Hybrid Phishing Detecting with Recommendation Decision Trees
Duncan Eric O. Ogonji1, Cheruiyot Wilson2, Waweru Mwangi3
1Duncan Eric O. Ogonji, School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and TechnologyNairobi, Kenya.
2Cheruiyot Wilson, School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology Nairobi, Kenya.
3Prof. Waweru Mwangi, School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and Technology Nairobi, Kenya.
Manuscript received on 22 June 2024 | Revised Manuscript received on 28 June 2024 | Manuscript Accepted on 15 July 2024 | Manuscript published on 30 July 2024 | PP: 32-35 | Volume-13 Issue-2, July 2024 | Retrieval Number: 100.1/ijrte.B812013020724 | DOI: 10.35940/ijrte.B8120.13020724
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Phishing is performed by trying to trick the victim into accessing any computing information that looks original and then instructing them to send necessary data to unauthorised/private resources. For prevention, it is essential to develop a phishing detection system. Recent phishing detection systems are based on data mining and machine learning techniques. Most of the related work literature requires the collection of previous phishing attack logs, analysing them to create a list of such activities, and blocking traffic from these sources. However, this is a cumbersome task because the data size is enormous, continues changing, and is dynamic. [1]. Instead of using a single algorithm approach, it would be better to use a hybrid approach. A hybrid approach would be more effective at mitigating phishing attacks because it handles the classification of different data formats, whether the intruder uses images or textual input to gain access to another user’s system for phishing purposes. Hybrid recommendation decision trees enhance the performance of machine learning and deep learning algorithms. The decision path of the model followed a series of if/else/then statements that connected the predicted class from the root of the tree through the branches to detect true positives and false negatives of phishing attempts. Ten decision trees were considered, and the features were used to train the recommendation decision regression model. The developed hybrid recommendation decision tree approach yielded an overall actual positive rate of 92.28% and a false negative rate of 7.72%.
Keywords: Phishing, Decision Tree, Detection, Hybrid, Attack
Scope of the Article: Computer Science and Applications