Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms

In recent years, the use of machine learning and data mining technologies has drawn researchers’ attention to new ways to improve the performance of Intrusion Detection Systems (IDS). These techniques have proven to be an effective method in distinguishing malicious network packets. One of the most challenging problems that researchers are faced with is the transformation of data into a form that can be handled effectively by Machine Learning Algorithms (MLA). In this paper, we present an IDS model based on the decision tree C4.5 algorithm with transforming simulated UNSW-NB15 dataset as a pre-processing operation. Our model uses Term Frequency.Inverse Document Frequency (TF.IDF) to convert data types to an acceptable and efficient form for machine learning to achieve high detection performance. The model has been tested with randomly selected 250000 records of the UNSW-NB15 dataset. Selected records have been grouped into various segment sizes, like 50, 500, 1000, and 5000 items. Each segment has been, further, grouped into two subsets of multi and binary class datasets. The performance of the Decision Tree C4.5 algorithm with Multilayer Perceptron (MLP) and Naive Bayes (NB) has been compared in Weka software. Our proposed method significantly has improved the accuracy of classifiers and decreased incorrectly detected instances. The increase in accuracy reflects the efficiency of transforming the dataset with TF.IDF of various segment sizes.

Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms

In recent years, the use of machine learning and data mining technologies has drawn researchers’ attention to new ways to improve the performance of Intrusion Detection Systems (IDS). These techniques have proven to be an effective method in distinguishing malicious network packets. One of the most challenging problems that researchers are faced with is the transformation of data into a form that can be handled effectively by Machine Learning Algorithms (MLA). In this paper, we present an IDS model based on the decision tree C4.5 algorithm with transforming simulated UNSW-NB15 dataset as a pre-processing operation. Our model uses Term Frequency.Inverse Document Frequency (TF.IDF) to convert data types to an acceptable and efficient form for machine learning to achieve high detection performance. The model has been tested with randomly selected 250000 records of the UNSW-NB15 dataset. Selected records have been grouped into various segment sizes, like 50, 500, 1000, and 5000 items. Each segment has been, further, grouped into two subsets of multi and binary class datasets. The performance of the Decision Tree C4.5 algorithm with Multilayer Perceptron (MLP) and Naive Bayes (NB) has been compared in Weka software. Our proposed method significantly has improved the accuracy of classifiers and decreased incorrectly detected instances. The increase in accuracy reflects the efficiency of transforming the dataset with TF.IDF of various segment sizes.

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Bibtex @araştırma makalesi { politeknik693221, journal = {Politeknik Dergisi}, eissn = {2147-9429}, address = {Gazi Üniversitesi Teknoloji Fakültesi 06500 Teknikokullar - ANKARA}, publisher = {Gazi Üniversitesi}, year = {2021}, volume = {24}, number = {4}, pages = {1691 - 1698}, doi = {10.2339/politeknik.693221}, title = {Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms}, key = {cite}, author = {Awadh, Khaldoon and Akbaş, Ayhan} }
APA Awadh, K. & Akbaş, A. (2021). Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms . Politeknik Dergisi , 24 (4) , 1691-1698 . DOI: 10.2339/politeknik.693221
MLA Awadh, K. , Akbaş, A. "Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms" . Politeknik Dergisi 24 (2021 ): 1691-1698 <
Chicago Awadh, K. , Akbaş, A. "Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms". Politeknik Dergisi 24 (2021 ): 1691-1698
RIS TY - JOUR T1 - Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms AU - Khaldoon Awadh , Ayhan Akbaş Y1 - 2021 PY - 2021 N1 - doi: 10.2339/politeknik.693221 DO - 10.2339/politeknik.693221 T2 - Politeknik Dergisi JF - Journal JO - JOR SP - 1691 EP - 1698 VL - 24 IS - 4 SN - -2147-9429 M3 - doi: 10.2339/politeknik.693221 UR - Y2 - 2020 ER -
EndNote %0 Politeknik Dergisi Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms %A Khaldoon Awadh , Ayhan Akbaş %T Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms %D 2021 %J Politeknik Dergisi %P -2147-9429 %V 24 %N 4 %R doi: 10.2339/politeknik.693221 %U 10.2339/politeknik.693221
ISNAD Awadh, Khaldoon , Akbaş, Ayhan . "Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms". Politeknik Dergisi 24 / 4 (Aralık 2021): 1691-1698 .
AMA Awadh K. , Akbaş A. Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms. Politeknik Dergisi. 2021; 24(4): 1691-1698.
Vancouver Awadh K. , Akbaş A. Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms. Politeknik Dergisi. 2021; 24(4): 1691-1698.
IEEE K. Awadh ve A. Akbaş , "Intrusion Detection Model Based on TF.IDF and C4.5 Algorithms", Politeknik Dergisi, c. 24, sayı. 4, ss. 1691-1698, Ara. 2021, doi:10.2339/politeknik.693221
Politeknik Dergisi
  • ISSN: 1302-0900
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 1998

64.9b9.5b

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