Comparative Analysis of K-Nearest Neighbor and Decision Tree in Detecting Distributed Denial of Service

Ramadhan, Ilham; Sukarno, Parman; Nugroho, Muhammad Arief

doi:10.1109/icoict49345.2020.9166380

Cited by 20 publications

(7 citation statements)

References 2 publications

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“…In addition, the selected algorithms have demonstrated effectiveness in various domains and are widely used for regression tasks [20]. Finally, compared to models like ANNs, K-Nearest Neighbors, SVM, and Naive Bayes, the chosen algorithms typically require less computational resources and are easier to implement, making them practical choices for experimentation and deployment [21].…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Machine Learning Models for Aqueous Solubility Prediction in Drug Discovery

Xue,

Zhang,

Liu

2024

Preprint

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Determining the aqueous solubility of the chemical compound is of great importancein-silicodrug discovery. However, correctly and rapidly predicting the aqueous solubility remains a challenging task. This paper explores and evaluates the predictability of multiple machine learning models in the aqueous solubility of compounds. Specifically, we apply a series of machine learning algorithms, including Random Forest, XG-Boost, LightGBM, and CatBoost, on a well-established aqueous solubility dataset (i. e., the Huuskonen dataset) of over 1200 compounds. Experimental results show that even traditional machine learning algorithms can achieve satisfactory performance with high accuracy. In addition, our investigation goes beyond mere prediction accuracy, delving into the interpretability of models to identify key features and understand the molecular properties that influence the predicted outcomes. This study sheds light on the ability to use machine learning approaches to predict compound solubility, significantly shortening the time that researchers spend on new drug discovery.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Machine Learning Models for Aqueous Solubility Prediction in Drug Discovery

Xue,

Zhang,

Liu

2024

Preprint

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“…Ramadhan et al [73] Demonstrated a comparative analysis of accuracy and process length for each algorithm performed using the K-Nearest Neighbor (KNN) and Decision Tree (DT) algorithms for the detection of DDoS attacks. Moreover, they used the CICIDS2017 dataset that consists of the latest attacks and global packages, is standard and applicable to real-world data in a PCAP format.…”

Section: Literature Reviewmentioning

confidence: 99%

“…While the study [75] utilized the DT(XGBoost) and RF on the datasets of the Smokers of the Chinese Center for Disease Control and Prevention, it was found that, again, the DT approach achieved the highest accuracy; which is 84.11%. Furthermore, based on studies [73], [78], [83] using DT and KNN in the CICIDS2017, RNA-seq Malaria, and Wisconsin Breast Cancer datasets, it was found that the DT approach had the highest accuracy in all three studies. Also, its accuracy was higher when using the CICIDS2017 datasets that achieved 99.91% accuracy.…”

Section: IVmentioning

confidence: 99%

Classification Based on Decision Tree Algorithm for Machine Learning

Charbuty

Abdulazeez

2021

JASTT

1,022

211

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Decision tree classifiers are regarded to be a standout of the most well-known methods to data classification representation of classifiers. Different researchers from various fields and backgrounds have considered the problem of extending a decision tree from available data, such as machine study, pattern recognition, and statistics. In various fields such as medical disease analysis, text classification, user smartphone classification, images, and many more the employment of Decision tree classifiers has been proposed in many ways. This paper provides a detailed approach to the decision trees. Furthermore, paper specifics, such as algorithms/approaches used, datasets, and outcomes achieved, are evaluated and outlined comprehensively. In addition, all of the approaches analyzed were discussed to illustrate the themes of the authors and identify the most accurate classifiers. As a result, the uses of different types of datasets are discussed and their findings are analyzed.

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“…In another study [20], the researchers proposed a structure that can be used to make fast decisions in situations by testing the various features of a decision tree known as Screen Content Coding. Ramadhan et al, [21] presented a comparative analysis of the performance of the different algorithms. For the detection of distributed denial of service attacks (DDoS), a method using the K-Nearest Neighbor and DT algorithms was developed.…”

Section: Literaturementioning

confidence: 99%

Experimental Testing of Primary User Detection Using Decision Tree Algorithm With Software Defined Radio Testbed

Reddy

Deevi

2022

Preprint

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The concept of cognitive radio is a technology that uses radio spectrum resources to provide effective and efficient communication. The spectrum sensing function of this technology is very important in the performance of the network. One of the most common issues that the network encounters is the disruption brought on by the activity of secondary users. To avoid the interference caused by the traffic of secondary users, the network should adopt an efficient algorithm that can identify the presence of primary users. A decision tree is a classification method that can be used to determine the presence of these users. It has the advantages of high learning ability and fast processing speed. This paper presents a data collection method that uses a software defined radio to analyze the signal transmission in a real-time environment. The radio uses USRPN210 as hardware and is supported by the GNU Radio software.

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Comparative Analysis of K-Nearest Neighbor and Decision Tree in Detecting Distributed Denial of Service

Cited by 20 publications

References 2 publications

Evaluation of Machine Learning Models for Aqueous Solubility Prediction in Drug Discovery

Evaluation of Machine Learning Models for Aqueous Solubility Prediction in Drug Discovery

Classification Based on Decision Tree Algorithm for Machine Learning

Experimental Testing of Primary User Detection Using Decision Tree Algorithm With Software Defined Radio Testbed

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