Fetching the paper…
Reading the bibliography…
Spear Phishing is a type of cyber-attack where the attacker sends hyperlinks through email on well-researched targets.
C. Nwobi-Okoye, “Game theoretic aspect of phishing and virtual websites,” Pacific Journal of Science and Technology , vol. 12, no. 1, pp. 260–269, 2011
2011
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. K. Jain and B. B. Gupta, “A novel approach to protect against phishing attacks at client side using auto-updated white-list,” EURASIP Journal on Information Security , vol. 2016, no. 1, pp. 1–11, 2016
2016
Earlier work this paper cites.
M. Zouina and B. Outtaj, “A novel lightweight url phishing detection system using svm and similarity index,” Human-centric Computing and Information Sciences , vol. 7, no. 1, pp. 1–13, 2017
2017
Earlier work this paper cites.
W. Niu, X. Zhang, G. Yang, Z. Ma, and Z. Zhuo, “Phishing emails detection using cs-svm,” in 2017 IEEE International Symposium on Parallel and Distributed Processing with Applications and 2017 IEEE International Conference on Ubiquitous Computing and Communications (ISPA/IUCC) . IEEE, 2017, pp. 1054–1059
2017
Earlier work this paper cites.
A. Subasi, E. Molah, F. Almkallawi, and T. J. Chaudhery, “Intelligent phishing website detection using random forest classifier,” in 2017 International conference on electrical and computing technologies and applications (ICECTA) . IEEE, 2017, pp. 1–5
2017
Earlier work this paper cites.
A. Altaher, “Phishing websites classification using hybrid svm and knn approach,” International Journal of Advanced Computer Science and Applications , vol. 8, no. 6, pp. 90–95, 2017
2017
Earlier work this paper cites.
Y. Zhang, Z. Gan, K. Fan, Z. Chen, R. Henao, D. Shen, and L. Carin, “Adversarial feature matching for text generation,” in International Conference on Machine Learning . PMLR, 2017, pp. 4006–4015
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
N. Figueroa, G. L’Huillier, and R. Weber, “Adversarial classification using signaling games with an application to phishing detection,” Data mining and knowledge discovery , vol. 31, no. 1, pp. 92–133, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Odena, C. Olah, and J. Shlens, “Conditional image synthesis with auxiliary classifier gans,” in International conference on machine learning . PMLR, 2017, pp. 2642–2651
2017
Earlier work this paper cites.
X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. Paul Smolley, “Least squares generative adversarial networks,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 2794–2802
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
A. K. Jain and B. Gupta, “Phish-safe: Url features-based phishing detection system using machine learning,” in Cyber Security . Springer, 2018, pp. 467–474
2018
Earlier work this paper cites.
S. Jagadeesan, A. Chaturvedi, and S. Kumar, “Url phishing analysis using random forest,” International Journal of Pure and Applied Mathematics , vol. 118, no. 20, pp. 4159–4163, 2018
2018
Earlier work this paper cites.
A. Pandey, N. Gill, K. S. P. Nadendla, and I. S. Thaseen, “Identification of phishing attack in websites using random forest-svm hybrid model,” in International conference on intelligent systems design and applications . Springer, 2018, pp. 120–128
2018
Earlier work this paper cites.
P. Yi, Y. Guan, F. Zou, Y. Yao, W. Wang, and T. Zhu, “Web phishing detection using a deep learning framework,” Wireless Communications and Mobile Computing , vol. 2018, 2018
2018
Cited alongside, same era.
W. Chen, W. Zhang, and Y. Su, “Phishing detection research based on lstm recurrent neural network,” in International Conference of Pioneering Computer Scientists, Engineers and Educators . Springer, 2018, pp. 638–645
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
“Internet crime report,” May 2020. [Online]. Available: $www.ic3.gov/Media/PDF/AnnualReport/2020_IC3Report.pdf$
2020
Later among the works it cites.
“Data breach investigations report,” 2020. [Online]. Available: $enterprise.verizon.com/en-gb/resources/reports/dbir/$
2020
Later among the works it cites.
F. Tajaddodianfar, J. W. Stokes, and A. Gururajan, “Texception: A character/word-level deep learning model for phishing url detection,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 2857–2861
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
W. Wang, Z. Gan, W. Wang, D. Shen, J. Huang, W. Ping, S. Satheesh, and L. Carin, “Topic compositional neural language model,” in International Conference on Artificial Intelligence and Statistics . PMLR, 2018, pp. 356–365
2018
Cited alongside, same era.
Y. Li and J. Ye, “Learning adversarial networks for semi-supervised text classification via policy gradient,” in Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining , 2018, pp. 1715–1723
2018
Cited alongside, same era.
A. Anand, K. Gorde, J. R. A. Moniz, N. Park, T. Chakraborty, and B.-T. Chu, “Phishing url detection with oversampling based on text generative adversarial networks,” in 2018 IEEE International Conference on Big Data (Big Data) . IEEE, 2018, pp. 1168–1177
2018
Cited alongside, same era.
“Internet security threat report,” February 2019. [Online]. Available: $docs.broadcom.com/doc/istr-24-2019-en$
2019
Cited alongside, same era.
Y. Huang, Q. Yang, J. Qin, and W. Wen, “Phishing url detection via cnn and attention-based hierarchical rnn,” in 2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) . IEEE, 2019, pp. 112–119
2019
Cited alongside, same era.
D. Aksu, Z. Turgut, S. Üstebay, and M. A. Aydin, “Phishing analysis of websites using classification techniques,” in International Telecommunications Conference . Springer, 2019, pp. 251–258
2019
Cited alongside, same era.
M. T. Suleman and S. M. Awan, “Optimization of url-based phishing websites detection through genetic algorithms,” Automatic Control and Computer Sciences , vol. 53, no. 4, pp. 333–341, 2019
2019
Cited alongside, same era.
P. Yang, G. Zhao, and P. Zeng, “Phishing website detection based on multidimensional features driven by deep learning,” IEEE Access , vol. 7, pp. 15 196–15 209, 2019
2019
Cited alongside, same era.
2020
Later among the works it cites.
A. AlEroud and G. Karabatis, “Bypassing detection of url-based phishing attacks using generative adversarial deep neural networks,” in Proceedings of the Sixth International Workshop on Security and Privacy Analytics , 2020, pp. 53–60
2020
Later among the works it cites.
D. Xiao and M. Jiang, “Malicious mail filtering and tracing system based on knn and improved lstm algorithm,” in 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) . IEEE, 2020, pp. 222–229
2020
Later among the works it cites.
X. Xiao, D. Zhang, G. Hu, Y. Jiang, and S. Xia, “Cnn–mhsa: A convolutional neural network and multi-head self-attention combined approach for detecting phishing websites,” Neural Networks , vol. 125, pp. 303–312, 2020
2020
Later among the works it cites.
D. Croce, G. Castellucci, and R. Basili, “Gan-bert: Generative adversarial learning for robust text classification with a bunch of labeled examples,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics , 2020, pp. 2114–2119
2020
Later among the works it cites.
H. Shirazi, S. R. Muramudalige, I. Ray, and A. P. Jayasumana, “Improved phishing detection algorithms using adversarial autoencoder synthesized data,” in 2020 IEEE 45th Conference on Local Computer Networks (LCN) . IEEE, 2020, pp. 24–32
2020
Later among the works it cites.
F. Tchakounte, V. S. Nyassi, D. E. H. Danga, K. P. Udagepola, and M. Atemkeng, “A game theoretical model for anticipating email spear-phishing strategies,” EAI Endorsed Transactions on Scalable Information Systems , vol. 8, no. 30, p. e5, 2020
2020
Later among the works it cites.
Y. Jin, Y. Wang, M. Long, J. Wang, S. Y. Philip, and J. Sun, “A multi-player minimax game for generative adversarial networks,” in 2020 IEEE International Conference on Multimedia and Expo (ICME) . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
M. Katzef, A. C. Cullen, T. Alpcan, C. Leckie, and J. Kopacz, “Distributed generative adversarial networks for anomaly detection,” in International Conference on Decision and Game Theory for Security . Springer, 2020, pp. 3–22
2020
Later among the works it cites.
“Phishing site urls,” July 2020. [Online]. Available: https://www.kaggle.com/taruntiwarihp/phishing-site-urls
2020
Later among the works it cites.
S. Y. Yerima and M. K. Alzaylaee, “High accuracy phishing detection based on convolutional neural networks,” in 2020 3rd International Conference on Computer Applications & Information Security (ICCAIS) . IEEE, 2020, pp. 1–6
2020
Later among the works it cites.
A. O’Mara, I. Alsmadi, and A. AlEroud, “Generative adverserial analysis of phishing attacks on static and dynamic content of webpages,” in 2021 IEEE Intl Conf on Parallel Distributed Processing with Applications, Big Data Cloud Computing, Sustainable Computing Communications, Social Computing Networking , 2021, pp. 1657–1662
2021
Closest in time.