Abstract
The paper proposes an alternative email account protection to prevent a very specific targeting email attacks where an attacker can impersonate a legitimate/trusted sender to steal personal information to the recipient. Authorship mechanism based on the analysis of the author’s writing style and implemented through binary traditional and deep learning classifiers is applied to build the email verification mechanism. A flexible architecture, where the authorship component can be placed in different locations, is proposed. Due to its location and consequently to the email data available, can be exploited an individual writing style, or an end to end writing style learning related to the sender-receiver communication. The system is validated on two different dataset (i) the well-known public Enron dataset, with the experiments showing the author verification accuracy of 96.5% and 99% respectively for the individual and end to end writing style learning and (ii) our private dataset, with accuracy results of 98.3% and 97%. An alternative classification training, that exploits the partition of the dataset in subsets having approximately the same length, is presented. From the results obtained is proved how such training approach outperforms the traditional training where emails of different lengths are contained in the same training dataset. The overall results obtained proved that the authorship mechanism proposed is a promising alternative support technique exploitable as an email anti-scam or anti-theft tool to guarantee secure email communication.
This work has been partially supported by H2020 EU-funded projects SPARTA, GA 830892, C3ISP, GA 700294 and EIT-Digital Project HII, PRIN Governing Adaptive.
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References
Brocardo, M.L., Traore, I., Saad, S., Woungang, I.: Authorship verification for short messages using stylometry. In: 2013 International Conference on Computer, Information and Telecommunication Systems (CITS), pp. 1–6. IEEE (2013)
Brocardo, M.L., Traore, I., Woungang, I.: Authorship verification of e-mail and tweet messages applied for continuous authentication. J. Comput. Syst. Sci. 81(8), 1429–1440 (2015)
Chen, X., Hao, P., Chandramouli, R., Subbalakshmi, K.P.: Authorship similarity detection from email messages. In: Perner, P. (ed.) MLDM 2011. LNCS (LNAI), vol. 6871, pp. 375–386. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23199-5_28
Connor, J.T., Martin, R.D., Atlas, L.E.: Recurrent neural networks and robust time series prediction. IEEE Trans. Neural Netw. 5(2), 240–254 (1994)
Dasarathy, B.V.: Nearest neighbor (NN) norms: NN pattern classification techniques. IEEE Computer Society Tutorial (1991)
Dewan, P., Kashyap, A., Kumaraguru, P.: Analyzing social and stylometric features to identify spear phishing emails. In: 2014 APWG Symposium on Electronic Crime Research (ecrime), pp. 1–13. IEEE (2014)
Donahue, J., et al.: Long-term recurrent convolutional networks for visual recognition and description. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2625–2634 (2015)
Freund, Y., Schapire, R., Abe, N.: A short introduction to boosting. J. Japanese Soc. Artif. Intell. 14(771–780), 1612 (1999)
Giorgi, G., Saracino, A., Martinelli, F.: Email spoofing attack detection through an end to end authorship attribution system. In: Furnell, S., Mori, P., Weippl, E.R., Camp, O. (eds.) Proceedings of the 6th International Conference on Information Systems Security and Privacy, ICISSP 2020, Valletta, Malta, February 25–27, 2020. pp. 64–74. SCITEPRESS (2020). https://doi.org/10.5220/0008954600640074
Graves, A., Mohamed, A.r., Hinton, G.: Speech recognition with deep recurrent neural networks. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6645–6649. IEEE (2013)
Guenther, M.: Social engineering-security awareness series. Information Warfare site UK online. http://www.iwar.org.uk/comsec/resources/sa-tools/social-engineering.pdf. Accessed 20 Dec 2006
Hamid, I.R.A., Abawajy, J., Kim, T.: Using feature selection and classification scheme for automating phishing email detection. Stud. Inf. Contr. 22(1), 61–70 (2013)
Han, Y., Shen, Y.: Accurate spear phishing campaign attribution and early detection. In: Proceedings of the 31st Annual ACM Symposium on Applied Computing, pp. 2079–2086 (2016)
Ho, T.K.: Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition, vol. 1, pp. 278–282. IEEE (1995)
Jakobsson, M.: Modeling and preventing phishing attacks. In: Financial Cryptography, vol. 5 (2005)
Kiefer, J., Wolfowitz, J., et al.: Stochastic estimation of the maximum of a regression function. Ann. Math. Stat. 23(3), 462–466 (1952)
Klimt, B., Yang, Y.: The enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22
Litvak, M.: Deep dive into authorship verification of email messages with convolutional neural network. In: Lossio-Ventura, J.A., Muñante, D., Alatrista-Salas, H. (eds.) SIMBig 2018. CCIS, vol. 898, pp. 129–136. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11680-4_14
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)
Peng, C.Y.J., Lee, K.L., Ingersoll, G.M.: An introduction to logistic regression analysis and reporting. J. Educ. Res. 96(1), 3–14 (2002)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)
Ruder, S., Ghaffari, P., Breslin, J.G.: Character-level and multi-channel convolutional neural networks for large-scale authorship attribution. arXiv preprint arXiv:1609.06686 (2016)
Sanderson, C., Guenter, S.: Short text authorship attribution via sequence kernels, markov chains and author unmasking: An investigation. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pp. 482–491. Association for Computational Linguistics (2006)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Sig. Process. 45(11), 2673–2681 (1997)
Shrestha, P., Sierra, S., Gonzalez, F., Montes, M., Rosso, P., Solorio, T.: Convolutional neural networks for authorship attribution of short texts. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics, vol. 2, pp. 669–674 (2017)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293–300 (1999)
Zhang, X., Zhao, J., LeCun, Y.: Character-level convolutional networks for text classification. In: Advances in neural information processing systems, pp. 649–657 (2015)
Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. J. Amer. Soc. Inf. Sci. Technol. 57(3), 378–393 (2006)
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Giorgi, G., Saracino, A., Martinelli, F. (2022). End to End Autorship Email Verification Framework for a Secure Communication. In: Furnell, S., Mori, P., Weippl, E., Camp, O. (eds) Information Systems Security and Privacy. ICISSP 2020. Communications in Computer and Information Science, vol 1545. Springer, Cham. https://doi.org/10.1007/978-3-030-94900-6_4
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