Evolutionary Algorithm with Deep Auto Encoder Network Based Website Phishing Detection and Classification

被引:3
作者
Alqahtani, Hamed [1 ]
Alotaibi, Saud S. [2 ]
Alrayes, Fatma S. [3 ]
Al-Turaiki, Isra [4 ]
Alissa, Khalid A. [5 ]
Aziz, Amira Sayed A. [6 ]
Maray, Mohammed [7 ]
Al Duhayyim, Mesfer [8 ]
机构
[1] King Khalid Univ, Coll Comp Sci, Ctr Artificial Intelligence, Dept Informat Syst,Unit Cybersecur, Abha 62529, Saudi Arabia
[2] Umm Al Qura Univ, Coll Comp & Informat Syst, Dept Informat Syst, Mecca 24382, Saudi Arabia
[3] Princess Nourah Bint Abdulrahman Univ, Coll Comp & Informat Sci, Dept Informat Syst, POB 84428, Riyadh 11671, Saudi Arabia
[4] King Saud Univ, Coll Comp & Informat Sci, Dept Informat Technol, POB 145111, Riyadh 4545, Saudi Arabia
[5] Imam Abdulrahman Bin Faisal Univ, Coll Comp Sci & Informat Technol, Networks & Commun Dept, SAUDI ARAMCO Cybersecur Chair, POB 1982, Dammam 31441, Saudi Arabia
[6] Future Univ Egypt, Fac Comp & Informat Technol, Dept Digital Media, New Cairo 11835, Egypt
[7] King Khalid Univ, Coll Comp Sci, Dept Informat Syst, Abha 62529, Saudi Arabia
[8] Prince Sattam Bin Abdulaziz Univ, Coll Sci & Humanities Aflaj, Dept Comp Sci, Al Kharj 16278, Saudi Arabia
来源
APPLIED SCIENCES-BASEL | 2022年 / 12卷 / 15期
关键词
cybersecurity; internet of things; cloud computing; computational models; deep learning; metaheuristics; phishing detection; website phishing;
D O I
10.3390/app12157441
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Website phishing is a cyberattack that targets online users for stealing their sensitive data containing login credential and banking details. The phishing websites appear very similar to their equivalent legitimate websites for attracting a huge amount of Internet users. The attacker fools the user by offering the masked webpage as legitimate or reliable for retrieving its important information. Presently, anti-phishing approaches necessitate experts to extract phishing site features and utilize third-party services for phishing website detection. These techniques have some drawbacks, as the requirement of experts for extracting phishing features is time consuming. Many solutions for phishing websites attack have been presented, such as blacklist or whitelist, heuristics, and machine learning (ML) based approaches, which face difficulty in accomplishing effectual recognition performance due to the continual improvements of phishing technologies. Therefore, this study presents an optimal deep autoencoder network based website phishing detection and classification (ODAE-WPDC) model. The proposed ODAE-WPDC model applies input data pre-processing at the initial stage to get rid of missing values in the dataset. Then, feature extraction and artificial algae algorithm (AAA) based feature selection (FS) are utilized. The DAE model with the received features carried out the classification process, and the parameter tuning of the DAE technique was performed using the invasive weed optimization (IWO) algorithm to accomplish enhanced performance. The performance validation of the ODAE-WPDC technique was tested using the Phishing URL dataset from the Kaggle repository. The experimental findings confirm the better performance of the ODAE-WPDC model with maximum accuracy of 99.28%.
引用
收藏
页数:16
相关论文
共 29 条
[1]   Intelligent phishing detection scheme using deep learning algorithms [J].
Adebowale, Moruf Akin ;
Lwin, Khin T. ;
Hossain, M. A. .
JOURNAL OF ENTERPRISE INFORMATION MANAGEMENT, 2023, 36 (03) :747-766
[2]   Optimal Machine Learning Based Privacy Preserving Blockchain Assisted Internet of Things with Smart Cities Environment [J].
Al-Qarafi, A. ;
Alrowais, Fadwa ;
Alotaibi, S. Saud ;
Nemri, Nadhem ;
Al-Wesabi, Fahd N. ;
Al Duhayyim, Mesfer ;
Marzouk, Radwa ;
Othman, Mahmoud ;
Al-Shabi, M. .
APPLIED SCIENCES-BASEL, 2022, 12 (12)
[3]   A novel framework for prognostic factors identification of malignant mesothelioma through association rule mining [J].
Alam, Talha Mahboob ;
Shaukat, Kamran ;
Hameed, Ibrahim A. ;
Khan, Wasim Ahmad ;
Sarwar, Muhammad Umer ;
Iqbal, Farhat ;
Luo, Suhuai .
BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2021, 68
[4]  
Crawford M, 2015, J BIG DATA-GER, V2, DOI [10.1186/s40537-015-0029-9, 10.1186/s40537-015-0029-9]
[5]   Deep Learning for Phishing Detection: Taxonomy, Current Challenges and Future Directions [J].
Do, Nguyet Quang ;
Selamat, Ali ;
Krejcar, Ondrej ;
Herrera-Viedma, Enrique ;
Fujita, Hamido .
IEEE ACCESS, 2022, 10 :36429-36463
[6]   Cyber Threat Intelligence-Based Malicious URL Detection Model Using Ensemble Learning [J].
Ghaleb, Fuad A. ;
Alsaedi, Mohammed ;
Saeed, Faisal ;
Ahmad, Jawad ;
Alasli, Mohammed .
SENSORS, 2022, 22 (09)
[7]   A machine learning based approach for phishing detection using hyperlinks information [J].
Jain, Ankit Kumar ;
Gupta, B. B. .
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (05) :2015-2028
[8]  
kaggle, about us
[9]  
Kocer HG, 2018, International Journal of Intelligent Systems and Applications in Engineering, V6, P306, DOI [10.18201/ijisae.2018448458, 10.18201/ijisae.2018448458]
[10]   Catching Transparent Phish: Analyzing and Detecting MITM Phishing Toolkits [J].
Kondracki, Brian ;
Azad, Babak Amin ;
Starov, Oleksii ;
Nikiforakis, Nick .
CCS '21: PROCEEDINGS OF THE 2021 ACM SIGSAC CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, 2021, :36-50