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Adaptive Attacker Strategy Development Against Moving Target Cyber Defenses Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Dynamic Recognition of Phishing URLs Using Deep Learning Techniques

verfasst von : S. Sountharrajan, M. Nivashini, Shishir K. Shandilya, E. Suganya, A. Bazila Banu, M. Karthiga

Erschienen in: Advances in Cyber Security Analytics and Decision Systems

Verlag: Springer International Publishing

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Abstract

Phishing is a critical issue that faces the digital security. The straightforwardness of the web and Internet uncovered open doors for offenders to transfer malevolent substance at the same time with the upgrade of online business trades, for example, phishing – the demonstration of taking individual data which ascends in number. Internet clients’ costs have been increased to billions of dollars for each year due to phishing. Phishers use parodied email, Uniform Resource Locator (URL) locations of phony sites, and phishing programming to take individual data and monetary record subtleties, for example, usernames and passwords. The boycott system is definitely not a sufficient method to remain safe from the cybercriminals. Hence, phishing site pointers must be considered for this reason, with the presence and utilization of machine learning calculations. The current techniques make utilization of all separated attributes in the phishing URL location, prompting high false positive rate.
In this manner, the proposed work manages strategies for distinguishing phishing web destinations by investigating different attributes of genuine and phishing URLs utilizing profound learning procedures, for example, deep Boltzmann machine (DBM), stacked auto-encoder (SAE), and deep neural network (DNN). DBM and SAE are utilized for pre-preparing the model with a superior portrayal of data for attribute determination, among which SAE has accomplished lower misclassification mistake with nine and includes a diminished list of attributes and DNN is utilized for twofold grouping in distinguishing obscure URL as either a phishing URL or a genuine URL. The proposed framework accomplishes higher location rate of 94% with low false positive rate than other machine learning strategies.

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Vorheriges Kapitel Deep Reinforcement Learning for Adaptive Cyber Defense and Attacker’s Pattern Identification
Nächstes Kapitel Efficient Reconfigurable Integrated Cryptosystems for Cybersecurity Protection
Literatur
Bergholz, A., De Beer, J., Glahn, S., Moens, M.-F., Paaß, G., & Strobel, S. (2010). New filtering approaches for phishing email. Journal of Computer Security, 18, 7–35.CrossRef
Cao, Y., Han, W., & Le, Y. (2008). Anti-phishing based on automated individual white-list. In DIM ‘08: Proceedings of the 4th ACM workshop on digital identity management (pp. 51–60). New York: ACM.CrossRef
Chen, K.-T., Chen, J.-Y., Huang, C.-R., & Chen, C.-S. (2009). Fighting phishing with discriminative keypoint features. Internet Computing, IEEE, 13(3), 56–63.CrossRef
Chou, N., Ledesma, R., Teraguchi, Y., & Mitchell, J. C. (2004). Client-side defense against web-based identity theft. In NDSS. The Internet Society.
Dong, X., Clark, J., & Jacob, J. (2008). Modelling user-phishing interaction in Human System Interactions, Conference on, 2008, May, pp. 627–632.
Downs, J. S., Holbrook, M., & Cranor, L. F. (2007). Behavioral response to phishing risk. In Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit, ser. eCrime ‘07 (pp. 37–44). New York: ACM.CrossRef
Hara, M., Yamada, A., & Miyake, Y. (2009). Visual similarity-based phishing detection without victim site information. In IEEE symposium on computational intelligence in cyber security, 2009. CICS ‘09 (pp. 30–36).CrossRef
Holz, T., Gorecki, C., Rieck, K., & Freiling, F. C. (2008). Measuring and detecting fast-flux service networks. In Proceedings of the network and distributed system security symposium (NDSS).
Huang, H., Tan, J., & Liu, L. (2009). Countermeasure techniques for deceptive phishing attack. In International conference on new trends in information and service science, 2009. NISS ‘09 (pp. 636–641).CrossRef
James, J., Sandhya, L., & Thomas, C. (2013). Detection of phishing URLs using machine learning techniques. IEEE international conference on control communication and computing (ICCC).
Knickerbocker, P., Yu, D., & Li, J. (2009). Humboldt: A distributed phishing disruption system. In eCrime researchers summit (pp. 1–12).
Kumaraguru, P., Rhee, Y., Acquisti, A., Cranor, L. F., Hong, J., & Nunge, E. (2007). Protecting people from phishing: The design and evaluation of an embedded training email system. In Proceedings of the SIGCHI conference on human factors in computing systems, ser. CHI ‘07 (pp. 905–914). New York: ACM.
Likarish, P., Dunbar, D., & Hansen, T. E. (2008). Phishguard: A browser plug-in for protection from phishing. In 2 international conference on internet multimedia services architecture and applications, 2008. IMSAA 2008 (pp. 1–6).
Moore, T., & Clayton, R. (2007). Examining the impact of website take-down on phishing. In eCrime ‘07: Proceedings of the anti-phishing working groups 2nd annual eCrime researchers summit (pp. 1–13). New York: ACM.
Prakash, P., Kumar, M., Kompella, R. R., & Gupta, M. (2010). Phishnet: Predictive blacklisting to detect phishing attacks. In INFOCOM’10: Proceedings of the 29th conference on information communications (pp. 346–350). Piscataway: IEEE Press.
Salakhutdinov, R. R., & Larochelle, H. (2010). Efficient learning of deep Boltzmann machines. In Proceedings of the international conference on artificial intelligence and statistics (Vol. 13).
Sewak, M., Karim, M. R., & Pujari, P. (2018). Practical convolutional neural network models: Implement advanced deep learning models using Python. Packt Publishing Ltd. Birmingham, United Kingdom.
Sheng, S., Wardman, B., Warner, G., Cranor, L. F., Hong, J., & Zhang, C. (2009, July). An empirical analysis of phishing blacklists. In Proceedings of the 6th conference in email and anti-spam, ser. CEAS’09. Mountain view.
Sheng, S., Holbrook, M., Kumaraguru, P., Cranor, L. F., & Downs, J. (2010). Who falls for phish?: A demographic analysis of phishing susceptibility and effectiveness of interventions. In Proceedings of the 28 international conference on human factors in computing systems, ser. CHI ‘10 (pp. 373–382). New York: ACM.
Weider, D., Yu, Nargundkar, S., & Tiruthani, N. (July 2008). A phishing vulnerability analysis of web based systems. In Proceedings of the 13th IEEE symposium on computers and communications (ISCC 2008) (pp. 326–331). Marrakech: IEEE.
Whittaker, C., Ryner, B., & Nazif, M. (2010). Large-scale automatic classification of phishing pages. In NDSS ‘10.
Yoshua, B., & Pascal, L. (2007). Greedy layer-wise training of deep networks. In Advances in neural networks.
Yue, C., & Wang, H. (2008). Anti-phishing in offense and defense. In Computer security applications conference, ACSAC 2008. Annual, 8–12 2008 (pp. 345–354).
Metadaten
Titel
Dynamic Recognition of Phishing URLs Using Deep Learning Techniques
verfasst von
S. Sountharrajan
M. Nivashini
Shishir K. Shandilya
E. Suganya
A. Bazila Banu
M. Karthiga
Copyright-Jahr
2020
Buch
Advances in Cyber Security Analytics and Decision Systems

Print ISBN: 978-3-030-19352-2

Electronic ISBN: 978-3-030-19353-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-19353-9_3

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