A Description Logic Ontology for Email Phishing
Year 2020,
Volume: 9 Issue: 1, 44 - 63, 01.03.2020
Franklin Tchakounté
Djeguedem Molengar
Justin Moskolai Ngossaha
Abstract
Phishing detection is an area of identifying malicious activities designed by phishers to lure users providing sensitive information. Existing anti-phishing systems use blacklists based on specific parameters, characterize attacker’s activities with artificial and computational approaches and educate users. The development and maintenance of these systems is hard and costly because of the polymorphic nature of phishing techniques. Phishing attacks are able to scam humans with insufficient knowledge, while countermeasures focus on specific characteristics to make decisions. Defining formal approaches for representing and reasoning knowledge in anti-phishing systems is therefore a concern. This work deals with this issue by proposing formalized description logic to build the knowledge base of phishing attacks. It additionally designs an ontology-oriented approach to add semantics on that knowledge. The ontology model has been proven consistent and satisfiable. Experimentations on case studies demonstrate the ability of the proposed model to represent knowledge attack scenarios. A comparison with state-of-the-art researches shows that the proposed formalism is more adequate to characterize phishing semantics. This work could successfully complement anti-phishing systems.
References
- [1] A. Patel and S. Jain. “Formalisms of
Representing Knowledge”. Procedia Comput.
Sci., vol. 125, pages 542–549, 2018, doi:
10.1016/J.PROCS.2017.12.070.
- [2] V. Nazaruks and J. Osis. “A Survey on
Domain Knowledge Representation with
Frames”. Proceedings of International
Conference on Evaluation of Novel
Approaches to Software Engineering
(ENASE), pages 346–354, 2017.
- [3] B. Nebel. “Logics for Knowledge
Representation”. Int. Encycl. Soc. Behav.
Sci., pages 319–321, 2015, doi:
10.1016/B978-0-08-097086-8.43053-9.
- [4] F. Baader, D. Calvanese, D. L. McGuinness,
D. Nardi, and P. F. Patel-Schneider. “The
Description Logic Handbook: Theory,
Implementation and Applications, 2nd ed”.
Cambridge University Press, 2010.
- [5] M. N. Asim, M. Wasim, M. U. G. Khan, W.
Mahmood, and H. M. Abbasi. “A Survey of
Ontology Learning Techniques and
Applications”. Database, vol. 2018, 2018,
doi: 10.1093/database/bay101.
- [6] D. Goel and A. K. Jain. “Mobile phishing
attacks and defence mechanisms: State of art
and open research challenges”. Comput.
Secur., vol. 73, pages 519–544, 2018, doi:
10.1016/j.cose.2017.12.006.
- [7] APWG. “Phishing Activity Trends Report 4th
Quarter 2018”. Report, 2019.
- [8] M. Nicho, H. Fakhry, and U. Egbue. “When
Spear Phishers Craft Contextually Convincing
Emails”. Proceedings of International
Conferences on WWW/Internet and Applied
Computing, 2018.
- [9] F. Salahdine, N. Kaabouch, F. Salahdine, and
N. Kaabouch. “Social Engineering Attacks: A
Survey”. Futur. Internet, 11( 4), p. 89, 2019,
doi: 10.3390/fi11040089.
- [10] K. L. Chiew, K. S. C. Yong, and C. L. Tan.
“A Survey of Phishing Attacks: Their Types,
Vectors and Technical Approaches”. Expert
Syst. Appl., vol. 106, pages 1–20, 2018, doi:
10.1016/J.ESWA.2018.03.050.
- [11] A. Aleroud and L. Zhou. “Phishing
Environments, Techniques, and
Countermeasures: A Survey”. Comput.
Secur., vol. 68, pages 160–196, 2017, doi:
10.1016/J.COSE.2017.04.006.
- [12] R. S. Rao and A. R. Pais. “Two level filtering
mechanism to detect phishing sites using
lightweight visual similarity approach”. J.
Ambient Intell. Humaniz. Comput., 2019, doi:
10.1007/s12652-019-01637-z.
- [13] K. L. Chiew, C. L. Tan, K. S. Wong, K. S. C.
Yong, and W. K. Tiong. “A new hybrid
ensemble feature selection framework for
machine learning-based phishing detection
system”. Inf. Sci. (Ny)., vol. 484, pages 153–
166, 2019, doi: 10.1016/j.ins.2019.01.064.
- [14] R. S. Rao and A. R. Pais. “Jail-Phish: An
improved search engine based phishing
detection system”. Comput. Secur., vol. 83,
pages 246–267, 2019, doi:
10.1016/j.cose.2019.02.011.
- [15] M. Volkamer, K. Renaud, B. Reinheimer, and
A. Kunz. “User experiences of TORPEDO:
TOoltip-poweRed Phishing Email
DetectiOn”. Comput. Secur., vol. 71, pages
100–113, 2017, doi:
10.1016/j.cose.2017.02.004.
- [16] S. W. Liew, N. F. M. Sani, M. T. Abdullah,
R. Yaakob, and M. Y. Sharum. “An effective
security alert mechanism for real-time
phishing tweet detection on Twitter”.
Comput. Secur., vol. 83, pages 201–207,
2019, doi: 10.1016/j.cose.2019.02.004.
- [17] D. Delgado-Gómez, J. C. Laria, and D. RuizHernández. “Computerized adaptive test and
decision trees: A unifying approach”. Expert
Syst. Appl., vol. 117, pages 358–366, 2019,
doi: 10.1016/j.eswa.2018.09.052.
- [18] T. Nagunwa, S. Naqvi, S. Fouad, and H.
Shah. “A Framework of New Hybrid Features
for Intelligent Detection of Zero Hour
Phishing Websites”. Advances in Intelligent
Systems and Computing, 2020, vol. 951,
pages 36–46, doi: 10.1007/978-3-030-20005-
3_4.
- [19] O. K. Sahingoz, E. Buber, O. Demir, and B.
Diri. “Machine learning based phishing
detection from URLs”. Expert Syst. Appl.,
vol. 117, pages 345–357, 2019, doi:
10.1016/j.eswa.2018.09.029.
- [20] V. Patil, P. Thakkar, C. Shah, T. Bhat, and S.
P. Godse. “Detection and Prevention of
Phishing Websites Using Machine Learning
Approach”. Proceedings of the 4th
International Conference on Computing,
Communication Control and Automation,
ICCUBEA 2018, 2018, doi:
10.1109/ICCUBEA.2018.8697412.
- [21] N. A. G. Arachchilage and S. Love. “A Game
Design Framework for Avoiding Phishing
Attacks”. Comput. Human Behav., 29(3),
pages 706–714, 2013, doi:
10.1016/J.CHB.2012.12.018.
- [22] N. A. G. Arachchilage and S. Love. “Security
Awareness of Computer Users: A Phishing
Threat Avoidance Perspective”. Comput.
Human Behav., vol. 38, pages 304–312, 2014, doi: 10.1016/J.CHB.2014.05.046.
- [23] N. A. G. Arachchilage and M. Cole.
“Designing a Mobile Game for Home
Computer Users to Protect Against Phishing
Attacks”. arXiv preprint arXiv:1602.03929,
2016
- [24] N. A. G. Arachchilage and S. Love. “A game
design framework for avoiding phishing
attacks”. Comput. Human Behav., 29(3),
pages 706–714, 2013, doi:
10.1016/j.chb.2012.12.018.
- [25] S.-S. Tseng, C.-H. Ku, T.-J. Lee, G.-G. Geng,
and Y.-J. Wang. “Building a Frame-Based
Anti-Phishing Model based on Phishing
Ontology”. Proceedings of International
Conference on Advances in Information
Technology, 2013.
- [26] M. Bazarganigilani. “Phishing E-Mail
Detection Using Ontology Concept and Naïve
Bayes Algorithm”. Int. J. Res. Rev. Comput.
Sci., 2(2), 2011.
- [27] M. S. Qaseem and A. Govardhan. “Phishing
Detection in IMs using Domain Ontology and
CBA - An innovative Rule Generation
Approach”. ArXiv preprint arXiv:1412.3056,
2014.
- [28] K. Kerremans, Y. Tang, R. Temmerman, and
G. Zhao. “Towards Ontology-based E-mail
Fraud Detection”. Proceedings of the 2005
Purtuguese Conference on Artificial
Intelligence, 2005, pages 106–111, doi:
10.1109/EPIA.2005.341275.
- [29] G. Park. “Towards Ontology-Based Phishing
Detection”. Purdue University, 2018.
- [30] Vamsee Krishna Kiran Muppavarapu,
Ramesh Gowtham, and Archanaa Rajendran.
“An RDF based Anti-Phishing Framework”.
Int. Assoc. Sci. Innov. Res., 1(9), pages 1–10,
2013.
- [31] C. Falk. “Knowledge Modeling of Phishing
Emails”. Open Access Diss., Aug. 2016.
- [32] J. Zhang, Q. Li, Q. Wang, T. Geng, X.
Ouyang, and Y. Xin. “Parsing and Detecting
Phishing Pages Based on Semantic
Understanding of Text”. J. Inf. Comput. Sci.,
9(6), pages 1521–1534, 2012.
- [33] A. S. Bozkir and E. A. Sezer. “Use of HOG
Descriptors in Phishing Detection”.
Proceedings of the 2016 4th International
Symposium on Digital Forensic and Security
(ISDFS), 2016, pages 148–153, doi:
10.1109/ISDFS.2016.7473534.
- [34] A. Oest, Y. Safaei, A. Doupé, G.-J. Ahn, B.
Wardman, and K. Tyers. “PhishFarm: A
Scalable Framework for Measuring the
Effectiveness of Evasion Techniques Against
Browser Phishing Blacklists”. Proceedings of
the 2019 IEEE Symposium on Security and
Privacy (SP), 2019, pages 764–781, doi:
10.1109/SP.2019.00049.
- [35] N. Virvilis, A. Mylonas, N. Tsalis, and D.
Gritzalis. “Security Busters: Web Browser
Security vs. Rogue Sites”. Comput. Secur.,
vol. 52, pages 90–105, 2015, doi:
10.1016/J.COSE.2015.04.009.
- [36] N. Tsalis, N. Virvilis, A. Mylonas, T.
Apostolopoulos, and D. Gritzalis. “Browser
Blacklists: The Utopia of Phishing
Protection”. Springer, pages 278–293, 2015.
- [37] L. F. Sikos. “Description Logics: Formal
Foundation for Web Ontology Engineering”.
in Description Logics in Multimedia
Reasoning, Cham: Springer International
Publishing, pages 67–120, 2017
- [38] D. Ellison, A. R. Ikuesan, and H. Venter.
“Description Logics and Axiom Formation
for a Digital Forensics Ontology”.
Proceedings of the European Conference on
Cyber Warfare and Security, pages 742–751,
2019
- [39] N. Scarpato, N. D. Cilia, and M. Romano.
“Reachability Matrix Ontology: A
Cybersecurity Ontology”. Appl. Artif. Intell.,
33(7), pages 643–655, 2019, doi:
10.1080/08839514.2019.1592344.
- [40] G. Park and J. Rayz. “Ontological Detection
of Phishing Emails”. Proceedings of the 2018
IEEE International Conference on Systems,
Man, and Cybernetics (SMC), pages 2858–
2863, 2018, doi: 10.1109/SMC.2018.00486.
- [41] M. Benedek, Y. N. Kenett, K. Umdasch, D.
Anaki, M. Faust, and A. C. Neubauer. “How
semantic memory structure and intelligence
contribute to creative thought: a network
science approach”. Think. Reason., 23(2),
pages 158–183, Apr. 2017, doi:
10.1080/13546783.2016.1278034.
- [42] P. Di Maio and M. C. Suárez-Figueroa.
“Introduction to the Special Issue ‘Artificial
Intelligence Knowledge Representation’”.
Systems, 7(3), p. 35, Jul. 2019, doi:
10.3390/systems7030035.
- [43] A. Patel and S. Jain. “Formalisms of
Representing Knowledge,” in Procedia
Computer Science, 2018, vol. 125, pages
542–549, doi: 10.1016/j.procs.2017.12.070.
- [44] G. Jakus, V. Milutinović, S. Omerović, and S.
Tomažič. “Concepts, Ontologies, and
Knowledge Representation”. Springer, 2013.
- [45] V. Varga, C. Săcărea, and A. E. Molnar.
“Conceptual Graphs Based Modeling of
Semi-structured Data”. Lecture Notes in
Computer Science (including subseries
Lecture Notes in Artificial Intelligence and
Lecture Notes in Bioinformatics), 2018, vol.
10872 LNAI, pages 167–175, doi:
10.1007/978-3-319-91379-7_13.
- [46] R. J. Brachman, “What’s in a concept:
structural foundations for semantic networks”.
Int. J. Man. Mach. Stud., 9(2), pages 127–
152, Mar. 1977, doi: 10.1016/S0020-
7373(77)80017-5.
- [47] R. Zakeri, R. Jalili, H. R. Shahriari, and H.
Abolhassani, “Using Description Logics for
Network Vulnerability Analysis”.
Proceedings of International Conference on
Networking, International Conference on
Systems and International Conference on
Mobile Communications and Learning
Technologies (ICNICONSMCL’06), pages
78–78, doi:
10.1109/ICNICONSMCL.2006.222.
- [48] W. Yan, E. Hou, and N. Ansari. “Description
logics for an autonomic IDS event analysis
system”. Comput. Commun., 29(15), pages
2841–2852, 2006, doi:
10.1016/j.comcom.2005.10.038.
- [49] T. Takahashi and Y. Kadobayashi.
“Reference Ontology for Cybersecurity
Operational Information”. Comput. J., 58(10),
pages 2297–2312, 2015, doi:
10.1093/comjnl/bxu101.
- [50] M. Krötzsch, F. Simančík, and I. Horrocks.
“A Description Logic Primer *”. 2013.
- [51] F. Baader, I. Horrocks, C. Lutz, and U.
Sattler. “An Introduction to Description
Logic”. Cambridge University Press, 2017.
- [52] H. S. Shin. “Reasoning processes in clinical
reasoning: from the perspective of cognitive
psychology”. Korean J. Med. Educ., 31(4),
pages 299–308, 2019, doi:
10.3946/kjme.2019.140.
- [53] C. Lutz, U. Sattler, C. Tinelli, A.-Y. Turhan,
and F. Wolter, Eds. “Description Logic,
Theory Combination, and All That”. Springer
International Publishing, 2019.
- [54] O. Curé and G. Blin. “Reasoning”. RDF
Database Systems, Morgan Kaufmann, 2015,
pages 191–222.
- [55] D. Allemang and J. Hendler. “Semantic Web
for the Working Ontologist”. Elsevier, 2011.
- [56] C. Thomas. “Ontology in Information
Science”. InTech, 2018.
- [57] K. Munir and M. Sheraz Anjum. “The use of ontologies for effective knowledge modelling
and information retrieval”. Applied
Computing and Informatics, 14(2), pages
116–126, 2018, doi:
10.1016/j.aci.2017.07.003.
- [58] Z. Jin and Z. Jin. “Ontology-Oriented
Interactive Environment Modeling”. Environ.
Model. Requir. Eng. Softw. Intensive Syst.,
pages 45–67, 2018, doi: 10.1016/B978-0-12-
801954-2.00004-2.
- [59] M. A. Musen and the P. Protégé Team. “The
Protégé Project: A Look Back and a Look
Forward”. AI matters, 1(4), pages 4–12,
2015, doi: 10.1145/2757001.2757003.
- [60] R. Zese, E. Bellodi, F. Riguzzi, G. Cota, and
E. Lamma. “Tableau reasoning for description
logics and its extension to probabilities”. Ann.
Math. Artif. Intell., 82(1–3), pages 101–130,
2018, doi: 10.1007/s10472-016-9529-3.
- [61] G. Mohamed. “Raisonnement pour les
Logiques de Description Appliqué Au Web
Semantique”. PhD thesis, Faculty of
Mathematics and Computer Science,
University of M’SILA, Algeria, 2016.