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

2020 ◽  
Author(s):  
Ilham Ramadhan ◽  
Parman Sukarno ◽  
Muhammad Arief Nugroho
Keyword(s):  
Comparative Analysis ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Denial Of Service ◽  
Distributed Denial Of Service ◽  
K Nearest Neighbor

Supervised Classifier Approach for Intrusion Detection on KDD with Optimal MapReduce Framework Model in Cloud Computing

2019 ◽  
Vol 12 ◽  
Author(s):  
M. Ilayaraja ◽  
S. Hemalatha ◽  
P. Manickam ◽  
K. Sathesh Kumar ◽  
K. Shankar
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Learning Strategies ◽  
Nearest Neighbor ◽  
Detection System ◽  
K Nearest Neighbor ◽  
Mapreduce Model ◽  
The Web

Cloud computing is characterized as the arrangement of assets or administrations accessible through the web to the clients on their request by cloud providers. It communicates everything as administrations over the web in view of the client request, for example operating system, organize equipment, storage, assets, and software. Nowadays, Intrusion Detection System (IDS) plays a powerful system, which deals with the influence of experts to get actions when the system is hacked under some intrusions. Most intrusion detection frameworks are created in light of machine learning strategies. Since the datasets, this utilized as a part of intrusion detection is Knowledge Discovery in Database (KDD). In this paper detect or classify the intruded data utilizing Machine Learning (ML) with the MapReduce model. The primary face considers Hadoop MapReduce model to reduce the extent of database ideal weight decided for reducer model and second stage utilizing Decision Tree (DT) classifier to detect the data. This DT classifier comprises utilizing an appropriate classifier to decide the class labels for the non-homogeneous leaf nodes. The decision tree fragment gives a coarse section profile while the leaf level classifier can give data about the qualities that influence the label inside a portion. From the proposed result accuracy for detection is 96.21% contrasted with existing classifiers, for example, Neural Network (NN), Naive Bayes (NB) and K Nearest Neighbor (KNN).

scholarly journals A Comparative Analysis of Machine Learning Algorithms Modeled from Machine Vision-Based Lettuce Growth Stage Classification in Smart Aquaponics

2020 ◽  
Vol 11 (9) ◽  
pp. 442-449 ◽  
Author(s):  
Sandy C. Lauguico ◽  
◽  
Ronnie S. Concepcion II ◽  
Jonnel D. Alejandrino ◽  
Rogelio Ruzcko Tobias ◽  
...  
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Machine Vision ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Urban Farming ◽  
K Nearest Neighbor ◽  
Lettuce Growth

The arising problem on food scarcity drives the innovation of urban farming. One of the methods in urban farming is the smart aquaponics. However, for a smart aquaponics to yield crops successfully, it needs intensive monitoring, control, and automation. An efficient way of implementing this is the utilization of vision systems and machine learning algorithms to optimize the capabilities of the farming technique. To realize this, a comparative analysis of three machine learning estimators: Logistic Regression (LR), K-Nearest Neighbor (KNN), and Linear Support Vector Machine (L-SVM) was conducted. This was done by modeling each algorithm from the machine vision-feature extracted images of lettuce which were raised in a smart aquaponics setup. Each of the model was optimized to increase cross and hold-out validations. The results showed that KNN having the tuned hyperparameters of n_neighbors=24, weights='distance', algorithm='auto', leaf_size = 10 was the most effective model for the given dataset, yielding a cross-validation mean accuracy of 87.06% and a classification accuracy of 91.67%.

A Comparison of the Analysis of Methods for Feature Extraction and Classification by Wavelet Transform in SSVEP BCIs

2020 ◽  
Author(s):  
Hoda Heidari ◽  
Zahra Einalou ◽  
Mehrdad Dadgostar ◽  
Hamidreza Hosseinzadeh
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Wavelet Transform ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Iir Filters ◽  
Wide Range ◽  
New Feature

Abstract Most of the studies in the field of Brain-Computer Interface (BCI) based on electroencephalography have a wide range of applications. Extracting Steady State Visual Evoked Potential (SSVEP) is regarded as one of the most useful tools in BCI systems. In this study, different methods such as feature extraction with different spectral methods (Shannon entropy, skewness, kurtosis, mean, variance) (bank of filters, narrow-bank IIR filters, and wavelet transform magnitude), feature selection performed by various methods (decision tree, principle component analysis (PCA), t-test, Wilcoxon, Receiver operating characteristic (ROC)), and classification step applying k nearest neighbor (k-NN), perceptron, support vector machines (SVM), Bayesian, multiple layer perceptron (MLP) were compared from the whole stream of signal processing. Through combining such methods, the effective overview of the study indicated the accuracy of classical methods. In addition, the present study relied on a rather new feature selection described by decision tree and PCA, which is used for the BCI-SSVEP systems. Finally, the obtained accuracies were calculated based on the four recorded frequencies representing four directions including right, left, up, and down.

scholarly journals DDoS-Capable IoT Malwares: Comparative Analysis and Mirai Investigation

2018 ◽  
Vol 2018 ◽  
pp. 1-30 ◽  
Author(s):  
Michele De Donno ◽  
Nicola Dragoni ◽  
Alberto Giaretta ◽  
Angelo Spognardi
Keyword(s):  
Comparative Analysis ◽  
Internet Of Things ◽  
Detailed Analysis ◽  
Real World ◽  
General Framework ◽  
Denial Of Service ◽  
The Internet ◽  
Distributed Denial Of Service ◽  
Ddos Attacks ◽  
The Internet Of Things

The Internet of Things (IoT) revolution has not only carried the astonishing promise to interconnect a whole generation of traditionally “dumb” devices, but also brought to the Internet the menace of billions of badly protected and easily hackable objects. Not surprisingly, this sudden flooding of fresh and insecure devices fueled older threats, such as Distributed Denial of Service (DDoS) attacks. In this paper, we first propose an updated and comprehensive taxonomy of DDoS attacks, together with a number of examples on how this classification maps to real-world attacks. Then, we outline the current situation of DDoS-enabled malwares in IoT networks, highlighting how recent data support our concerns about the growing in popularity of these malwares. Finally, we give a detailed analysis of the general framework and the operating principles of Mirai, the most disruptive DDoS-capable IoT malware seen so far.

scholarly journals Centroid Based Classifier With TF – IDF – ICF for Classfication of Student’s Complaint at Appliation E-Complaint in Muhammadiyah University of Sidoarjo

2016 ◽  
Vol 1 (1) ◽  
pp. 17 ◽  
Author(s):  
Mochamad Alfan Rosid ◽  
Gunawan Gunawan ◽  
Edwin Pramana
Keyword(s):  
Text Mining ◽  
Decision Tree ◽  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
K Nearest Neighbor ◽  
Base Classifier

Text mining mengacu pada proses mengambil informasi berkualitas tinggi dari teks. Informasi berkualitas tinggi biasanya diperoleh melalui peramalan pola dan kecenderungan melalui sarana seperti pembelajaran pola statistik. Salah satu kegiatan penting dalam text mining adalah klasifikasi atau kategorisasi teks. Kategorisasi teks sendiri saat ini memiliki berbagai metode antara lain metode K-Nearest Neighbor, Naïve Bayes, dan Centroid Base Classifier, atau decision tree classification.Pada penelitian ini, klasifikasi keluhan mahasiswa dilakukan dengan metode centroid based classifier dan dengan fitur TF-IDF-ICF, Ada lima tahap yang dilakukan untuk mendapatkan hasil klasifikasi. Tahap pengambilan data keluhan kemudian dilanjutkan dengan tahap preprosesing yaitu mempersiapkan data yang tidak terstruktur sehingga siap digunakan untuk proses selanjutnya, kemudian dilanjutkan dengan proses pembagian data, data dibagi menjadi dua macam yaitu data latih dan data uji, tahap selanjutnya yaitu tahap pelatihan untuk menghasilkan model klasifikasi dan tahap terakhir adalah tahap pengujian yaitu menguji model klasifikasi yang telah dibuat pada tahap pelatihan terhadap data uji. Keluhan untuk pengujian akan diambilkan dari database aplikasi e-complaint Universitas Muhammadiyah Sidoarjo. Adapun hasil uji coba menunjukkan bahwa klasifikasi keluhan dengan algoritma centroid based classifier dan dengan fitur TF-IDF-ICF memiliki rata-rata akurasi yang cukup tinggi yaitu 79.5%. Nilai akurasi akan meningkat dengan meningkatnya data latih dan efesiensi sistem semakin menurun dengan meningkatnya data latih.

Sign in / Sign up

Export Citation Format

Share Document