Using Ensemble Method to Detect Attacks in the Recommender System
Shill attacks are a serious threat to the stability of filtering and recommendation systems. These attacks involve the injection of fake profiles into the system, which can compromise the reliability of system output. Several shilling attack detection techniques have been proposed, but they often ha...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10268947/ |
| _version_ | 1797659028584660992 |
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| author | Reda A. Zayed Lamiaa Fattouh Ibrahim Hesham A. Hefny Hesham A. Salman Abdulaziz AlMohimeed |
| author_facet | Reda A. Zayed Lamiaa Fattouh Ibrahim Hesham A. Hefny Hesham A. Salman Abdulaziz AlMohimeed |
| author_sort | Reda A. Zayed |
| collection | DOAJ |
| description | Shill attacks are a serious threat to the stability of filtering and recommendation systems. These attacks involve the injection of fake profiles into the system, which can compromise the reliability of system output. Several shilling attack detection techniques have been proposed, but they often have limitations in terms of accuracy. This works presents a enhanced method for detecting attacks in collaborative recommender systems. The proposed method is based on a combination of statistical and machine learning techniques. The statistical techniques are used to identify anomalous user behavior, while the machine learning techniques are used to classify users as either malicious or benign. The main contribution of the proposed method is the use of a hybrid approach that combines the strengths of statistical and machine learning techniques. The statistical techniques are able to identify anomalous user behavior that is not easily detected by machine learning techniques. The machine learning techniques are able to classify users as either malicious or benign with a high degree of accuracy. The proposed method was evaluated on a real-world dataset. The results showed that the proposed method was able to detect attacks with a high degree of accuracy. The proposed method uses a combination of ensemble learning and feature selection to achieve better accuracy than previous methods. The results of the experiments show that the proposed method can achieve an accuracy of up to 99%. |
| first_indexed | 2024-03-11T18:08:59Z |
| format | Article |
| id | doaj.art-2c2da97174a3452c8b5f134185937e85 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T18:08:59Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-2c2da97174a3452c8b5f134185937e852023-10-16T23:00:28ZengIEEEIEEE Access2169-35362023-01-011111131511132310.1109/ACCESS.2023.332132210268947Using Ensemble Method to Detect Attacks in the Recommender SystemReda A. Zayed0Lamiaa Fattouh Ibrahim1https://orcid.org/0000-0001-5671-8941Hesham A. Hefny2https://orcid.org/0000-0001-5862-675XHesham A. Salman3https://orcid.org/0009-0005-1048-1658Abdulaziz AlMohimeed4https://orcid.org/0009-0005-9862-6398Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, EgyptFaculty of Graduate Studies for Statistical Research, Cairo University, Giza, EgyptFaculty of Graduate Studies for Statistical Research, Cairo University, Giza, EgyptCollege of Informatics, Midocean University, Moroni, ComorosCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaShill attacks are a serious threat to the stability of filtering and recommendation systems. These attacks involve the injection of fake profiles into the system, which can compromise the reliability of system output. Several shilling attack detection techniques have been proposed, but they often have limitations in terms of accuracy. This works presents a enhanced method for detecting attacks in collaborative recommender systems. The proposed method is based on a combination of statistical and machine learning techniques. The statistical techniques are used to identify anomalous user behavior, while the machine learning techniques are used to classify users as either malicious or benign. The main contribution of the proposed method is the use of a hybrid approach that combines the strengths of statistical and machine learning techniques. The statistical techniques are able to identify anomalous user behavior that is not easily detected by machine learning techniques. The machine learning techniques are able to classify users as either malicious or benign with a high degree of accuracy. The proposed method was evaluated on a real-world dataset. The results showed that the proposed method was able to detect attacks with a high degree of accuracy. The proposed method uses a combination of ensemble learning and feature selection to achieve better accuracy than previous methods. The results of the experiments show that the proposed method can achieve an accuracy of up to 99%.https://ieeexplore.ieee.org/document/10268947/Shilling attack detectionprofile injectionrecommender systemmachine learningensemble method |
| spellingShingle | Reda A. Zayed Lamiaa Fattouh Ibrahim Hesham A. Hefny Hesham A. Salman Abdulaziz AlMohimeed Using Ensemble Method to Detect Attacks in the Recommender System IEEE Access Shilling attack detection profile injection recommender system machine learning ensemble method |
| title | Using Ensemble Method to Detect Attacks in the Recommender System |
| title_full | Using Ensemble Method to Detect Attacks in the Recommender System |
| title_fullStr | Using Ensemble Method to Detect Attacks in the Recommender System |
| title_full_unstemmed | Using Ensemble Method to Detect Attacks in the Recommender System |
| title_short | Using Ensemble Method to Detect Attacks in the Recommender System |
| title_sort | using ensemble method to detect attacks in the recommender system |
| topic | Shilling attack detection profile injection recommender system machine learning ensemble method |
| url | https://ieeexplore.ieee.org/document/10268947/ |
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