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Data correlation matrix-based spam URL detection using machine learning algorithms

Yıl 2024, Sayı: 056, 56 - 69, 31.03.2024

Funda Akar

https://doi.org/10.59313/jsr-a.1422913

Öz

In recent years, the widespread availability of internet access has brought both advantages and disadvantages. Users now enjoy numerous benefits, including unlimited access to vast amounts of information and seamless communication with others. However, this accessibility also exposes users to various threats, including malicious software and deceptive practices, leading to victimization of many individuals. Common issues encountered include spam emails, fake websites, and phishing attempts. Given the essential nature of internet usage in contemporary society, the development of systems to protect users from such malicious activities has become imperative. Accordingly, this study utilized eight prominent machine learning algorithms to identify spam URLs using a large dataset. Since the dataset only contained URL information and spam classification, additional feature extractions such as URL length and the number of digits were necessary. The inclusion of such features enhances decision-making processes within the framework of machine learning, resulting in more efficient detection. As the effectiveness of feature extraction significantly impacts the results of the methods, the study initially conducted feature extraction and trained models based on the weight of features. This paper proposes a data correlated matrix approach for spam URL detection using machine learning algorithms. The distinctive aspect of this study lies in the feature extraction process applied to the dataset, aimed at discerning the most impactful features, and subsequently training models while considering the weighting of these features. The entire dataset was used without any reduction in data. Experimental findings indicate that tree-based machine learning algorithms yield superior results. Among all applied methods, the Random Forest approach achieved the highest success rate, with a detection rate of 96.33% for the non-spam class. Additionally, a combined and weighted calculation method yielded an accuracy of 94.16% for both spam and non-spam data.

Anahtar Kelimeler

classification machine learning Spam Detection Tree-based Algorithms

Teşekkür

Makale yayım sürecinde emeği geçecek olan tüm hocalara saygılar

Kaynakça

  • [1] R. S. Arslan, “Kötücül Web Sayfalarının Tespitinde Doc2Vec Modeli ve Makine Öğrenmesi Yaklaşımı,” European Journal of Science and Technology, no. 27, pp. 792–801, 2021, doi: 10.31590/ejosat.981450.
  • [2] D. Sahoo, C. Liu, and S. C. H. Hoi, “Malicious URL Detection using Machine Learning: A Survey,” ArXiv, vol. abs/1701.0, 2017.
  • [3] P. Kolari, A. Java, T. Finin, T. Oates, and A. Joshi, “Detecting spam blogs: A machine learning approach,” Proceedings of the National Conference on Artificial Intelligence, vol. 2, pp. 1351–1356, 2006.
  • [4] F. O. Catak, K. Sahinbas, and V. Dörtkarde\cs, “Malicious URL detection using machine learning,” Artificial intelligence paradigms for smart cyber-physical systems, IGI Global, pp. 160–180, 2021.
  • [5] A. Begum and S. Badugu, “A study of malicious url detection using machine learning and heuristic approaches,” Advances in Decision Sciences, Image Processing, Security and Computer Vision, Springer, pp. 587–597, 2020.
  • [6] S. Kumar, X. Gao, I. Welch, and M. Mansoori, “A machine learning based web spam filtering approach,” 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016, pp. 973–980.
  • [7] P. Parekh, K. Parmar, and P. Awate, “Spam URL detection and image spam filtering using machine learning,” Computer Engineering, 2018.
  • [8] M. Aljabri et al., “Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions,” IEEE Access, vol. 10, no. October, pp. 121395–121417, 2022, doi: 10.1109/ACCESS.2022.3222307.
  • [9] I. Hernández, C. R. Rivero, D. Ruiz, and R. Corchuelo, “CALA: ClAssifying Links Automatically based on their URL,” Journal of Systems and Software, vol. 115, pp. 130–143, 2016.
  • [10] C.-M. Chen, J.-J. Huang, and Y.-H. Ou, “Efficient suspicious URL filtering based on reputation,” Journal of Information Security and Applications, vol. 20, pp. 26–36, 2015.
  • [11] T. Manyumwa, P. F. Chapita, H. Wu, and S. Ji, “Towards Fighting Cybercrime: Malicious URL Attack Type Detection using Multiclass Classification,” 2020 IEEE International Conference on Big Data (Big Data), pp. 1813–1822, 2020.
  • [12] D. K. McGrath and M. Gupta, “Behind Phishing: An Examination of Phisher Modi Operandi.,” LEET, vol. 8, p. 4, 2008.
  • [13] J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Identifying suspicious URLs: an application of large-scale online learning,” Proceedings of the 26th annual international conference on machine learning, pp. 681–688, 2009.
  • [14] H. Kwon, M. B. Baig, and L. Akoglu, “A domain-agnostic approach to spam-url detection via redirects,” Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 220–232, 2017.
  • [15] Y. Takata, M. Akiyama, T. Yagi, T. Hariu, and S. Goto, “Minespider: Extracting urls from environment-dependent drive-by download attacks,” 2015 IEEE 39th Annual Computer Software and Applications Conference, vol. 2, pp. 444–449, 2015.
  • [16] R. Almeida and C. Westphall, “Heuristic phishing detection and URL checking methodology based on scraping and web crawling,” 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 1–6, 2020.
  • [17] R. S. Rao and A. R. Pais, “Detection of phishing websites using an efficient feature-based machine learning framework,” Neural Computing and Applications, vol. 31, no. 8, pp. 3851–3873, 2019.
  • [18] R. Raj and S. S. Kang, “Spam and Non-Spam URL Detection using Machine Learning Approach,” 2022 3rd International Conference for Emerging Technology (INCET), pp. 1–6, 2022.
  • [19] Q. Abu Al-Haija and M. Al-Fayoumi, “An intelligent identification and classification system for malicious uniform resource locators (URLs),” Neural Computing and Applications, vol. 35, no. 23, pp. 16995–17011, 2023, doi: 10.1007/s00521-023-08592-z.
  • [20] Kaggle, “Spam URLs Classification Dataset.” https://www.kaggle.com/datasets/shivamb/spam-url-prediction.
  • [21] A. Hmimou and others, “On the computation of the correlation matrix implied by a recursive path model,” 2020 IEEE 6th International Conference on Optimization and Applications (ICOA), pp. 1–5, 2020.
  • [22] S. Sperandei, “Understanding logistic regression analysis,” Biochemia medica, vol. 24, no. 1, pp. 12–18, 2014.
  • [23] D. Maulud and A. M. Abdulazeez, “A review on linear regression comprehensive in machine learning,” Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 140–147, 2020.
  • [24] J. Chen et al., “A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide,” Environment international, vol. 130, p. 104934, 2019.
  • [25] S. R. Safavian and D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE transactions on systems, man, and cybernetics, vol. 21, no. 3, pp. 660–674, 1991.
  • [26] Y. K. Qawqzeh, M. M. Otoom, and F. Al-Fayez, “A Proposed Decision Tree Classifier for Atherosclerosis Prediction and Classification,” International Journal of Computer Science and Network Security (IJCSNS), vol. 19, no. 12, pp. 197–202, 2019.
  • [27] B. Charbuty and A. Abdulazeez, “Classification based on decision tree algorithm for machine learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, 2021.
  • [28] L. Breiman, “Random forests; uc berkeley tr567,” University of California: Berkeley, CA, USA, 1999.
  • [29] J. R. Quinlan, C4. 5: programs for machine learning, Elsevier, 2014.
  • [30] L. Breiman, J. Friedman, C. Stone, and R. Olshen, “Classification and regression trees (crc, boca raton, fl),” 1984.
  • [31] I. Rish and others, “An empirical study of the naive Bayes classifier,” IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 3, no. 22, pp. 41–46, 2001.
  • [32] E. Frank and R. R. Bouckaert, “Naive bayes for text classification with unbalanced classes,” Knowledge Discovery in Databases: PKDD 2006: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases Berlin, Germany, September 18-22, 2006 Proceedings 10, pp. 503–510, 2006.
  • [33] Ö. Şahinaslan, H. Dalyan, and E. Şahinaslan, “Naive bayes sınıflandırıcısı kullanılarak youtube verileri üzerinden çok dilli duygu analizi,” Bilişim Teknolojileri Dergisi, vol. 15, no. 2, pp. 221–229, 2022.
  • [34] Y. Wu, K. Ianakiev, and V. Govindaraju, “Improved k-nearest neighbor classification,” Pattern recognition, vol. 35, no. 10, pp. 2311–2318, 2002.
  • [35] G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “KNN Model-Based Approach in Classification,” On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, pp. 986–996, 2003.
  • [36] T. Chen et al., “Xgboost: extreme gradient boosting,” R package version 0.4-2, vol. 1, no. 4, pp. 1–4, 2015.
  • [37] A. Asselman, M. Khaldi, and S. Aammou, “Enhancing the prediction of student performance based on the machine learning XGBoost algorithm,” Interactive Learning Environments, pp. 1–20, 2021.
  • [38] T.-K. An and M.-H. Kim, “A new diverse AdaBoost classifier,” 2010 International conference on artificial intelligence and computational intelligence, vol. 1, pp. 359–363, 2010.
  • [39] X. Li, L. Wang, and E. Sung, “AdaBoost with SVM-based component classifiers,” Engineering Applications of Artificial Intelligence, vol. 21, no. 5, pp. 785–795, 2008.
  • [40] A. Vezhnevets and V. Vezhnevets, “Modest AdaBoost-teaching AdaBoost to generalize better,” Graphicon, vol. 12, no. 5, pp. 987–997, 2005.
  • [41] J. Son, I. Jung, K. Park, and B. Han, “Tracking-by-segmentation with online gradient boosting decision tree,” Proceedings of the IEEE international conference on computer vision, pp. 3056–3064, 2015.
  • [42] S. Peter, F. Diego, F. A. Hamprecht, and B. Nadler, “Cost efficient gradient boosting,” Advances in neural information processing systems, vol. 30, 2017.

Yıl 2024, Sayı: 056, 56 - 69, 31.03.2024

Funda Akar

https://doi.org/10.59313/jsr-a.1422913

Öz

Kaynakça

  • [1] R. S. Arslan, “Kötücül Web Sayfalarının Tespitinde Doc2Vec Modeli ve Makine Öğrenmesi Yaklaşımı,” European Journal of Science and Technology, no. 27, pp. 792–801, 2021, doi: 10.31590/ejosat.981450.
  • [2] D. Sahoo, C. Liu, and S. C. H. Hoi, “Malicious URL Detection using Machine Learning: A Survey,” ArXiv, vol. abs/1701.0, 2017.
  • [3] P. Kolari, A. Java, T. Finin, T. Oates, and A. Joshi, “Detecting spam blogs: A machine learning approach,” Proceedings of the National Conference on Artificial Intelligence, vol. 2, pp. 1351–1356, 2006.
  • [4] F. O. Catak, K. Sahinbas, and V. Dörtkarde\cs, “Malicious URL detection using machine learning,” Artificial intelligence paradigms for smart cyber-physical systems, IGI Global, pp. 160–180, 2021.
  • [5] A. Begum and S. Badugu, “A study of malicious url detection using machine learning and heuristic approaches,” Advances in Decision Sciences, Image Processing, Security and Computer Vision, Springer, pp. 587–597, 2020.
  • [6] S. Kumar, X. Gao, I. Welch, and M. Mansoori, “A machine learning based web spam filtering approach,” 2016 IEEE 30th International Conference on Advanced Information Networking and Applications (AINA), 2016, pp. 973–980.
  • [7] P. Parekh, K. Parmar, and P. Awate, “Spam URL detection and image spam filtering using machine learning,” Computer Engineering, 2018.
  • [8] M. Aljabri et al., “Detecting Malicious URLs Using Machine Learning Techniques: Review and Research Directions,” IEEE Access, vol. 10, no. October, pp. 121395–121417, 2022, doi: 10.1109/ACCESS.2022.3222307.
  • [9] I. Hernández, C. R. Rivero, D. Ruiz, and R. Corchuelo, “CALA: ClAssifying Links Automatically based on their URL,” Journal of Systems and Software, vol. 115, pp. 130–143, 2016.
  • [10] C.-M. Chen, J.-J. Huang, and Y.-H. Ou, “Efficient suspicious URL filtering based on reputation,” Journal of Information Security and Applications, vol. 20, pp. 26–36, 2015.
  • [11] T. Manyumwa, P. F. Chapita, H. Wu, and S. Ji, “Towards Fighting Cybercrime: Malicious URL Attack Type Detection using Multiclass Classification,” 2020 IEEE International Conference on Big Data (Big Data), pp. 1813–1822, 2020.
  • [12] D. K. McGrath and M. Gupta, “Behind Phishing: An Examination of Phisher Modi Operandi.,” LEET, vol. 8, p. 4, 2008.
  • [13] J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Identifying suspicious URLs: an application of large-scale online learning,” Proceedings of the 26th annual international conference on machine learning, pp. 681–688, 2009.
  • [14] H. Kwon, M. B. Baig, and L. Akoglu, “A domain-agnostic approach to spam-url detection via redirects,” Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 220–232, 2017.
  • [15] Y. Takata, M. Akiyama, T. Yagi, T. Hariu, and S. Goto, “Minespider: Extracting urls from environment-dependent drive-by download attacks,” 2015 IEEE 39th Annual Computer Software and Applications Conference, vol. 2, pp. 444–449, 2015.
  • [16] R. Almeida and C. Westphall, “Heuristic phishing detection and URL checking methodology based on scraping and web crawling,” 2020 IEEE International Conference on Intelligence and Security Informatics (ISI), pp. 1–6, 2020.
  • [17] R. S. Rao and A. R. Pais, “Detection of phishing websites using an efficient feature-based machine learning framework,” Neural Computing and Applications, vol. 31, no. 8, pp. 3851–3873, 2019.
  • [18] R. Raj and S. S. Kang, “Spam and Non-Spam URL Detection using Machine Learning Approach,” 2022 3rd International Conference for Emerging Technology (INCET), pp. 1–6, 2022.
  • [19] Q. Abu Al-Haija and M. Al-Fayoumi, “An intelligent identification and classification system for malicious uniform resource locators (URLs),” Neural Computing and Applications, vol. 35, no. 23, pp. 16995–17011, 2023, doi: 10.1007/s00521-023-08592-z.
  • [20] Kaggle, “Spam URLs Classification Dataset.” https://www.kaggle.com/datasets/shivamb/spam-url-prediction.
  • [21] A. Hmimou and others, “On the computation of the correlation matrix implied by a recursive path model,” 2020 IEEE 6th International Conference on Optimization and Applications (ICOA), pp. 1–5, 2020.
  • [22] S. Sperandei, “Understanding logistic regression analysis,” Biochemia medica, vol. 24, no. 1, pp. 12–18, 2014.
  • [23] D. Maulud and A. M. Abdulazeez, “A review on linear regression comprehensive in machine learning,” Journal of Applied Science and Technology Trends, vol. 1, no. 4, pp. 140–147, 2020.
  • [24] J. Chen et al., “A comparison of linear regression, regularization, and machine learning algorithms to develop Europe-wide spatial models of fine particles and nitrogen dioxide,” Environment international, vol. 130, p. 104934, 2019.
  • [25] S. R. Safavian and D. Landgrebe, “A survey of decision tree classifier methodology,” IEEE transactions on systems, man, and cybernetics, vol. 21, no. 3, pp. 660–674, 1991.
  • [26] Y. K. Qawqzeh, M. M. Otoom, and F. Al-Fayez, “A Proposed Decision Tree Classifier for Atherosclerosis Prediction and Classification,” International Journal of Computer Science and Network Security (IJCSNS), vol. 19, no. 12, pp. 197–202, 2019.
  • [27] B. Charbuty and A. Abdulazeez, “Classification based on decision tree algorithm for machine learning,” Journal of Applied Science and Technology Trends, vol. 2, no. 01, pp. 20–28, 2021.
  • [28] L. Breiman, “Random forests; uc berkeley tr567,” University of California: Berkeley, CA, USA, 1999.
  • [29] J. R. Quinlan, C4. 5: programs for machine learning, Elsevier, 2014.
  • [30] L. Breiman, J. Friedman, C. Stone, and R. Olshen, “Classification and regression trees (crc, boca raton, fl),” 1984.
  • [31] I. Rish and others, “An empirical study of the naive Bayes classifier,” IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 3, no. 22, pp. 41–46, 2001.
  • [32] E. Frank and R. R. Bouckaert, “Naive bayes for text classification with unbalanced classes,” Knowledge Discovery in Databases: PKDD 2006: 10th European Conference on Principles and Practice of Knowledge Discovery in Databases Berlin, Germany, September 18-22, 2006 Proceedings 10, pp. 503–510, 2006.
  • [33] Ö. Şahinaslan, H. Dalyan, and E. Şahinaslan, “Naive bayes sınıflandırıcısı kullanılarak youtube verileri üzerinden çok dilli duygu analizi,” Bilişim Teknolojileri Dergisi, vol. 15, no. 2, pp. 221–229, 2022.
  • [34] Y. Wu, K. Ianakiev, and V. Govindaraju, “Improved k-nearest neighbor classification,” Pattern recognition, vol. 35, no. 10, pp. 2311–2318, 2002.
  • [35] G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “KNN Model-Based Approach in Classification,” On The Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE, pp. 986–996, 2003.
  • [36] T. Chen et al., “Xgboost: extreme gradient boosting,” R package version 0.4-2, vol. 1, no. 4, pp. 1–4, 2015.
  • [37] A. Asselman, M. Khaldi, and S. Aammou, “Enhancing the prediction of student performance based on the machine learning XGBoost algorithm,” Interactive Learning Environments, pp. 1–20, 2021.
  • [38] T.-K. An and M.-H. Kim, “A new diverse AdaBoost classifier,” 2010 International conference on artificial intelligence and computational intelligence, vol. 1, pp. 359–363, 2010.
  • [39] X. Li, L. Wang, and E. Sung, “AdaBoost with SVM-based component classifiers,” Engineering Applications of Artificial Intelligence, vol. 21, no. 5, pp. 785–795, 2008.
  • [40] A. Vezhnevets and V. Vezhnevets, “Modest AdaBoost-teaching AdaBoost to generalize better,” Graphicon, vol. 12, no. 5, pp. 987–997, 2005.
  • [41] J. Son, I. Jung, K. Park, and B. Han, “Tracking-by-segmentation with online gradient boosting decision tree,” Proceedings of the IEEE international conference on computer vision, pp. 3056–3064, 2015.
  • [42] S. Peter, F. Diego, F. A. Hamprecht, and B. Nadler, “Cost efficient gradient boosting,” Advances in neural information processing systems, vol. 30, 2017.
Toplam 42 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Makine Öğrenme (Diğer), Yapay Zeka (Diğer)
Bölüm Research Articles
Yazarlar

Funda Akar 0000-0001-9376-8710

Yayımlanma Tarihi 31 Mart 2024
Gönderilme Tarihi 20 Ocak 2024
Kabul Tarihi 21 Şubat 2024
Yayımlandığı Sayı Yıl 2024 Sayı: 056

Kaynak Göster

IEEE F. Akar, “Data correlation matrix-based spam URL detection using machine learning algorithms”, JSR-A, sy. 056, ss. 56–69, Mart 2024, doi: 10.59313/jsr-a.1422913.
 Kapak Resmi İndir