In the dynamic realm of digital advancements, the persistent menace of phishing attacks continues to jeopardize the security landscape for both individuals and organizations. As cyber attacks continue to proliferate, URL-based phishing attacks are growing rapidly. This paper presents an exploratory study aimed at enhancing cybersecurity measures through the detection of phishing URLs. Our approach involves exploring the integration of Gated Recurrent Units (GRU) with various attention mechanisms to bolster accuracy in discerning between legitimate and phishing URLs. Notably, our study reveals that the implementation of the Bahdanau attention mechanism with GRU yields remarkable results, achieving an accuracy of 98.14%. We conducted experiments on a comprehensive dataset comprising 95,913 URLs. Our primary objectives include fortifying cybersecurity defenses against phishing threats, innovating through the integration of diverse attention mechanisms with GRU, and substantiating the efficacy of our model through rigorous evaluation metrics. As the realm of cybersecurity confronts escalating challenges, our research not only offers valuable insights but also charts a promising trajectory for future advancements in cybersecurity strategies.
AbroshanHDevosJPoelsGLaermansE. COVID-19 and phishing: Effects of human emotions, behavior, and demographics on the success of phishing attempts during the pandemic. IEEE Access. 2021; 9: 121916-121929. doi: 10.1109/ACCESS.2021.3109091.
2.
GoenkaRChawlaMTiwariN. A comprehensive survey of phishing: Mediums, intended targets, attack and defence techniques, and a novel taxonomy. International Journal of Information Security. 2023. doi: 10.1007/s10207-023-00768-x.
3.
GalloLGentileDRuggieroSBottaAVentreG. The human factor in phishing: collecting and analyzing user behavior when reading emails. Computers and Security. 2023; 103671. doi: 10.1016/j.cose.2023.103671.
4.
SolicKOcevcicHBlazevicD. Survey on password quality and confidentiality. Automatika. 2015; 56(1): 69-75. doi: 10.7305/automatika.2015.04.587.
5.
SafiASinghS. A systematic literature review on phishing website detection techniques. Journal of King Saud University – Computer and Information Sciences. 2023; 35(2): 590-611. doi: 10.1016/j.jksuci.2023.01.004.
6.
DoNQSelamatAKrejcarOHerrera-ViedmaEFujitaH. Deep learning for phishing detection: Taxonomy, current challenges and future directions. IEEE Access. 2022; 10: 36429-36463. doi: 10.1109/ACCESS.2022.3151903.
7.
MohanMTamizhazhaganVBalajiS. Adapt: Attack detection and prevention via deep learning techniques. Journal of Intelligent and Fuzzy Systems. 2024; 1: 1-10. doi: 10.3233/JIFS-236371.
8.
VinayakumarRSomanKPPoornachandranP. Detecting malicious domain names using deep learning approaches at scale. Journal of Intelligent and Fuzzy Systems. 2018; 1: 1355-1367. doi: 10.3233/JIFS-169431.
9.
YamarthyAKKoteswararaoC. Mdepthnet based phishing attack detection using integrated deep learning methodologies for cyber security enhancement. Cluster Computing. 2024. doi: 10.1007/s10586-024-04313-w.
10.
JalilSUsmanMFongA. Highly accurate phishing url detection based on machine learning. Journal of Ambient Intelligence and Humanized Computing. 2023; 14: 9233-9251. doi: 10.1007/s12652-022-04426-3.
11.
AdaneKBeyeneBAbebeM. Single and hybrid-ensemble learning-based phishing website detection: examining impacts of varied nature datasets and informative feature selection technique. Digital Threats: Research and Practice. 2023; 4(3): 1-27. doi: 10.1145/3611392.
12.
Al-SawwaJAlmseidinMAlkasassbehM, et al. Spark-based multi-verse optimizer as wrapper features selection algorithm for phishing attack challenge. Cluster Computing. 2024. doi: 10.1007/s10586-024-04272-2.
13.
LakshmiLReddyMPSanthaiahC, et al. Smart phishing detection in web pages using supervised deep learning classification and optimization technique adam. Wireless Personal Communications. 2021; 118: 3549-3564. doi: 10.1007/s11277-021-08196-7.
14.
TangLMahmoudQH. A deep learning-based framework for phishing website detection. IEEE Access. 2022; 10: 1509-1521. doi: 10.1109/ACCESS.2021.3137636.
15.
SahingozOKBUBErEKuguE. Dephides: Deep learning based phishing detection system. IEEE Access. 2024; 12: 8052-8070. doi: 10.1109/ACCESS.2024.3352629.
FengJZouLYeOHanJ. Web2vec: Phishing webpage detection method based on multidimensional features driven by deep learning. IEEE Access. 2020; 8: 221214-221224. doi: 10.1109/ACCESS.2020.3043188.
18.
MittalKGillKSChauhanRSinghMBanerjeeD. Detection of phishing domain using logistic regression technique and feature extraction using bert classification model. In: 2023 3rd International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON). 2023; 1-5. doi: 10.1109/SMARTGENCON60755.2023.10442975.
19.
NabilaOGWicaksonoHRGirinotoYasaRNSetiawanH. Benchmarking model url features and image based for phishing url detection. In: 2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS). 2023; 177-182. doi: 10.1109/ICIMCIS60089.2023.10349059.
20.
Benavides-AstudilloEFuertesWSanchez-GordonSNuñez-AgurtoDRodríguez-GalánG. A phishing-attack-detection model using natural language processing and deep learning. Applied Sciences. 2023; 13(9): 5275. doi: 10.3390/app13095275.
21.
WangYZhuWXuHQinZRenKMaW. A large-scale pretrained deep model for phishing url detection. In: ICASSP 2023 – 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2023; 1-5. doi: 10.1109/ICASSP49357.2023.10095719.
22.
PrabakaranMKMeenakshi SundaramPChandrasekarAD. An enhanced deep learning-based phishing detection mechanism to effectively identify malicious urls using variational autoencoders. IET Information Security. 2023; 17(3): 423-440. doi: 10.1049/ise2.12106.
23.
VaitkeviciusPMarcinkeviciusV. Comparison of classification algorithms for detection of phishing websites. Informatica. 2020; 31(1): 143-160. doi: 10.15388/20-INFOR404.
24.
AmanullahMSelvakumarVJyotAPurohitN.SSFahleviM. Cnn based prediction analysis for web phishing prevention. In: 2022 International Conference on Edge Computing and Applications (ICECAA). 2022; 1-7. doi: 10.1109/ICECAA55415.2022.9936112.
25.
AlshingitiZAlaqelR.Al-MuhtadiJHaqQ.E.USaleemKFaheemMH. A deep learning-based phishing detection system using cnn, lstm, and lstm-cnn. Electronics. 2023; 12(1): 232. doi: 10.3390/electronics12010232.
26.
SalahHZuhairH. Deep learning in phishing mitigation: a uniform resource locator-based predictive model. International Journal of Electrical and Computer Engineering. 2023; 13(3): 3227-3243. doi: 10.11591/ijece.v13i3.pp3227-3243.
27.
AnupamSKarAK. Phishing website detection using support vector machines and nature-inspired optimization algorithms. Telecommunication Systems. 2021; 76: 17-32. doi: 10.1007/s11235-020-00739-w.
28.
AldakheelEAZakariahMGashgariGAAlmarshadFAAlzahraniAI. A deep learning-based innovative technique for phishing detection in modern security with uniform resource locators. Sensors. 2023; 23(9): 4403. doi: 10.3390/s23094403.
29.
PatilSPatilMChinnaiahK. Machine learning and deep learning for phishing page detection. Research Reports on Computer Science. 2023; 2(3): 45-54. doi: 10.37256/rrcs.2320232629.
30.
ZaimiRHafidiMLamiaM. A deep learning approach to detect phishing websites using cnn for privacy protection. Intelligent Decision Technologies (Preprint), 2023; 1-16. doi: 10.3233/idt-220307.
RoySSAwadAIAmareLAErkihunMTAnasM. Multimodel phishing url detection using lstm, bidirectional lstm, and gru models. Future Internet. 2022; 14(11). doi: 10.3390/fi14110340.
33.
ChoKVan MerriënboerBGulcehreCBahdanauDBougaresFSchwenkHBengioY. Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. 2014. doi: 10.48550/arXiv.1406.1078.
34.
ZhouPShiWTianJQiZLiBHaoHXuB. Attention-based bidirectional long short-term memory networks for relation classification. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (volume 2: Short Papers). 2016; 207-212.
35.
XuKBaJKirosRChoKCourvilleASalakhudinovRZemelRBengioY. Show, attend and tell: Neural image caption generation with visual attention. In: International Conference on Machine Learning. 2015; 2048-2057. PMLR.
36.
ChorowskiJKBahdanauDSerdyukDChoKBengioY. Attention-based models for speech recognition. Advances in Neural Information Processing Systems. 2015; 28.
37.
BahdanauDChoKBengioY. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. 2014. doi: 10.48550/arXiv.1409.0473.
VaswaniAShazeerNParmarNUszkoreitJJonesLGomezANKaiserŁPolosukhinI. Attention is all you need. Advances in Neural Information Processing Systems. 2017; 30.
40.
GoodfellowIBengioYCourvilleABengioY. Deep Learning. 2016; 1. MIT press Cambridge.
41.
SrivastavaNHintonGKrizhevskyASutskeverISalakhutdinovR. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research. 2014; 15(1); 1929-1958.
42.
KingmaDPBaJ. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 2014. doi: 10.48550/arXiv.1412.6980.
43.
DomingosP. A few useful things to know about machine learning. Communications of the ACM. 2012; 55(10): 78-87. doi: 10.1145/2347736.2347755.
