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Evaluating the Impact of data preprocessing techniques on the Performance of Intrusion Detection Systems

JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2024年第2期32卷 36-36页

作者： Santos, Kelson Carvalho Miani, Rodrigo Sanches Silva, Flavio de Oliveira Fed Inst Piaui IFPI Comp Dept Ave Pedro Freitas1020 BR-64018000 Teresina PI Brazil Fed Univ Uberlandia UFU Fac Comp FACOM Ave Joao Naves Avila2121 Campus Santa Monica BR-38400902 Uberlandia MG Brazil Univ Minho Sch Engn Dept Informat DI R Univ P-4710057 Braga Portugal

The development of Intrusion Detection Systems using Machine Learning techniques (ML-based IDS) has emerged as an important research topic in the cybersecurity field. However, there is a noticeable absence of systematic studies to comprehend the usability of such systems in real-world applications. This paper analyzes the impact of data preprocessing techniques on the performance of ML-based IDS using two public datasets, UNSW-NB15 and CIC-IDS2017. Specifically, we evaluated the effects of data cleaning, encoding, and normalization techniques on the performance of binary and multiclass intrusion detection models. This work investigates the impact of data preprocessing techniques on the performance of ML-based IDS and how the performance of different ML-based IDS is affected by data preprocessing techniques. To this end, we implemented a machine learning pipeline to apply the data preprocessing techniques in different scenarios to answer such questions. The findings analyzed using the Friedman statistical test and Nemenyi post-hoc test revealed significant differences in groups of data preprocessing techniques and ML-based IDS, according to the evaluation metrics. However, these differences were not observed in multiclass scenarios for data preprocessing techniques. Additionally, ML-based IDS exhibited varying performances in binary and multiclass classifications. Therefore, our investigation presents insights into the efficacy of different data preprocessing techniques for building robust and accurate intrusion detection models.

关键词： data preprocessing techniques IDS Intrusion detection system Machine learning ML-based IDS Statistical test

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Trade-Space Exploration With data preprocessing and Machine Learning for Satellite Anomalies Reliability Classification

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IEEE ACCESS 2025年 13卷 35903-35921页

作者： Mutholib, Abdul Rahim, Nadirah Abdul Gunawan, Teddy Surya Kartiwi, Mira Int Islamic Univ Malaysia Fac Engn Dept Elect & Comp Engn Kuala Lumpur 53100 Malaysia UIN Syarif Hidayatullah Jakarta Fac Sci & Technol Dept Informat Syst Banten 15412 Indonesia Int Islamic Univ Malaysia Fac Informat & Commun Technol Dept Informat Syst Kuala Lumpur 53100 Malaysia

Satellite reliability is critical to ensuring uninterrupted operations in aerospace systems, where anomalies can lead to mission failures and significant economic losses. Existing anomaly classification methods often lack scalability, interpretability, and adaptability to diverse datasets. This study introduces the Trade-Space Exploration Machine Learning (TSE-ML) framework, a comprehensive pipeline for satellite anomaly classification that optimizes preprocessing, transformation, normalization, and machine learning stages. Leveraging a Seradata dataset spanning 66 years and 4,455 satellite records, the framework systematically evaluates four data cleaning methods, four data transformation techniques, five normalization strategies, and seven machine learning algorithms across 480 configurations. The optimal configuration, comprising Iterative Imputation, FastText, Robust Scaling, and Decision Tree, achieved the highest testing accuracy of 95.74% with competitive computational efficiency. The Decision Tree model delivered superior accuracy and provided interpretability, revealing critical factors influencing satellite anomalies, such as Age Since Launch, Design Life, and Orbit Category. Stratified 5-fold cross-validation ensured robustness and generalizability of the results. The TSE-ML framework's transparency and high performance enable actionable insights for improving satellite design, operational planning, and anomaly mitigation. Future research will focus on real-time anomaly detection, integrating satellite telemetry data, and extending the framework to other space applications. This study establishes a robust, interpretable foundation for advancing anomaly classification in aerospace engineering, addressing the dual challenges of reliability and operational efficiency.

关键词： Satellites Reliability Accuracy Anomaly detection Space vehicles data preprocessing data models Planetary orbits Computational modeling Real-time systems Satellite anomaly detection satellite reliability classification trade-space exploration data preprocessing techniques machine learning models seradata dataset decision support systems

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Evaluating the Performance of Several data preprocessing Methods Based on GRU in Forecasting Monthly Runoff Time Series

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WATER RESOURCES MANAGEMENT 2024年第9期38卷 3135-3152页

作者： Wang, Wen-chuan Du, Yu-jin Chau, Kwok-wing Cheng, Chun-Tian Xu, Dong-mei Zhuang, Wen-Tao North China Univ Water Resources & Elect Power Coll Water Resources Zhengzhou 450046 Peoples R China Yellow River Conservancy Commiss Xifeng Hydrol & Water Resources Survey Bur Qingyang 745099 Peoples R China Hong Kong Polytech Univ Dept Civil & Environm Engn Hung Hom Kowloon Hong Kong Peoples R China Dalian Univ Technol Inst Hydropower Syst & Hydroinformat Dalian 116024 Peoples R China

The optimal planning and management of modern water resources depends highly on reliable and accurate runoff forecasting. data preprocessing technology can provide new possibilities for improving the accuracy of runoff forecasting when basic physical relationships cannot be captured using a single prediction model. Yet, few studies have evaluated the performances of various data preprocessing technologies in predicting monthly runoff time series so far. In order to fill this research gap, this paper investigates the potential of five data preprocessing techniques based on the gated recurrent unit network (GRU) model for monthly runoff prediction, namely variational mode decomposition (VMD), wavelet packet decomposition (WPD), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), extreme-point symmetric mode decomposition (ESMD), and singular spectrum analysis (SSA). In this study, the original monthly runoff data is first decomposed into a set of subcomponents using five data preprocessing methods;second, each component is predicted by developing an appropriate GRU model;and finally, the forecasting results of different two-stage hybrid models are obtained by aggregating the forecast results of the corresponding subcomponents. Four performance metrics are employed as the evaluation benchmarks. The experimental results from two Hydropower Stations in China show that five data preprocessing techniques can attain satisfying prediction results, while VMD and WPD methods can yield better performance than CEEMDAN, ESMD, and SSA in both training and testing periods in terms of four indexes. Indeed, it is significantly important to carefully determine an appropriate data preprocessing method according to the actual characteristics of the study area.

关键词： Monthly runoff prediction data preprocessing techniques Gated recurrent unit network Variational mode decomposition Wavelet packet decomposition

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A fuzzy series-parallel preprocessing (FSPP) based hybrid model for wind forecasting

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第3期16卷 430-452页

作者： Ahmadi, Mehrnaz Khashei, Mehdi Isfahan Univ Technol IUT Dept Ind & Syst Engn Esfahan *** Iran

Wind power is one of the most important renewable energy sources that is widely used in many developed and developing countries. However, it is generally stated in the literature that providing accurate forecasts for large-scale planning purposes is not a simple task, especially by single models. It is the main reason for this fact that why researchers in recent years have sought to propose hybrid models for increasing the accuracy of predictions. In general, choosing the appropriate type and the number of components, as well as the proper type of hybridization methodology, are the most effective factors in the performance of the developed hybrid models. Although in the literature, numerous attempts have been made in order to answer these questions, there is no general consensus on this matter. For this reason, the main idea of this paper is to concurrently combine different hybrid methodologies as well as different single models in order to benefit from the advantages of these models and methodologies, simultaneously. In this way, three well-known and widely used hybrid methodologies, including the preprocessing, the series, and the parallel methodologies, are combined together by incorporating the linear/nonlinear and certain/uncertain components. In addition, in the proposed model, a new process is proposed based on the complex/uncertain modelling to model the preprocessing phase residuals, which have been ignored in the modelling procedures. In this way, in the first stage of the proposed model, the data is preprocessed by the Kalman filter as a preprocessing approach in order to divide data into two groups of trend and residual patterns. The trend data provided in the previous step, with the original data, are simultaneously considered input data of an autoregressive integrated moving average as the certain linear model and a multilayer perceptron as the certain nonlinear model for certain linear and nonlinear modeling of patterns. This step is repeated for the

关键词： autoregressive integrated moving average with explanatory variable (ARIMAX) data preprocessing techniques Kalman filter multi-layer perceptrons (MLPs) series-parallel structures wind power forecasting

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Agroinsights Chatbot: Ai-Driven Precision Farming For Optimal Yields, Crop Selection, And Disease-Free Harvests 3

Agroinsights Chatbot: Ai-Driven Precision Farming For Optima...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Geethajali, T.M. Prithviraj, B. Prajwal, K.M. Prajwal Gowda, C.M. Priyanka Visvesvaraya Technological University Pes College of Engineering Mandya Dept. Information Science and Engineering Mandya India

ISBN: (纸本)9798350364569

The Agriculture AI-based chatbot presented offers a ground-breaking answer to farming challenges by fusing cutting-edge technologies. Through meticulous planning and execution, including data collection, preprocessing, and model selection, it solves significant challenges including agricultural yield projection, disease detection, and recommendations. The chatbot employs a range of machine learning and deep learning techniques to provide farmers with useful advice using an easy-to-use interface built with HTML, CSS, and Flask. A trained natural language processing (NLP) chatbot model increases user engagement and facilitates easier access to agricultural information. This comprehensive approach promotes the sustainability and prosperity of the global agriculture sector while boosting production and streamlining farming practices. © 2024 IEEE.

关键词： Agricultural Sustainability Artificial Intelligence (AI) in Agriculture Chatbot Technology Climate Sensitivity Crop Disease Identification Crop Health Monitoring Crop Yield Prediction data preprocessing techniques Decision Support Systems Environmental Impact Mitigation Fertilizer Recommendation Machine Learning Models Optimal Crop Selection Precision Agriculture Soil Analysis

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Remote sensing based rain fall prediction using big data assisted integrated routing framework

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年 1-11页

作者： Refonaa, J. Lakshmi, M. Sathyabama Inst Sci & Technol Dept Comp Sci & Engn Chennai 600119 Tamil Nadu India SRM Inst Sci & Technol Coll Engn & Technol Dept Comp Sci & Engn SRM Nagar Kattankulathur 603203 India

At present, soft computing technology is the latest, most challenging technology used in prediction analysis of weather parameters. Therefore, the accuracy of the prediction in the area of rainfall analysis is very important. However, present methods in predicting rainfalls have less accuracy. In many cases, weather prediction models function poorly. Machine Learning methods ignore the effect of physical factors that make rainfall forecasting. It improves the accuracy of rainfall prediction and analysis and aims to predict rainfall with the related air density, relative humidity and temperature parameters. The most recent advanced remote sensing techniques with Artificial Neural Network model and Fuzzy Logic Control (FLC) associated with big data Assisted Integrated Routing and Surplus Memory (BIRSM) model has been developed with the available India Meteorological Department (IMD) data for the year from to data preprocessing techniques are applied to the volume of available data. An appropriate and error-reducing evaluation for rainfall prediction is performed in combination with the proposed BIRSM model and Artificial Neural Network. Accurate and time series prediction can be very helpful for agriculture and flood management etc.

关键词： Soft computing techniques Rainfall data preprocessing techniques Fuzzy logic control Artificial neural network

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Current Status and Challenges and Future Trends of Deep Learning-Based Intrusion Detection Models

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JOURNAL OF IMAGING 2024年第10期10卷 254-254页

作者： Wu, Yuqiang Zou, Bailin Cao, Yifei Nanjing Police Univ Coll Informat & Technol Nanjing 210023 Peoples R China Nanjing Agr Univ Coll Artificial Intelligence Nanjing 210095 Peoples R China Anhui Polytech Univ Sch Elect Engn Wuhu 241000 Peoples R China

With the advancement of deep learning (DL) technology, DL-based intrusion detection models have emerged as a focal point of research within the domain of cybersecurity. This paper provides an overview of the datasets frequently utilized in the research. This article presents an overview of the widely utilized datasets in the research, establishing a basis for future investigation and analysis. The text subsequently summarizes the prevalent data preprocessing methods and feature engineering techniques utilized in intrusion detection. Following this, it provides a review of seven deep learning-based intrusion detection models, namely, deep autoencoders, deep belief networks, deep neural networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, and transformers. Each model is examined from various dimensions, highlighting their unique architectures and applications within the context of cybersecurity. Furthermore, this paper broadens its scope to include intrusion detection techniques facilitated by the following two large-scale predictive models: the BERT series and the GPT series. These models, leveraging the power of transformers and attention mechanisms, have demonstrated remarkable capabilities in understanding and processing sequential data. In light of these findings, this paper concludes with a prospective outlook on future research directions. Four key areas have been identified for further research. By addressing these issues and advancing research in the aforementioned areas, this paper envisions a future in which DL-based intrusion detection systems are not only more accurate and efficient but also better aligned with the dynamic and evolving landscape of cybersecurity threats.

关键词： deep learning intrusion detection models cybersecurity research data preprocessing techniques BERT and GPT models future research directions

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Feasibility study on exhaled-breath analysis by untargeted Selected-Ion Flow-Tube Mass Spectrometry in children with cystic fibrosis, asthma, and healthy controls: Comparison of data pretreatment and classification techniques

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TALANTA 2021年 225卷 122080-122080页

作者： Segers, Karen Slosse, Amorn Viaene, Johan Bannier, Michiel A. G. E. Van de Kant, Kim D. G. Dompeling, Edward Van Eeckhaut, Ann Vercammen, Joeri Vander Heyden, Yvan Vrije Univ Brussel VUB Dept Analyt Chem Appl Chemometr & Mol Modelling Laarbeeklaan 103 B-1090 Brussels Belgium Vrije Univ Brussel VUB Ctr Neurosci C4N Dept Pharmaceut Chem Drug Anal & Drug Informat Laarbeeklaan 103 B-1090 Brussels Belgium Maastricht Univ Med Ctr Sch Publ Hlth & Primary Care Dept Paediat Resp Med Maastricht Netherlands Intersci Expert Ctr IS X Ave Jean Etienne Lenoir 2 B-1348 Louvain La Neuve Belgium Univ Ghent Fac Engn & Architecture Ind Catalysis & Adsorpt Technol INCAT Valentin Vaerwyckweg 1 B-9000 Ghent Belgium

Selected-Ion Flow-Tube Mass Spectrometry (SIFT-MS) has been applied in a clinical context as diagnostic tool for breath samples using target biomarkers. Exhaled breath sampling is non-invasive and therefore much more patient friendly compared to bronchoscopy, which is the golden standard for evaluating airway inflammation. In the actual pilot study, 55 exhaled breath samples of children with asthma, cystic-fibrosis and healthy individuals were included. Rather than focusing on the analysis of target biomarkers or on the identification of biomarkers, different data analysis strategies, including a variety of pretreatment, classification and discrimination techniques, are evaluated regarding their capacity to distinguish the three classes based on subtle differences in their full scan SIFT-MS spectra. Proper data-analysis strategies are required because these full scan spectra contain much external, i.e. unwanted, variation. Each SIFT-MS analysis generates three spectra resulting from ionmolecule reactions of analyte molecules with H3O+, NO+ and O-2(+). Models were built with Linear Discriminant Analysis, Quadratic Discriminant Analysis, Soft Independent Modelling by Class Analogy, Partial Least Squares - Discriminant Analysis, K-Nearest Neighbours, and Classification and Regression Trees. Perfect models, concerning overall sensitivity and specificity (100% for both) were found using Direct Orthogonal Signal Correction (DOSC) pretreatment. Given the uncertainty related to the classification models associated with DOSC pretreatments (i.e. good classification found also for random classes), other models are built applying other preprocessing approaches. A Partial Least Squares - Discriminant Analysis model with a combined pre-processing method considering single value imputation results in 100% sensitivity and specificity for calibration, but was less good predictive. Pareto scaling prior to Quadratic Discriminant Analysis resulted in 41/55 correctly classified samples

关键词： Exhaled breath analysis Selected-Ion Flow-Tube Mass Spectrometry Principal Component Analysis Classification and discrimination data preprocessing techniques

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preprocessing compensation techniques for improved classification of imbalanced medical datasets

Preprocessing compensation techniques for improved classific...

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Federated Conference on Computer Science and Information Systems (FedCSIS)

作者： Wosiak, Agnieszka Karbowiak, Sylwia Lodz Univ Technol Inst Informat Technol Ul Wolczanska 215 PL-90924 Lodz Poland

ISBN: (纸本)9788394625375

The paper describes the study on the problem of applying classification techniques in medical datasets with a class imbalance. The aim of the research is to identify factors that negatively affect classification results and propose actions that may be taken to improve the performance. To alleviate the impact of uneven and complex class distribution, methods of balancing the datasets are proposed and compared. The experiments were conducted on five datasets - three binary and two multiclass. They comprise several data preprocessing methods applied on data and the classification with different techniques. The study shows that for some datasets there exists a combination of a certain preprocessing method and a classification technique which outperforms other approaches. For datasets with complex distribution or too many features the ratio of correctly predicted labels may be low regardless what resampling method and classification technique has been applied.

关键词： imbalanced datasets class imbalance medical data analysis data preprocessing techniques

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data-mining massive time series astronomical data: challenges, problems and solutions

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INFORMATION AND SOFTWARE TECHNOLOGY 1999年第9期41卷 545-556页

作者： Ng, MK Huang, Z Univ Hong Kong Dept Math Hong Kong Peoples R China CSIRO CRC ACSys Canberra ACT 2601 Australia

In this paper we present some initial results of a project which uses data-mining techniques to search for evidence of massive compact halo objects (MACHOs) from very large time series database. MACHOs are the proposed materials that probably make the "dark matter" surrounding our own and other galaxies. It was suggested that MACHOs may be detected through the gravitational microlensing effect which can be identified from the light curves of background stars. The objective of this project is two-fold, namely, (i) identification of new classes of variable stars and (ii) detection of microlensing events. In this paper, we present the major characteristics of the time series astronomical data, data preprocessing techniques to process these time series, and some domain-specific techniques to separate candidate variable stars from the nonvariant ones. We discuss the use of the Fourier model to represent the time series and the k-means based clustering method to classify variable stars. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： data mining time series astronomical data data preprocessing techniques

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