DYNA JOURNAL ENGINEERING

Key words:

phishing effect, prediction, machine learning, feature representation, scams, Internet of Things, email security, regression model, support vector machine, dataset partitioning, phishing detection, Bald Eagle Eye Optimizer, global optimization, classification, early warning system

Article type:

ARTICULO DE INVESTIGACION / RESEARCH ARTICLE

Section:

RESEARCH ARTICLES

Abstract- Phishing effects have turned to be a most pre-dominant attacks encountered by Internet users especially for users in IoT environment. Many investigators attempt to provide solution which leads to a major prediction disaster. This work proposes a novel Machine Learning (ML) approach to handle Phishing effect on Email over the targeted sectors. Thus, effectual phishing detection is required to needed to trace the phishing effect over the email with a periodic alarm rate. This work proposes a novel regression-based linear support vector model (r-lSV) to mitigate the phishing effect problem and provides awareness by analyzing the phishing features to predict and prevent the phishing scams in its earlier stage. The proposed prediction model partitions the dataset into testing and training to analyze the inherent phishing characteristics over email. The proposed model partitions the phishing and non-phishing using online available dataset. The functionality of the anticipated model is compared with other machine learning approaches. The functionality of the proposed model is optimized using Bald Eagle Eye Optimizer (BEO) to attain global outcomes. The proposed model intends to give superior outcomes based the feature learning significance and classification. The proposed r-lSV establishes better trade-off compared to other approaches.
Keywords- phishing effect, prediction, machine learning, feature representation, scams