semantic Web Conference (ISWC), Springer, Cham, 11136, 391-407, 2018.
  • R. F. Cordova, A. L. Marcovich, C. A. Santivanez, “An Efficient Method for Ontology-Based Multi-Vendor Firewall Misconfiguration Detection: A Real-Case Study”, In: IEEE ANDESCON, IEEE, Santiago de Cali, Colombia, 1-3, 2018.
  • R. Sarno, F. P. Sinaga, “Business process anomaly detection using ontology-based process modelling and Multi-Level Class Association Rule Learning”, In: International Conference on Computer, Control, Informatics and its Applications (IC3INA), IEEE, Bandung, 12-17, 2015.
  • E. Ben-Abdallah, K. Boukadi, M. Hammami, “Spam Detection Approach for Cloud Service Reviews Based on Probabilistic Ontology”, In: OTM Confederated International Conferences "On the Move to Meaningful Internet Systems", Springer, Cham, 11229, 534-551, 2018.
  • A. Maurya, K. Murray, Y. Liu, C. Dyer, W. W. Cohen, D. B. Neill, “Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams”, Information Retrieval, Cornell University, doi: 10.1145/1235, 2016.
  • M. Riga, E. Kontopoulos, K. Karatzas, S. Vrochidis, I. Kompatsiaris, “An Ontology-Based Decision Support Framework for Personalized Quality of Life Recommendations”, In: Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support (ICDSST 2018), Lecture Notes in Business Information Processing, 313, 38-51, 2018.
  • S. Ishizu, A. Gehrmann, J. Minegishi, Y. Nagai, “Ontology-Driven Decision Support Systems For Management System Audit”, In: Proceedings of the 52nd Annual Meeting of the ISSS - 2008, Madison, Wisconsin, 2008.
  • M. Rospocher, L. Serafini L., “An Ontological Framework for Decision Support”, In: Joint International Semantic Technology Conference-Semantic Technology (JIST 2012), Lecture Notes in Computer Science, 7774, 239-254, 2013.
  • [27] A. Galopina, J. Bouaude, S. Pereira, B. Seroussi, “An Ontology-Based Clinical Decision Support System for the Management of Patientswith Multiple Chronic Disorders”, Stud Health Technol Inform., 216-275, 2015.
  • P. C. Sherimon, R. Krishnan, Arabian Journal for Science and Engineering, 41(3), 1145–1160, 2016.
  • M. Alkahtani, A. Choudhary, A. De, J. A. Harding, “A decision support system based on ontology and data mining to improve design using warranty data”, Computers & Industrial Engineering, 128, 1027–1039, 2019.
  • T. Berners-Lee, J. Hendler, O. Lassila, “The Semantic Web”, Scientific American, 284(5), 28-37, 2001.
  • Internet: N. F. Noy, D. L. McGuiness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford University, Stanford, CA, 25p., https://protege.stanford.edu/publications/ontology_ development/ontology101.pdf.
  • Internet: M. S. Fox, Enterprise Integration Laboratory, TOVE Ontologies, http://www.eil.utoronto.ca/theory/enterprise-modelling/ tove/, 23.11.2018.
  • Internet: Stanford University, Protégé Ontology Editor, https://protege.stanford.edu/, 23.11.2018.
  • Internet: World Wide Web Consortium, SPARQL Query Language for RDF, W3C Recommendation 15 January 2008, https://www.w3.org/TR/rdf-sparql-query/, 23.11.2018.
  • S. Agrawal, J. Agrawal, “A Survey on Anomaly Detection using Data Mining Techniques”, In: 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems , Elsevier B. V., 60, 708-713, 2015.
  • S. Ahmad, A. N. Mahmood, J. Hu, “A Survey of Network Anomaly Detection Techniques”, Journal of Network and Computer Applications, 60, 19-31, 2015.
  • Internet: Apache Jena, A free and open source Java framework for building Semantic Web and Linked Data applications, https://jena.apache.org, 23.11.2018.

Dosya Entegrasyonu Etki Alanında Anomali Tespiti İçin Bir Ontoloji Geliştirimi

Yıl 2019, Cilt: 12 Sayı: 3, 239 - 252, 31.07.2019
https://doi.org/10.17671/gazibtd.487373

Öz

Günümüzde, veri depolama ve yazılım geliştirme
teknolojilerinin çeşitliliğinde büyük bir artış yaşanmıştır. Hızla gelişen ve
değişen teknolojiler sebebiyle, ortak çalışan organizasyonlardaki entegrasyon
ve çok çeşitlilik, temel bir sorun olarak ortaya çıkmaktadır. Bu kapsamda dosya
entegrasyonları, farklı iş platformları arasındaki veri bütünleştirmesine
yardımcı olan etkili bir çözüm olarak sunulmaktadır. Böylelikle, farklı
elektronik sistemler arasındaki rutin iş süreçleri ve iş mantıkları otomatize edilebilmektedir.
Anomali tespiti, sistemlerde meydana gelebilecek
anormal durumları tespit eden bir veri analiz işlemidir. Anomali tespiti, bilgi
tabanlı sistemlerde beklenmedik durumlara karşı farkındalık ve beklenen
davranışa uymayan anomaliler karşısında gerekli eylemlerin yerine getirilmesini
sağlamaktadır. Bu nedenle, anomali tespiti dosya entegrasyonlarında meydana
gelen anomalilerin tespiti için önemli bir veri analizi işlemidir. Bu çalışma kapsamında,
dosya entegrasyonu sistemlerinde gerçekleşen anomalileri tespit edebilmek için ontoloji
tabanlı bir yaklaşım sunulmaktadır. Dosya entegrasyonlarında anormalliklerin
tespiti, bilgi güvenliği üçlüsünden (gizlilik, bütünlük ve kullanılabilirlik) biri
olan kullanılabilirlik açısından önemlidir. Entegrasyonlardaki anomalilerin
büyük bir kısmı veri bütünlüğüne yöneliktir ve bu anomaliler transfer
süresinden ya da gelen dosya boyutundan tespit edilerek yakalanabilmektedir.
Önerilen ontolojik yaklaşımda, örnek bir sisteme yapılan dosya entegrasyonları sorgulanarak
entegrasyon işlemlerinde meydana gelen anomaliler tespit edilebilmektedir.
Önerilen yaklaşımın, dosya entegrasyon sistemlerinde veri bütünlüğüne ve
kullanılabilirliğe (dosya akışını durdurabilecek anomaliler) yönelik anormal
durumlara karşı ontoloji bazlı bir çözüm sunması amaçlanmaktadır.

Kaynakça

  • V. Chandola, A. Banerjee, V. Kumar, “Anomaly Detection : A Survey”, ACM Computing Surveys (CSUR), 41(3), Article No 15, 2009.
  • A. H. Hamamoto, L. F. Carvalho, L. D. H. Sampaio, T. Abrão, M. L. Proença Jr., “Network Anomaly Detection System using Genetic Algorithm and Fuzzy Logic”, Expert Systems with Applications, 92(C), 390-402, 2018.
  • M. Ahmeda, A. N. Mahmooda, Md. R. Islam, “A survey of anomaly detection techniques in financial domain”, Future Generation Computer Systems, 55(C), 278-288, 2016.
  • M. Hauskrecht, M. Valko, B. Kveton, S. Visweswaran G. F. Cooper, “Evidence-based Anomaly Detection in Clinical Domains”, AMIA Annual Symposium Proceedings/AMIA Symposium, 319-323, 2017.
  • Internet: W3C, Extensible Markup Language (XML), https://www.w3.org/XML/ , 23.11.2018.
  • Internet: F. Arnaboldi, OWASP - XML Security Cheat Sheet, , https://www.owasp.org/index.php/XML_Security_Cheat_Sheet, 23.11.2018.
  • İ. Üzüm, Ö. Can, “An anomaly detection approach for enterprise file integration”, 6th International Symposium on Digital Forensic and Security (ISDFS 2018), Antalya, Turkey, March 22-25, 2018.
  • İ. Üzüm, Ö. Can, “An anomaly detection system proposal to ensure information security for file integrations”, 26th Signal Processing and Communications Applications Conference (SIU 2018), Izmir, Turkey, 1-4, 2-5 May, 2018.
  • Ö. Can, M. Ünalır, “Ontoloji Tabanlı Bilgi Sistemlerinde Politika Yönetimi”, Bilişim Teknolojileri Dergisi, 3(2), 1-16, 2010.
  • Ö. Gümüş, Ö. Gürcan, O. Dikenelli, “Anlamsal Servis Aracılığı İçin Bir Çok Etmenli Sistem ve Aracılık Etkileşim Protokolü”, Bilişim Teknolojileri Dergisi, 5(2), 9-24, 2012.
  • Ö. Öztürk, “Petrol, Gaz ve Madencilik Endüstrisinde Bilgi Gösterimi için Ontoloji Temelli bir Yaklaşım”, Bilişim Teknolojileri Dergisi, 12(2), 147-158, 2019.
  • F. Abdoli, M. Kahani, “Ontology Based Distributed Intrusion Detection System”, In 14th International CSI Computer Conference, Tehran, Iran, 65-70, 20-21 Oct., 2009.
  • C. Hsieh, R. Chen, Y. Huang, “Applying an Ontology to a Patrol Intrusion Detection System for Wireless Sensor Networks”, International Journal of Distributed Sensor Networks, 10(1), doi: 10.1155/2014/634748, 2014.
  • S. Hung, D. S. Liu, “A user-oriented ontology-based approach for network intrusion detection”, Computer Standards & Interfaces, 78-88, 2008.
  • O. Can, O., M. O. Unalir, E. Sezer, O. Bursa, B. Erdogdu, “An Ontology Based Approach For Host Intrusion Detection Systems”, In: 11th International Conference on Metadata and Semantic Research (MTSR 2017), Garoufallou E., Virkus S., Siatri R., Koutsomiha D. (eds), Communications in Computer and Information Science, Springer, Cham, Tallinn, Estonia, 755, 80-86, November 28 – December 1, 2017.
  • G. Kolaczek, K.Juszczyszyn, “Attack pattern analysis framework for multiagent intrusion detection system”, International Journal Of Computational Intelligence Systems, 1(3), 215-224, 2008.
  • H. A. Karande, S. S. Gupta, S., S., “Ontology based Intrusion Detection System for Web Application Security”, In: International Conference On Communication Networks (lCCN), IEEE, Gwalior, India, 228-232, 19-21 November, 2015.
  • E. Pardo, D. Espes, P. Le-Parc, “A Framework for Anomaly Diagnosis in Smart Homes Based on Ontology”, Procedia Computer Science, 83, 80-86, 2016.
  • J. Raad, W. Beek, F. van Harmelen, N. Pernelle, F. Sais, “Detecting Erroneous Identity Links on the Web Using Network Metrics”, In: International Semantic Web Conference (ISWC), Springer, Cham, 11136, 391-407, 2018.
  • R. F. Cordova, A. L. Marcovich, C. A. Santivanez, “An Efficient Method for Ontology-Based Multi-Vendor Firewall Misconfiguration Detection: A Real-Case Study”, In: IEEE ANDESCON, IEEE, Santiago de Cali, Colombia, 1-3, 2018.
  • R. Sarno, F. P. Sinaga, “Business process anomaly detection using ontology-based process modelling and Multi-Level Class Association Rule Learning”, In: International Conference on Computer, Control, Informatics and its Applications (IC3INA), IEEE, Bandung, 12-17, 2015.
  • E. Ben-Abdallah, K. Boukadi, M. Hammami, “Spam Detection Approach for Cloud Service Reviews Based on Probabilistic Ontology”, In: OTM Confederated International Conferences "On the Move to Meaningful Internet Systems", Springer, Cham, 11229, 534-551, 2018.
  • A. Maurya, K. Murray, Y. Liu, C. Dyer, W. W. Cohen, D. B. Neill, “Semantic Scan: Detecting Subtle, Spatially Localized Events in Text Streams”, Information Retrieval, Cornell University, doi: 10.1145/1235, 2016.
  • M. Riga, E. Kontopoulos, K. Karatzas, S. Vrochidis, I. Kompatsiaris, “An Ontology-Based Decision Support Framework for Personalized Quality of Life Recommendations”, In: Decision Support Systems VIII: Sustainable Data-Driven and Evidence-Based Decision Support (ICDSST 2018), Lecture Notes in Business Information Processing, 313, 38-51, 2018.
  • S. Ishizu, A. Gehrmann, J. Minegishi, Y. Nagai, “Ontology-Driven Decision Support Systems For Management System Audit”, In: Proceedings of the 52nd Annual Meeting of the ISSS - 2008, Madison, Wisconsin, 2008.
  • M. Rospocher, L. Serafini L., “An Ontological Framework for Decision Support”, In: Joint International Semantic Technology Conference-Semantic Technology (JIST 2012), Lecture Notes in Computer Science, 7774, 239-254, 2013.
  • [27] A. Galopina, J. Bouaude, S. Pereira, B. Seroussi, “An Ontology-Based Clinical Decision Support System for the Management of Patientswith Multiple Chronic Disorders”, Stud Health Technol Inform., 216-275, 2015.
  • P. C. Sherimon, R. Krishnan, Arabian Journal for Science and Engineering, 41(3), 1145–1160, 2016.
  • M. Alkahtani, A. Choudhary, A. De, J. A. Harding, “A decision support system based on ontology and data mining to improve design using warranty data”, Computers & Industrial Engineering, 128, 1027–1039, 2019.
  • T. Berners-Lee, J. Hendler, O. Lassila, “The Semantic Web”, Scientific American, 284(5), 28-37, 2001.
  • Internet: N. F. Noy, D. L. McGuiness, Ontology Development 101: A Guide to Creating Your First Ontology, Stanford University, Stanford, CA, 25p., https://protege.stanford.edu/publications/ontology_ development/ontology101.pdf.
  • Internet: M. S. Fox, Enterprise Integration Laboratory, TOVE Ontologies, http://www.eil.utoronto.ca/theory/enterprise-modelling/ tove/, 23.11.2018.
  • Internet: Stanford University, Protégé Ontology Editor, https://protege.stanford.edu/, 23.11.2018.
  • Internet: World Wide Web Consortium, SPARQL Query Language for RDF, W3C Recommendation 15 January 2008, https://www.w3.org/TR/rdf-sparql-query/, 23.11.2018.
  • S. Agrawal, J. Agrawal, “A Survey on Anomaly Detection using Data Mining Techniques”, In: 19th International Conference on Knowledge Based and Intelligent Information and Engineering Systems , Elsevier B. V., 60, 708-713, 2015.
  • S. Ahmad, A. N. Mahmood, J. Hu, “A Survey of Network Anomaly Detection Techniques”, Journal of Network and Computer Applications, 60, 19-31, 2015.
  • Internet: Apache Jena, A free and open source Java framework for building Semantic Web and Linked Data applications, https://jena.apache.org, 23.11.2018.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Bilgisayar Yazılımı
Bölüm Makaleler
Yazarlar

Özgü Can 0000-0002-8064-2905

Murat Osman Ünalır

İbrahim Üzüm Bu kişi benim

Yayımlanma Tarihi 31 Temmuz 2019
Gönderilme Tarihi 25 Kasım 2018
Yayımlandığı Sayı Yıl 2019 Cilt: 12 Sayı: 3

Kaynak Göster

APA Can, Ö., Ünalır, M. O., & Üzüm, İ. (2019). Dosya Entegrasyonu Etki Alanında Anomali Tespiti İçin Bir Ontoloji Geliştirimi. Bilişim Teknolojileri Dergisi, 12(3), 239-252. https://doi.org/10.17671/gazibtd.487373