Dataset

This dataset was created by amrikkatoch308

Contents

NSL-KDD is a data set suggested to solve some of the inherent problems of the KDD'99 data set . Although, this new version of the KDD data set still suffers from some of the problems discussed by McHugh and may not be a perfect representative of existing real networks, because of the lack of public data sets for network-based IDSs, we believe it still can be applied as an effective benchmark data set to help researchers compare different intrusion detection methods.

Furthermore, the number of records in the NSL-KDD train and test sets are reasonable. This advantage makes it affordable to run the experiments on the complete set without the need to randomly select a small portion. Consequently, evaluation results of different research work will be consistent and comparable.

Data files

KDDTrain+.ARFF: The full NSL-KDD train set with binary labels in ARFF format
KDDTrain+.TXT: The full NSL-KDD train set including attack-type labels and difficulty level in CSV format
KDDTrain+_20Percent.ARFF: A 20% subset of the KDDTrain+.arff file
KDDTrain+_20Percent.TXT: A 20% subset of the KDDTrain+.txt file
KDDTest+.ARFF: The full NSL-KDD test set with binary labels in ARFF format
KDDTest+.TXT: The full NSL-KDD test set including attack-type labels and difficulty level in CSV format
KDDTest-21.ARFF: A subset of the KDDTest+.arff file which does not include records with difficulty level of 21 out of 21
KDDTest-21.TXT: A subset of the KDDTest+.txt file which does not include records with difficulty level of 21 out of 21
; cic@unb.ca.

SI1_Supporting Information file (docx) brings together detailed information on the outstanding models obtained for each dataset analyzed in this study such as statistical and training parameters and outliers. There can be found the responses in spikes/s of the mosquito Culex quinquefasciatus to the 50 IRs. Besides, there is presented a full table of the up-to-date studies related to QSAR and insect repellency.

SI2_EXP1_50IRs from Liu et al (2013) SDF file presents the structures of each of the 50 IRs analyzed.

SI3_EXP2_Datasets gathers the four datasets as SDF files from Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009) used for the repellency modeling in EXP2.

SI4_EXP3_Prospective analysis provides Malaria Box Library (400 compounds) as an SDF file, which were analyzed in our virtual screening to prospect potential virtual hits.

SI5_QuBiLS-MIDAS MDs lists contain three TXT lists of 3D molecular descriptors used in QuBiLS-MIDAS to describe the molecules used in the present study.

SI6_EXP1_Sensillar Modeling comprises two subfolders: Classification and Regression models for each of the six sensilla. Models built to predict the physiological interaction experimentally obtained from Liu et al. (2013). All of the models are implemented in the software SiLiS-PAPACS. Every single folder compiles a DOCX file with the detailed description of the model, an XLSX file with the output obtained from the training in Weka 3.9.4, an ARFF, and CSV files with the MDs for each molecule, and the SDF of the study dataset.

SI7_EXP2_Repellency Modeling encompasses the four datasets in the study: Oliferenko et al. (2013), Gaudin et al. (2008), Omolo et al. (2004), and Paluch et al. (2009). Inside the subfolders, there are three models per type of MDs (duplex, triple, generic, and mix) selected that best predict each dataset. As well as the SI6 folder, each model includes six files: DOCX, XLSX, ARFF, CSV, and an SDF.

SI8_Virtual Hits includes the cluster analysis results and physico-chemical properties of new IR virtual leads.

FANTASIAThis repository contains the data related to image descriptors and sound associated with a selection of frames of the films Fantasia and Fantasia 2000 produced by DisneyAboutThis repository contains the data used in the article Automatic composition of descriptive music: A case study of the relationship between image and sound published in the 6th International Workshop on Computational Creativity, Concept Invention, and General Intelligence (C3GI). Data structure is explained in detail in the article. AbstractHuman beings establish relationships with the environment mainly through sight and hearing. This work focuses on the concept of descriptive music, which makes use of sound resources to narrate a story. The Fantasia film, produced by Walt Disney was used in the case study. One of its musical pieces is analyzed in order to obtain the relationship between image and music. This connection is subsequently used to create a descriptive musical composition from a new video. Naive Bayes, Support Vector Machine and Random Forest are the three classifiers studied for the model induction process. After an analysis of their performance, it was concluded that Random Forest provided the best solution; the produced musical composition had a considerably high descriptive quality. DataNutcracker_data.arff: Image descriptors and the most important sound of each frame from the fragment "The Nutcracker Suite" in film Fantasia. Data stored into ARFF format.Firebird_data.arff: Image descriptors of each frame from the fragment "The Firebird" in film Fantasia 2000. Data stored into ARFF format.Firebird_midi_prediction.csv: Frame number of the fragment "The Firebird" in film Fantasia 2000 and the sound predicted by the system encoded in MIDI. Data stored into CSV format.Firebird_prediction.mp3: Audio file with the synthesizing of the prediction data for the fragment "The Firebird" of film Fantasia 2000.LicenseData is available under MIT License. To make use of the data the article must be cited.

NSL-KDD

The data set is a data set that converts the arff File provided by the link into CSV and results. The data set is personally stored by converting data to float64. If you want to obtain additional original files, they are organized in the Original Directory in the repo.

  Labels

The label of the data set is as follows.

# Column Non-Null Count Dtype

0 duration 151165 non-null int64

1 protocol_type 151165 non-null object

2 service 151165 non-null… See the full description on the dataset page: https://huggingface.co/datasets/Mireu-Lab/NSL-KDD.

This dataset is taken from UCI Phishing Dataset originally in ARFF format, converted into CSV. This dataset can be used to train and validate Phishing Detection Machine Learning Projects

This repository contains the data used in the article Convolutional neural networks and transfer learning applied to automatic composition of descriptive music published in the 15th International Conference on Distributed Computing and Artificial Intelligence (DCAI). Data structure is explained in detail in the article. This proposal is the continuation of an earlier work whose data are available in a GitHub repository.AbstractVisual and musical arts has been strongly interconnected throughout history. The aim of this work is to compose music on the basis of the visual characteristics of a video. For this purpose, descriptive music is used as a link between image and sound and a video fragment of film Fantasia is deeply analyzed. Specially, convolutional neural networks in combination with transfer learning are applied in the process of extracting image descriptors. In order to establish a relationship between the visual and musical information, Naive Bayes, Support Vector Machine and Random Forest classifiers are applied. The obtained model is subsequently employed to compose descriptive music from a new video. The results of this proposal are compared with those of an antecedent work in order to evaluate the performance of the classifiers and the quality of the descriptive musical composition.DATAtrain_data.arff: Image descriptors and the most important sound of each frame from the fragment "The Nutcracker Suite" in film Fantasia obtained by means of CNNs. Data stored into ARFF format.test_data.arff: Image descriptors of each frame from the fragment "The Firebird" in film Fantasia 2000 obtained by means of CNNs. Data stored into ARFF format.midi.csv: Frame number of the fragment "The Firebird" in film Fantasia 2000 and the sound predicted by the system encoded in MIDI. Data stored into CSV format.firebird_prediction.mp3: Audio file with the synthesizing of the prediction data for the fragment "The Firebird" of film Fantasia 2000.LICENSEData is available under MIT License. To make use of the data the article must be cited.