Abstract
In today’s era, the world is moving toward automation, in which objects connected to Internet can take independent decisions. In such an environment, machine learning which is an integral part of artificial intelligence, is used widely to design algorithms based on the data trends and historical relationships between data. However, security and privacy preservation in such an environment are key challenges in front of the research communities to handle. Hence, in this paper we present a framework which detects the phishing websites using machine learning. The proposed framework implements ten machine learning models and the best three models are ensembled followed by ten rounds of cross-validation. The overall performance of the proposed framework resulted an accuracy of 97.27% which is better than the existing proposals in the literature.
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References
Barbash GI (2010) New technology and health care costs-the case of robot-assisted surgery. The New Engl J Medi 363(8):701
Brady M (1985) Artificial intelligence and robotics. Artif Intell 26(1):79–121
Burgio LD, Allen-Burge R, Roth DL, Bourgeois MS, Dijkstra K, Gerstle J, Jackson E, Bankester L (2001) Come talk with me: Improving communication between nursing assistants and nursing home residents during care routines. The Gerontologist 41(4):449–460
Erickson T, Danis CM, Kellogg WA, Helander ME (2008) Assistance: the work practices of human administrative assistants and their implications for it and organizations. In: Proceedings of the 2008 ACM conference on Computer supported cooperative work, ACM, pp 609–618
EY (2019 (accessed April 21, 2019)) Report by ey.com. https://www.ey.com/Publication/vwLUAssets/EY-global-information-security-survey-2014/FILE/EY-global-information-security-survey-2014.pdf
F5 (2019 (accessed April 20, 2019)) Report by f5 labs. https://www.darkreading.com/attacks-breaches/new-report-iot-now-top-internet-attack-target/d/d-id/1333147
Forbes (2019 (accessed October 11, 2019)) Artificial intelligence examples. https://www.forbes.com/sites/bernardmarr/2018/04/30/27-incredible-examples-of-ai-and-machine-learning-in-practice/#67c9eb217502
Fortune (2019 (accessed October 11, 2019)) Artificial intelligence could be a boon to the global economy. https://fortune.com/2019/10/09/artificial-intelligence-14-trillion-boon-only-if-overcome-one-thing/
HP (2019 (accessed April 20, 2019)) Internet of things devices. https://www8.hp.com/us/en/hp-news/press-release.html?id=1744676
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y (2017) Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2(4):230–243
Kausar F, Al-Otaibi B, Al-Qadi A, Al-Dossari N (2014) Hybrid client side phishing websites detection approach. Int J Adv Comput Sci Appl (IJACSA) 5(7):132–140
Kruegel C, Vigna G (2003) Anomaly detection of web-based attacks. In: Proceedings of the 10th ACM conference on computer and communications security, ACM, pp 251–261
Makkar A, Kumar N (2019) Cognitive spammer: a framework for pagerank analysis with split by over-sampling and train by under-fitting. Future Gener Comput Syst 90:381–404
Mohammad RM, Thabtah F, McCluskey L (2015) Phishing websites features. University of Huddersfield, School of Computing and Engineering
Schorr H, Rappaport A (1989) Innovative applications of artificial intelligence. Cambridge, MA, MIT Press, , 379 p (For individual items see A90-32502 to A90-32504)
Sigmoidal (2019 (accessed October 11, 2019)) Artificial intelligence in healthcare. https://sigmoidal.io/artificial-intelligence-and-machine-learning-for-healthcare/#targetText=Artificial
Times E (2019 (accessed April 20, 2019)) Report by economic times. https://telecom.economictimes.indiatimes.com/news/iot-devices-malware-grew-73-in-q3-2018-report/67171649
Tsai CF, Hsu YF, Lin CY, Lin WY (2009) Intrusion detection by machine learning: a review. Expert Syst Appl 36(10):11994–12000
Zeng D, Chen H, Lusch R, Li SH (2010) Social media analytics and intelligence. IEEE Intell Syst 25(6):13–16
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Makkar, A., Kumar, N., Sama, L., Mishra, S., Samdani, Y. (2021). An Intelligent Phishing Detection Scheme Using Machine Learning. In: Giri, D., Buyya, R., Ponnusamy, S., De, D., Adamatzky, A., Abawajy, J.H. (eds) Proceedings of the Sixth International Conference on Mathematics and Computing. Advances in Intelligent Systems and Computing, vol 1262. Springer, Singapore. https://doi.org/10.1007/978-981-15-8061-1_13
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DOI: https://doi.org/10.1007/978-981-15-8061-1_13
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