TY  - JOUR
N1  - cited By 5; Conference of 5th Computational Methods in Systems and Software, CoMeSySo 2021 ; Conference Date: 1 October 2021 Through 1 October 2021; Conference Code:268789
N2  - The development of web and internet technology has resulted in its application in a wide range of services. This has resulted in an increase in the number of cybersecurity issues over the years, the most famous of which is the phishing attack, in which hostile websites impersonate genuine websites to acquire naïve usersâ?? data required for illegal access. Current mitigation measures, including anti-phishing software and machine learning (ML) approach, have proven to be successful in identifying phishing operations. Hackers, on the other hand, are coming up with new techniques to get around these counter-measures. Nonetheless, given the dynamism of phishing efforts, there is a constant requirement for novel and efficient website phishing detection solutions. In this study, an optimized decision forest (ODF) method for detecting website phishing is proposed ODF involves the use of a genetic algorithm (GA) for the selection of optimal diverse individual trees in a forest to generate an efficient sub-forest. Specifically, accurate and diverse trees from a decision forest are passed into GA as an initial population to generate a more robust forest with high efficacy. The performance of the proposed ODF is evaluated using three phishing datasets from the UCI repository. Findings from the experimental results revealed that ODF performed better than selected baseline classifiers. Particularly, ODF recorded a high detection accuracy (98.37), AUC (0.999), f-measure (0.98), MCC (0.967) values with a low false-positive rate (0.016). In addition, ODF outperformed some existing ML-based phishing attack models. Consequently, the proposed ODF method is recommended for dealing with sophisticated phishing attacks. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
SP  - 568
ID  - scholars15614
TI  - Optimized Decision Forest for Website Phishing Detection
AV  - none
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120689064&doi=10.1007%2f978-3-030-90321-3_47&partnerID=40&md5=456b754af9aa5d92230c7532745d536a
A1  - Balogun, A.O.
A1  - Mojeed, H.A.
A1  - Adewole, K.S.
A1  - Akintola, A.G.
A1  - Salihu, S.A.
A1  - Bajeh, A.O.
A1  - Jimoh, R.G.
JF  - Lecture Notes in Networks and Systems
VL  - 231 LN
EP  - 582
Y1  - 2021///
SN  - 23673370
PB  - Springer Science and Business Media Deutschland GmbH
ER  -