A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees
Phishing performs by trying to trick the victim into accessing any computing information which looks original, then instructing them to send important data to unrestricted/unwanted privacy resource. For prevention, it is essential to develop a phishing detection system. Recent phishing detection sys...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
|
| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_01018.pdf |
| _version_ | 1797343942369345536 |
|---|---|
| author | Ogonji Duncan Eric O. Wilson Cheruiyot Mwangi Waweru |
| author_facet | Ogonji Duncan Eric O. Wilson Cheruiyot Mwangi Waweru |
| author_sort | Ogonji Duncan Eric O. |
| collection | DOAJ |
| description | Phishing performs by trying to trick the victim into accessing any computing information which looks original, then instructing them to send important data to unrestricted/unwanted privacy resource. 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 require collection of previous phishing attack logs, analyze them and create a list of such activities and block traffic from such sources. But this is a cumbersome task because the data size is very large, continue changing and dynamic nature. [1]. Instead of using single algorithm approach it would be better to use a hybrid approach. A hybrid approach would be better at mitigating phishing attacks because classification of different format of data is handled; whether the intruder want to use images or textural input to gain into another user system for phishing. Hybrid recommendation decision tress enhances any of machine learning and deep learning algorithms performance. The decision path of the model followed a series of if/else/then statements that connect the predicted class from the root of the tree through the branches of the tree to detect true positive and false negatives of phishing attempts. 10 decision trees were considered and used the features to train the recommendation decision regression model. The developed hybrid recommendation decision tree approach provided an overall true positive rate of the model of 92.28 % and false negative rate is 7.4%. |
| first_indexed | 2024-03-08T10:55:07Z |
| format | Article |
| id | doaj.art-3c4c1216f6d246c3870d04b418806f41 |
| institution | Directory Open Access Journal |
| issn | 2271-2097 |
| language | English |
| last_indexed | 2024-03-08T10:55:07Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | ITM Web of Conferences |
| spelling | doaj.art-3c4c1216f6d246c3870d04b418806f412024-01-26T16:34:27ZengEDP SciencesITM Web of Conferences2271-20972023-01-01570101810.1051/itmconf/20235701018itmconf_icaect2023_01018A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision TreesOgonji Duncan Eric O.0Wilson Cheruiyot1Mwangi Waweru2School of Computing and Information Technology, Jomo Kenyatta University of Agriculture and technologySchool of Computing and Information Technology, Jomo Kenyatta University of Agriculture and technologySchool of Computing and Information Technology, Jomo Kenyatta University of Agriculture and technologyPhishing performs by trying to trick the victim into accessing any computing information which looks original, then instructing them to send important data to unrestricted/unwanted privacy resource. 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 require collection of previous phishing attack logs, analyze them and create a list of such activities and block traffic from such sources. But this is a cumbersome task because the data size is very large, continue changing and dynamic nature. [1]. Instead of using single algorithm approach it would be better to use a hybrid approach. A hybrid approach would be better at mitigating phishing attacks because classification of different format of data is handled; whether the intruder want to use images or textural input to gain into another user system for phishing. Hybrid recommendation decision tress enhances any of machine learning and deep learning algorithms performance. The decision path of the model followed a series of if/else/then statements that connect the predicted class from the root of the tree through the branches of the tree to detect true positive and false negatives of phishing attempts. 10 decision trees were considered and used the features to train the recommendation decision regression model. The developed hybrid recommendation decision tree approach provided an overall true positive rate of the model of 92.28 % and false negative rate is 7.4%.https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_01018.pdfphishingdecision treedetectionhybridattack |
| spellingShingle | Ogonji Duncan Eric O. Wilson Cheruiyot Mwangi Waweru A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees ITM Web of Conferences phishing decision tree detection hybrid attack |
| title | A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees |
| title_full | A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees |
| title_fullStr | A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees |
| title_full_unstemmed | A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees |
| title_short | A Hybrid Model for Detecting Phishing Attack Using Recommedation Decision Trees |
| title_sort | hybrid model for detecting phishing attack using recommedation decision trees |
| topic | phishing decision tree detection hybrid attack |
| url | https://www.itm-conferences.org/articles/itmconf/pdf/2023/07/itmconf_icaect2023_01018.pdf |
| work_keys_str_mv | AT ogonjiduncanerico ahybridmodelfordetectingphishingattackusingrecommedationdecisiontrees AT wilsoncheruiyot ahybridmodelfordetectingphishingattackusingrecommedationdecisiontrees AT mwangiwaweru ahybridmodelfordetectingphishingattackusingrecommedationdecisiontrees AT ogonjiduncanerico hybridmodelfordetectingphishingattackusingrecommedationdecisiontrees AT wilsoncheruiyot hybridmodelfordetectingphishingattackusingrecommedationdecisiontrees AT mwangiwaweru hybridmodelfordetectingphishingattackusingrecommedationdecisiontrees |