0
Views
0
CrossRef citations to date
0
Altmetric
Review Article

A comprehensive literature review on phishing URL detection using deep learning techniques

Received 22 Feb 2024, Accepted 07 Jul 2024, Published online: 23 Jul 2024

References

  • Available from: https://www.getastra.com/blog/security-audit/cyber-crime-statistics
  • Gogoi B, Ahmed T, Dutta A. A hybrid approach combining blocklists, machine learning and deep learning for detection of malicious URLs. In: 2022 IEEE India Council International Subsections Conference (INDISCON); Bhubaneswar, India. IEEE. 2022 July. p. 1–6.
  • Choi H, Zhu BB, Lee H. Detecting malicious web links and identifying their attack types. In: 2nd USENIX Conference on Web Application Development (WebApps 11); Portland OR. 2011.
  • Thakur K, Ali ML, Obaidat MA, et al. A systematic review on deep-learning-based phishing email detection. Electronics. 2023;12(21):4545. doi: 10.3390/electronics12214545
  • Berghel H, Carpinter J, Jo JY. Phish phactors: offensive and defensive strategies. Adv Comput. 2007;70:223–268.
  • Kelleher JD. Deep learning. United States: MIT press; 2019.
  • Sarker IH. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci. 2021;2(6):420. doi: 10.1007/s42979-021-00815-1
  • Al-Ahmadi S, Alotaibi A, Alsaleh O. PDGAN: phishing detection with generative adversarial networks. IEEE Access. 2022;10:42459–42468. doi: 10.1109/ACCESS.2022.3168235
  • Xiao X, Xiao W, Zhang D, et al. Phishing websites detection via CNN and multi-head self-attention on imbalanced datasets. Comput & Secur. 2021;108:102372. doi: 10.1016/j.cose.2021.102372
  • Mourtaji Y, Bouhorma M, Alghazzawi D, et al. Hybrid rule-based solution for phishing URL detection using convolutional neural network. Wireless Commun Mob Comput. 2021;2021:1–24. doi: 10.1155/2021/8241104
  • Wei W, Ke Q, Nowak J, et al. Accurate and fast URL phishing detector: a convolutional neural network approach. Comput Networks. 2020;178:107275. doi: 10.1016/j.comnet.2020.107275
  • Chen Y, Zhou Y, Dong Q, et al. A malicious URL detection method based on CNN. In: 2020 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS); Shenyang, China. IEEE; 2020 Dec. p. 23–28.
  • Janet B, Reddy S. Anti-phishing system using LSTM and CNN. In: 2020 IEEE International Conference for Innovation in Technology (INOCON); Bangalore, India. IEEE. 2020 Nov. p. 1–5.
  • Luo C, Su S, Sun Y, et al. A convolution-based system for malicious URLs detection. Comput, Mater & Continua. 2020;62(1):399–411. doi: 10.32604/cmc.2020.06507
  • Al-Milli N, Hammo BH. A convolutional neural network model to detect illegitimate URLs. In: 2020 11th International Conference on Information and Communication Systems (ICICS); Irbid, Jordan. IEEE. 2020 Apr. p. 220–225.
  • Aljofey A, Jiang Q, Qu Q, et al. An effective phishing detection model based on character level convolutional neural network from URL. Electronics. 2020;9(9):1514. doi: 10.3390/electronics9091514
  • Adebowale MA, Lwin KT, Hossain MA. Deep learning with convolutional neural network and long short-term memory for phishing detection. In: 2019 13th International Conference on Software, Knowledge, Information Management and Applications (SKIMA); Island of Ulkulhas, Maldives. IEEE. 2019 Aug. p. 1–8.
  • Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J Big Data. 2021;8(1):1–74. doi: 10.1186/s40537-021-00444-8
  • Kumar A, Gaur N, Chakravarty S, et al. Analysis of spectrum sensing using deep learning algorithms: CNNs and RNNs. Ain Shams Eng J. 2024;15(3):102505. doi: 10.1016/j.asej.2023.102505
  • Sahoo VK, Singh V, Gourisaria MK, et al. URL classification on extracted feature using deep learning. In: Computer Vision and Machine Intelligence: Proceedings of CVMI 2022; Singapore. Springer Nature; 2023. p. 415–428.
  • Saha I, Sarma D, Chakma RJ, et al. Phishing attacks detection using deep learning approach. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT); Tirunelveli, India. IEEE. 2020 Aug. p. 1180–1185.
  • Remmide MA, Boumahdi F, Boustia N, et al. Detection of phishing URLs using temporal convolutional network. Procedia Comput Sci. 2022;212:74–82. doi: 10.1016/j.procs.2022.10.209
  • Ozcan A, Catal C, Donmez E, et al. A hybrid DNN–LSTM model for detecting phishing URLs. Neural Comput Appl. 2021;35(7):1–17. doi: 10.1007/s00521-021-06401-z
  • Pham TD, Pham TTT, Hoang ST, et al. Exploring efficiency of GAN-based generated URLs for phishing URL detection. In: 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE. 2021 Oct. p. 1–6.
  • AlEroud A, Karabatis G. 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; New Orleans LA USA. 2020 Mar. p. 53–60.
  • Said Y, Alsheikhy AA, Lahza H, et al. Detecting phishing websites through improving convolutional neural networks with self-attention mechanism. Ain Shams Eng J. 2024;15(4):102643. doi: 10.1016/j.asej.2024.102643
  • Hussain M, Cheng C, Xu R, et al. CNN-Fusion: an effective and lightweight phishing detection method based on multi-variant ConvNet. Inf Sci. 2023;631:328–345. doi: 10.1016/j.ins.2023.02.039
  • Kumar PP, Jaya T, Rajendran V. SI-BBA–A novel phishing website detection based on swarm intelligence with deep learning. Mater Today: Proc. 2023;80:3129–3139. doi: 10.1016/j.matpr.2021.07.178
  • Patgiri R, Biswas A, Nayak S. deepBF: malicious URL detection using learned bloom filter and evolutionary deep learning. Comput Commun. 2023;200:30–41. doi: 10.1016/j.comcom.2022.12.027
  • Prabakaran MK, Meenakshi Sundaram P, Chandrasekar AD. An enhanced deep learning‐based phishing detection mechanism to effectively identify malicious URLs using variational autoencoders. IET Inf Secur. 2023;17(3):423–440. doi: 10.1049/ise2.12106
  • Alsaedi M, Ghaleb FA, Saeed F, et al. Multi-modal features representation-based convolutional neural network Model for malicious website detection. IEEE Access. 2023;12:7271–7284. doi: 10.1109/ACCESS.2023.3348071
  • Su MY, Su KL. BERT-Based approaches to identifying malicious URLs. Sensors. 2023;23(20):8499. doi: 10.3390/s23208499
  • Zonyfar C, Lee JB, Kim JD. HCNN-LSTM: hybrid convolutional neural network with long short-term memory integrated for legitimate web prediction. J Web Eng. 2023;22(5):757–782. doi: 10.13052/jwe1540-9589.2251
  • Salah H, Zuhair H. Deep learning in phishing mitigation: a uniform resource locator-based predictive model. Int J Electr Comput Eng. 2023;13(3):3227–3243. doi: 10.11591/ijece.v13i3.pp3227-3243
  • Wang C, Chen Y. TCURL: exploring hybrid transformer and convolutional neural network on phishing URL detection. Knowl-Based Syst. 2022;258:109955. doi: 10.1016/j.knosys.2022.109955
  • Wu T, Wang M, Xi Y, et al. Malicious url detection model based on bidirectional gated recurrent unit and attention mechanism. Appl Sci. 2022;12(23):12367. doi: 10.3390/app122312367
  • Zheng F, Yan Q, Leung VC, et al. HDP-CNN: highway deep pyramid convolution neural network combining word-level and character-level representations for phishing website detection. Comput & Secur. 2022;114:102584. doi: 10.1016/j.cose.2021.102584
  • Wang Z, Ren X, Li S, et al. A malicious URL detection model based on convolutional neural network. Secur Commun Networks. 2021;2021:1–12. doi: 10.1155/2021/8690662
  • Chen Z, Liu Y, Chen C, et al. Malicious URL detection based on improved multilayer recurrent convolutional neural network model. Secur d Commun Networks. 2021;2021:1–13. doi: 10.1155/2021/9994127
  • Singh S, Singh MP, Pandey R. Phishing detection from URLs using deep learning approach. In: 2020 5th International Conference on Computing, Communication and Security (ICCCS); Patna, Bihar, India. IEEE; 2020 Oct. p. 1–4.
  • Pooja AL, Sridhar M. Analysis of phishing website detection using CNN and bidirectional LSTM. In: 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA); Coimbatore, India. IEEE. 2020 Nov. p. 1620–1629.
  • Huang Y, Yang Q, Qin J, et al. 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); Rotorua, New Zealand. IEEE. 2019 Aug. p. 112–119.
  • Yang W, Zuo W, Cui B. Detecting malicious URLs via a keyword-based convolutional gated-recurrent-unit neural network. IEEE Access. 2019;7:29891–29900. doi: 10.1109/ACCESS.2019.2895751
  • Chatterjee M, Namin AS. Detecting phishing websites through deep reinforcement learning. In: 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC); Winconsin, USA. IEEE. 2019 July (Vol. 2. p. 227–232).
  • Liang Y, Yan X. Using deep learning to detect malicious urls. In: 2019 IEEE International Conference on Energy Internet (ICEI); United States. IEEE. 2019 May. p. 487–492.
  • Chen W, Zeng Y, Qiu M. Using adversarial examples to bypass deep learning based url detection system. In: 2019 IEEE International Conference on Smart Cloud (SmartCloud); Tokyo, Japan. IEEE. 2019 Dec. p. 128–130.
  • Ren F, Jiang Z, Liu J. A bi-directional lstm model with attention for malicious url detection. In: 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC); Chengdu, China. IEEE. 2019 Dec. p. 300–305.
  • Ateeq JH, Moreb M. Detecting malicious URL using neural network. In: 2021 International Congress of Advanced Technology and Engineering (ICOTEN). IEEE. 2021 July. p. 1–8.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.