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HFSTE: Hybrid Feature Selections and Tree-Based Classifiers Ensemble for Intrusion Detection System
TAMA, Bayu Adhi,RHEE, Kyung-Hyune 'Institute of Electronics, Information and Communi 2017 IEICE transactions on information and systems Vol.100e.d No.8
<P>Anomaly detection is one approach in intrusion detection systems (IDSs) which aims at capturing any deviation from the profiles of normal network activities. However, it suffers from high false alarm rate since it has impediment to distinguish the boundaries between normal and attack profiles. In this paper, we propose an effective anomaly detection approach by hybridizing three techniques, i.e. particle swarm optimization (PSO), ant colony optimization (ACO), and genetic algorithm (GA) for feature selection and ensemble of four tree-based classifiers, i.e. random forest (RF), naive bayes tree (NBT), logistic model trees (LMT), and reduces error pruning tree (REPT) for classification. Proposed approach is implemented on NSL-KDD dataset and from the experimental result, it significantly outperforms the existing methods in terms of accuracy and false alarm rate.</P>
A Comparative Study of Phishing Websites Classification Based on Classifier Ensembles
Bayu Adhi Tama,이경현 한국멀티미디어학회 2018 멀티미디어학회논문지 Vol.21 No.5
Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.
Learning to Prevent Inactive Student of Indonesia Open University
Tama, Bayu Adhi Korea Information Processing Society 2015 Journal of information processing systems Vol.11 No.2
The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.
Learning to Prevent Inactive Student of Indonesia Open University
( Bayu Adhi Tama ) 한국정보처리학회 2015 Journal of information processing systems Vol.11 No.2
The inactive student rate is becoming a major problem in most open universities worldwide. In Indonesia, roughly 36% of students were found to be inactive, in 2005. Data mining had been successfully employed to solve problems in many domains, such as for educational purposes. We are proposing a method for preventing inactive students by mining knowledge from student record systems with several state of the art ensemble methods, such as Bagging, AdaBoost, Random Subspace, Random Forest, and Rotation Forest. The most influential attributes, as well as demographic attributes (marital status and employment), were successfully obtained which were affecting student of being inactive. The complexity and accuracy of classification techniques were also compared and the experimental results show that Rotation Forest, with decision tree as the base-classifier, denotes the best performance compared to other classifiers.
Bayu Adhi Tama,김도현,김규원,김수환,이승철 대한이비인후과학회 2020 Clinical and Experimental Otorhinolaryngology Vol.13 No.4
This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.
A Comparative Study of Phishing Websites Classification Based on Classifier Ensembles
Tama, Bayu Adhi,Rhee, Kyung-Hyune Korea Multimedia Society 2018 The journal of multimedia information system Vol.5 No.2
Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.
A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network
Bayu Adhi Tama,이경현 한국정보처리학회 2017 Journal of information processing systems Vol.13 No.5
Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computinginfrastructure. It intelligently detects malicious and predicts future attack patterns based on the classificationanalysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluateclassifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting andstacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), andsupport vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally,we conduct two statistical significance tests to evaluate the performance differences among classifiers.
A Detailed Analysis of Classifier Ensembles for Intrusion Detection in Wireless Network
Tama, Bayu Adhi,Rhee, Kyung-Hyune Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.5
Intrusion detection systems (IDSs) are crucial in this overwhelming increase of attacks on the computing infrastructure. It intelligently detects malicious and predicts future attack patterns based on the classification analysis using machine learning and data mining techniques. This paper is devoted to thoroughly evaluate classifier ensembles for IDSs in IEEE 802.11 wireless network. Two ensemble techniques, i.e. voting and stacking are employed to combine the three base classifiers, i.e. decision tree (DT), random forest (RF), and support vector machine (SVM). We use area under ROC curve (AUC) value as a performance metric. Finally, we conduct two statistical significance tests to evaluate the performance differences among classifiers.
A Comparative Study of Phishing Websites Classification Based on Classifier Ensemble
Tama, Bayu Adhi,Rhee, Kyung-Hyune Korea Multimedia Society 2018 멀티미디어학회논문지 Vol.21 No.5
Phishing website has become a crucial concern in cyber security applications. It is performed by fraudulently deceiving users with the aim of obtaining their sensitive information such as bank account information, credit card, username, and password. The threat has led to huge losses to online retailers, e-business platform, financial institutions, and to name but a few. One way to build anti-phishing detection mechanism is to construct classification algorithm based on machine learning techniques. The objective of this paper is to compare different classifier ensemble approaches, i.e. random forest, rotation forest, gradient boosted machine, and extreme gradient boosting against single classifiers, i.e. decision tree, classification and regression tree, and credal decision tree in the case of website phishing. Area under ROC curve (AUC) is employed as a performance metric, whilst statistical tests are used as baseline indicator of significance evaluation among classifiers. The paper contributes the existing literature on making a benchmark of classifier ensembles for web phishing detection.