Phish Responder: A Hybrid Machine Learning Approach to Detect Phishing and Spam Emails

被引:11
作者
Dewis, Molly [1 ]
Viana, Thiago [1 ]
机构
[1] Univ Gloucestershire, Cyber & Tech Comp, Cheltenham GL50 2RH, Glos, England
关键词
phishing; spam; deep learning; machine learning; natural language processing;
D O I
10.3390/asi5040073
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Using technology to prevent cyber-attacks has allowed organisations to somewhat automate cyber security. Despite solutions to aid organisations, many are susceptible to phishing and spam emails which can make an unwanted impact if not mitigated. Traits that make organisations susceptible to phishing and spam emails include a lack of awareness around the identification of malicious emails, explicit trust, and the lack of basic security controls. For any organisation, phishing and spam emails can be received and the consequences of an attack could result in disruption. This research investigated the threat of phishing and spam and developed a detection solution to address this challenge. Deep learning and natural language processing are two techniques that have been employed in related research, which has illustrated improvements in the detection of phishing. Therefore, this research contributes by developing Phish Responder, a solution that uses a hybrid machine learning approach combining natural language processing to detect phishing and spam emails. To ensure its efficiency, Phish Responder was subjected to an experiment in which it has achieved an average accuracy of 99% with the LSTM model for text-based datasets. Furthermore, Phish Responder has presented an average accuracy of 94% with the MLP model for numerical-based datasets. Phish Responder was evaluated by comparing it with other solutions and through an independent t-test which demonstrated that the numerical-based technique is statistically significantly better than existing approaches.
引用
收藏
页数:19
相关论文
共 48 条
[21]  
Kul G., 2020, PHISHING EMAIL COLLE
[22]   An introduction to Deep Learning in Natural Language Processing: Models, techniques, and tools [J].
Lauriola, Ivano ;
Lavelli, Alberto ;
Aiolli, Fabio .
NEUROCOMPUTING, 2022, 470 :443-456
[23]   Deep Learning Techniques on Text Classification Using Natural Language Processing (NLP) In Social Healthcare Network: A Comprehensive Survey [J].
Lavanya, P. M. ;
Sasikala, E. .
ICSPC'21: 2021 3RD INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND COMMUNICATION (ICPSC), 2021, :603-609
[24]   D-Fence: A Flexible, Efficient, and Comprehensive Phishing Email Detection System [J].
Lee, Jehyun ;
Tang, Farren ;
Ye, Pingxiao ;
Abbasi, Fahim ;
Hay, Phil ;
Divakaran, Dinil Mon .
2021 IEEE EUROPEAN SYMPOSIUM ON SECURITY AND PRIVACY (EUROS&P 2021), 2021, :578-597
[25]  
Marková E, 2019, 2019 IEEE 15TH INTERNATIONAL SCIENTIFIC CONFERENCE ON INFORMATICS (INFORMATICS 2019), P279, DOI [10.1109/Informatics47936.2019.9119329, 10.1109/informatics47936.2019.9119329]
[26]   Convolutional Neural Network Optimization for Phishing Email Classification [J].
McGinley, Cameron ;
Monroy, Sergio A. Salinas .
2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, :5609-5613
[27]   Context based NLP framework of textual tagging for low resource language [J].
Mishra, Atul ;
Shaikh, Soharab Hossain ;
Sanyal, Ratna .
MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (25) :35655-35670
[28]  
Montalbano E., SNAKE KEYLOGGER SPRE
[29]  
Naidu C., 2021, SPAM CLASSIFICATION
[30]  
Nass S.J., 2009, The value, importance, and oversight of health research