Multi-class Classification Model Research Articles

Few-shot satellite image classification for bringing deep learning on board OPS-SAT

Bringing artificial intelligence on board Earth observation satellites unlocks unprecedented possibilities to extract actionable items from various image modalities at the global scale in real time. This is of paramount importance nowadays, as downlinking large amounts of imagery is not only prohibitively expensive but also time-consuming. However, building deep learning solutions that could be deployed on board an edge device is challenging due to the limited manually-annotated satellite datasets and hardware constraints of an edge device. This paper addresses these challenges through harnessing a blend of data-centric and model-centric approaches to build a well-generalizing yet efficient and resource-frugal deep learning model for multi-class satellite image classification in the few-shot learning settings. This integrated strategy is formulated to enhance classification performance while accommodating the unique demands of an image analysis chain on board OPS-SAT, a nanosatellite operated by the European Space Agency. The experiments performed over a real-world dataset of OPS-SAT images delves into the interactions between data- and model-centric techniques, underscores the significance of synthesizing artificial training data and emphasizes the value of ensemble learning. However, they also caution against negative transfer in domain adaptation. This study sheds light on effective model training strategies and highlights the multifaceted challenges inherent in deep learning for practical Earth observation, contributing insights to the field of satellite image classification within the constraints of nanosatellite operations. To ensure reproducibility of our study, the implementation is available at https://github.com/ShendoxParadox/Few-shot-satellite-image-classification-OPS-SAT.

Ultrasound-based deep learning radiomics model for differentiating benign, borderline, and malignant ovarian tumours: a multi-class classification exploratory study.

Accurate preoperative identification of ovarian tumour subtypes is imperative for patients as it enables physicians to custom-tailor precise and individualized management strategies. So, we have developed an ultrasound (US)-based multiclass prediction algorithm for differentiating between benign, borderline, and malignant ovarian tumours. We randomised data from 849 patients with ovarian tumours into training and testing sets in a ratio of 8:2. The regions of interest on the US images were segmented and handcrafted radiomics features were extracted and screened. We applied the one-versus-rest method in multiclass classification. We inputted the best features into machine learning (ML) models and constructed a radiomic signature (Rad_Sig). US images of the maximum trimmed ovarian tumour sections were inputted into a pre-trained convolutional neural network (CNN) model. After internal enhancement and complex algorithms, each sample's predicted probability, known as the deep transfer learning signature (DTL_Sig), was generated. Clinical baseline data were analysed. Statistically significant clinical parameters and US semantic features in the training set were used to construct clinical signatures (Clinic_Sig). The prediction results of Rad_Sig, DTL_Sig, and Clinic_Sig for each sample were fused as new feature sets, to build the combined model, namely, the deep learning radiomic signature (DLR_Sig). We used the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) to estimate the performance of the multiclass classification model. The training set included 440 benign, 44 borderline, and 196 malignant ovarian tumours. The testing set included 109 benign, 11 borderline, and 49 malignant ovarian tumours. DLR_Sig three-class prediction model had the best overall and class-specific classification performance, with micro- and macro-average AUC of 0.90 and 0.84, respectively, on the testing set. Categories of identification AUC were 0.84, 0.85, and 0.83 for benign, borderline, and malignant ovarian tumours, respectively. In the confusion matrix, the classifier models of Clinic_Sig and Rad_Sig could not recognise borderline ovarian tumours. However, the proportions of borderline and malignant ovarian tumours identified by DLR_Sig were the highest at 54.55% and 63.27%, respectively. The three-class prediction model of US-based DLR_Sig can discriminate between benign, borderline, and malignant ovarian tumours. Therefore, it may guide clinicians in determining the differential management of patients with ovarian tumours.

Rapid Endoscopic Diagnosis of Benign Ulcerative Colorectal Diseases with an Artificial Intelligence Contextual Framework

Benign ulcerative colorectal diseases (UCDs) such as ulcerative colitis (UC), Crohn's disease (CD), ischemic colitis (IC) and intestinal tuberculosis (ITB) share similar phenotypes with different etiologies and treatment strategies. To accurately diagnose closely-related diseases like UCDs, we hypothesize that contextual learning is critical in enhancing the ability of the artificial intelligence models to differentiate the subtle differences in lesions amidst the vastly divergent spatial contexts. White light colonoscopy datasets of patients with confirmed UCDs and normal controls were retrospectively collected. We developed a Multi-class Contextual Classification (MCC) model that can differentiate among the mentioned UCDs and normal controls by incorporating the tissue object contexts surrounding the individual lesion region in a scene and spatial information from other endoscopic frames (video-level) into a unified framework. Internal and external datasets were used to validate the model's performance. Training datasets included 762 patients, and the internal and external testing cohorts included 257 patients and 293 patients, respectively. Our MCC model provided a rapid reference diagnosis on internal test sets with a high averaged AUROC (image level: 0.950 and video-level: 0.973) and balanced accuracy (image-level: 76.1% and video-level: 80.8%), which was superior to junior endoscopists (accuracy: 71.8%, p<0.0001) and similar to experts (accuracy: 79.7%, p=0.732). The MCC model achieved a AUROC of 0.988 and balanced accuracy of 85.8% using external testing datasets. These results enable this model to fit in the routine endoscopic workflow, and the contextual framework to be adopted for diagnosing other closely-related diseases.

Intelligent diagnosis of the severity of disease conditions in COVID-19 patients based on the LASSO method.

The purpose of this study is to develop an intelligent diagnosis model based on the LASSO method to predict the severity of COVID-19 patients. The study uses the clinical data of 500 COVID-19 patients from a designated hospital in Guangzhou, China, and selects eight features, including age, sex, dyspnea, comorbidity, complication, lymphocytes (LYM), CRP, and lung injury score, as the most important predictors of COVID-19 severity. The study applies the LASSO method to perform feature selection and regularization, and compares the LASSO method with other machine learning methods, such as ridge regression, support vector machine, and random forest. The study finds that the ridge regression model has the best performance among the four models, with an AUROC of 0.92 in the internal validation and 0.91 in the external validation. The study provides a simple, robust, and interpretable model for the intelligent diagnosis of COVID-19 severity, and a convenient and practical tool for the public and the health care workers to assess COVID-19 severity. However, the study also has some limitations and directions for future research, such as the need for more data from different sources and settings, and from prospective, longitudinal, multi-class classification models. The study hopes to contribute to the prevention and control of COVID-19, and to the improvement of the diagnosis and treatment of COVID-19 patients.

An Approach for Detecting Security Attacks using Machine Learning in IoT Environment

The strong security measures are becoming even more necessary to protect these networked systems as a result of the proliferation of Internet of Things (IoT) devices. The dynamic and varied nature of IoT networks frequently makes it impossible for traditional security solutions to be effective. The method suggested in this research uses machine learning to identify security attacks in IoT contexts. The suggested method makes use of the capabilities of machine learning algorithms to examine the enormous amounts of data produced by IoT devices. The system can develop the ability to recognise possible security threats and take immediate action by training models on labelled datasets that include both normal and attack patterns. In this paper multiple ML models for security threat detection in IoT environments in this study. Our evaluation uses both binary and multiclass classification models in an effort to accurately reflect the variety of assaults that can be found in IoT environments. The proposed method provide a new specialised IoT dataset that was created especially to reflect the characteristics of actual IoT environments in order to assure the validity of our assessment. By completing this extensive analysis, we hope to shed light on how well AI-based methods for security attack detection in IoT contexts work. The results of this study can help researchers and professionals decide which ML models and feature engineering techniques are best for IoT security. At the end of the day, we want to help with the creation of reliable security systems that safeguard IoT devices and shield user data from harmful attacks.

A method of classifying IoT devices based on attack sensitivity

The emergence of IoT has introduced new security concerns, particularly the detection of large-scale network attacks initiated by compromised IoT devices. The diverse nature of IoT devices adds complexity to attack detection, necessitating the classification of devices based on their unique characteristics and behavior patterns. This paper presents a novel device classification method grounded in traffic differentials before and after IoT devices succumb to compromise. The study defines the concept of attack sensitivity and designs a method for calculating the value. The K-means algorithm is used to classify devices into predefined categories based on their attack sensitivity values. The effectiveness of this method is demonstrated through experiments on a public dataset. Additionally, a multi-class classification model is developed to identify newly added devices to the IoT environment, with good results in accuracy, precision, recall, F1 score, and FPR (0.999, 0.997–1,0.997–1,0.998–1,0–0.001). Overall, The device classification method proposed in this paper, based on attack sensitivity, not only enables a rational definition of device categories but also ensures precise classification for devices newly integrated into the IoT environment. This lays the foundation for achieving more precise attack detection and protection, advancing the field towards enhanced cybersecurity measures.

Predicting resprouting of Platanus × hispanica following branch pruning by means of machine learning.

Resprouting is a crucial survival strategy following the loss of branches, being it by natural events or artificially by pruning. The resprouting prediction on a physiological basis is a highly complex approach. However, trained gardeners try to predict a tree's resprouting after pruning purely based on their empirical knowledge. In this study, we explore how far such predictions can also be made by machine learning. Table-topped annually pruned Platanus × hispanica trees at a nursery were LiDAR-scanned for two consecutive years. Topological structures for these trees were abstracted by cylinder fitting. Then, new shoots and trimmed branches were labelled on corresponding cylinders. Binary and multiclass classification models were tested for predicting the location and number of new sprouts. The accuracy for predicting whether having or not new shoots on each cylinder reaches 90.8% with the LGBMClassifier, the balanced accuracy is 80.3%. The accuracy for predicting the exact numbers of new shoots with the GaussianNB model is 82.1%, but its balanced accuracy is reduced to 42.9%. The results were validated with a separate dataset, proving the feasibility of resprouting prediction after pruning using this approach. Different tree species, tree forms, and other variables should be addressed in further research.

Machine Learning Based Open Switch Fault Detection and Localization of Inverters

The location and detection of switch faults is a crucial step in improving the dependability of inverters, which is a requirement for critical applications. This paper focuses on a machine learning-based approach for the open-switch fault detection system for cascaded H-bridge (CHB) multilevel inverters (MLI) used in high- and medium-power applications. Each switch failure is taken into account in this analysis, and the problem is used to model logically different machine learning algorithms, namely Gaussian Naive Bayes (GNB), support vector machines (SVM) and multinomial logistic regression (MLR) models for fault identification and localization based on multi-class classification (MCC) methods. This work includes the simulation and hardware-based data collection from five-level CHB-MLI and correlation-based selection of prominent features in the final dataset. The results of three MCC models for all possible combinations of predictors (features) prove that the mean voltage of bridges is the dominant feature for fault detection in the CHB inverter. Other major findings are machine learning-assisted fault identification and diagnosis is undoubtedly accurate and significantly more promising in condition monitoring and fault diagnosis in CHB-MLIs. The results reveal 100% accuracy in fault diagnosis. Therefore, the presented work confirms switch fault detection in CHB-MLI with mean value as dominant fault feature along with machine learning techniques simple, robust and accurate fault detection.

Vision Transformers, Ensemble Model, and Transfer Learning Leveraging Explainable AI for Brain Tumor Detection and Classification.

The abnormal growth of malignant or nonmalignant tissues in the brain causes long-term damage to the brain. Magnetic resonance imaging (MRI) is one of the most common methods of detecting brain tumors. To determine whether a patient has a brain tumor, MRI filters are physically examined by experts after they are received. It is possible for MRI images examined by different specialists to produce inconsistent results since professionals formulate evaluations differently. Furthermore, merely identifying a tumor is not enough. To begin treatment as soon as possible, it is equally important to determine the type of tumor the patient has. In this paper, we consider the multiclass classification of brain tumors since significant work has been done on binary classification. In order to detect tumors faster, more unbiased, and reliably, we investigated the performance of several deep learning (DL) architectures including Visual Geometry Group 16 (VGG16), InceptionV3, VGG19, ResNet50, InceptionResNetV2, and Xception. Following this, we propose a transfer learning(TL) based multiclass classification model called IVX16 based on the three best-performing TL models. We use a dataset consisting of a total of 3264 images. Through extensive experiments, we achieve peak accuracy of 95.11%, 93.88%, 94.19%, 93.88%, 93.58%, 94.5%, and 96.94% for VGG16, InceptionV3, VGG19, ResNet50, InceptionResNetV2, Xception, and IVX16, respectively. Furthermore, we use Explainable AI to evaluate the performance and validity of each DL model and implement recently introduced Vison Transformer (ViT) models and compare their obtained output with the TL and ensemble model.

Machine learning tools in the tasks of selecting the determinants of socio-economic situation and development potential of Russian regions

Subject. The article addresses the selection of determinants that are significant for assessing the level of socio-economic situation and development potential of Russian regions. Objectives. The aim is to study machine learning algorithms for the selection of determinants – predictors of the level of socio-economic situation and development potential of Russian regions, to build models of classification of regions, according to the level of socio-economic situation, using various machine learning algorithms. Methods. To build classification models, I used data from the Federal State Statistics Service, the Institute of Scientific Communications, the RIA Novosti news agency, and the TAdviser Internet portal. Procedures for data classification, model parameter estimation, selection of significant determinants and visualization of results are performed, using the basic functions of the PyCaret library. Cohen's Kappa statistics and Matthews correlation coefficient were employed as priority metrics for evaluating the model productivity. The algorithms for selecting determinants are implemented in the Google Colab analytical environment. Results. I constructed multiclass classification models based on simple and ensemble machine learning algorithms. Simple classification algorithms, including logistic and ridge regression models, naive Bayesian algorithm, decision tree, support vector machine, and k-nearest neighbor methods are characterized by accuracy values at 77%, however, Cohen's Kappa statistics and the Matthews correlation coefficient only show a satisfactory relationship between the actual and predicted value of the region class. Ensemble algorithms, including random forest, gradient boosting and extreme gradient boosting, are characterized by a close relationship between the actual and forecast estimates of the classifier at a level of more than 70%. Conclusions. The random forest algorithm is recognized as the most effective classification model. The gross regional product and investments in fixed assets are informative determinants for measuring the socio-economic status.

Multi-Identity Recognition of Darknet Vendors Based on Metric Learning

Dark web vendor identification can be seen as an authorship aliasing problem, aiming to determine whether different accounts on different markets belong to the same real-world vendor, in order to locate cybercriminals involved in dark web market transactions. Existing open-source datasets for dark web marketplaces are outdated and cannot simulate real-world situations, while data labeling methods are difficult and suffer from issues such as inaccurate labeling and limited cross-market research. The problem of identifying vendors’ multiple identities on the dark web involves a large number of categories and a limited number of samples, making it difficult to use traditional multiclass classification models. To address these issues, this paper proposes a metric learning-based method for dark web vendor identification, collecting product data from 21 currently active English dark web marketplaces and using a multi-dimensional feature extraction method based on product titles, descriptions, and images. Using pseudo-labeling technology combined with manual labeling improves data labeling accuracy compared to previous labeling methods. The proposed method uses a Siamese neural network with metric learning to learn the similarity between vendors and achieve the recognition of vendors’ multiple identities. This method achieved better performance with an average F1-score of 0.889 and an accuracy rate of 97.535% on the constructed dataset. The contributions of this paper lie in the proposed method for collecting and labeling data for dark web marketplaces and overcoming the limitations of traditional multiclass classifiers to achieve effective recognition of vendors’ multiple identities.

Segmentation of anatomical layers and imaging artifacts in intravascular polarization sensitive optical coherence tomography using attending physician and boundary cardinality losses.

Intravascular ultrasound and optical coherence tomography are widely available for assessing coronary stenoses and provide critical information to optimize percutaneous coronary intervention. Intravascular polarization-sensitive optical coherence tomography (PS-OCT) measures the polarization state of the light scattered by the vessel wall in addition to conventional cross-sectional images of subsurface microstructure. This affords reconstruction of tissue polarization properties and reveals improved contrast between the layers of the vessel wall along with insight into collagen and smooth muscle content. Here, we propose a convolutional neural network model, optimized using two new loss terms (Boundary Cardinality and Attending Physician), that takes advantage of the additional polarization contrast and classifies the lumen, intima, and media layers in addition to guidewire and plaque shadows. Our model segments the media boundaries through fibrotic plaques and continues to estimate the outer media boundary behind shadows of lipid-rich plaques. We demonstrate that our multi-class classification model outperforms existing methods that exclusively use conventional OCT data, predominantly segment the lumen, and consider subsurface layers at most in regions of minimal disease. Segmentation of all anatomical layers throughout diseased vessels may facilitate stent sizing and will enable automated characterization of plaque polarization properties for investigation of the natural history and significance of coronary atheromas.

Distinguishing schizophrenia and bipolar disorder through a Multiclass Classification model based on multimodal neuroimaging data

This study aimed to identify neural biomarkers for schizophrenia (SZ) and bipolar disorder (BP) by analyzing multimodal neuroimaging. Utilizing data from structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and resting-state functional magnetic resonance imaging (rs-fMRI), multiclass classification models were created for SZ, BP, and healthy controls (HC). A total of 113 participants (BP: 31, SZ: 39, and HC: 43) were recruited under strict enrollment control, from which 272, 200, and 1875 features were extracted from sMRI, DTI, and rs-fMRI data, respectively. A support vector machine (SVM) with recursive feature elimination (RFE) was employed to build the models using a one-against-one approach and leave-one-out cross-validation, achieving a classification accuracy of 70.8%. The most discriminative features were primarily from rs-fMRI, along with significant findings in sMRI and DTI. Key biomarkers identified included the increased thickness of the left cuneus cortex and decreased regional functional connectivity strength (rFCS) in the left supramarginal gyrus as shared indicators for BP and SZ. Additionally, decreased fractional anisotropy in the left superior fronto-occipital fasciculus was suggested as specific to BP, while decreased rFCS in the left inferior parietal area might serve as a specific biomarker for SZ. These findings underscore the potential of multimodal neuroimaging in distinguishing between BP and SZ and contribute to the understanding of their neural underpinnings.

Individual- vs. Multiple-Objective Strategies for Targeted Sentiment Analysis in Finances Using the Spanish MTSA 2023 Corpus

Multitarget sentiment analysis extracts the subjective polarity of text from multiple targets simultaneously in a given context. This approach is useful in finance, where opinions about different entities affect the target differently. Examples of possible targets are other companies and society. However, typical multitarget solutions are resource-intensive due to the need to deploy multiple classification models for each target. An alternative to this is the use of multiobjective training approaches, where a single model is capable of handling multiple targets. In this work, we propose the Spanish MTSACorpus 2023, a novel corpus for multitarget sentiment analysis in finance, and we evaluate its reliability with several large language models for multiobjective training. To this end, we compare three design approaches: (i) a Main Economic Target (MET) detection model based on token classification plus a multiclass classification model for sentiment analysis for each target; (ii) a MET detection model based on token classification but replacing the sentiment analysis models with a multilabel classification model; and (iii) using seq2seq-type models, such as mBART and mT5, to return a response sequence containing the MET and the sentiments of different targets. Based on the computational resources required and the performance obtained, we consider the fine-tuned mBART to be the best approach, with a mean F1 of 80.300%.

AI Model for Industry Classification Based on Website Data

This paper presents a broad study on the application of the BERT (Bidirectional Encoder Representations from Transformers) model for multiclass text classification, specifically focusing on categorizing business descriptions into 1 of 13 distinct industry categories. The study involved a detailed fine-tuning phase resulting in a consistent decrease in training loss, indicative of the model’s learning efficacy. Subsequent validation on a separate dataset revealed the model’s robust performance, with classification accuracies ranging from 83.5% to 92.6% across different industry classes. Our model showed a high overall accuracy of 88.23%, coupled with a robust F1 score of 0.88. These results highlight the model’s ability to capture and utilize the nuanced features of text data pertinent to various industries. The model has the capability to harness real-time web data, thereby enabling the utilization of the latest and most up-to-date information affecting to the company’s product portfolio. Based on the model’s performance and its characteristics, we believe that the process of relative valuation can be drastically improved.

Genetic Clustering Algorithm-Based Feature Selection and Divergent Random Forest for Multiclass Cancer Classification Using Gene Expression Data

AbstractComputational identification and classification of clinical disorders gather major importance due to the effective improvement of machine learning methodologies. Cancer identification and classification are essential clinical areas to address, where accurate classification for multiple types of cancer is still in a progressive stage. In this article, we propose a multiclass cancer classification model that categorizes the five different types of cancers using gene expression data. To perform efficient analysis of the available clinical data, we propose feature selection and classification methods. We propose a genetic clustering algorithm (GCA) for optimal feature selection from the RNA-gene expression data, consisting of 801 samples belonging to the five major classes of cancer. The proposed feature selection method reduces the 1621 gene expressions into a cluster of 21 features. The optimum feature set acts as input data to the proposed divergent random forest. Based on the features computed, the proposed classifier categorizes the data samples into 5 different classes of cancers, including breast cancer, colon cancer, kidney cancer, lung cancer, and prostate cancer. The proposed divergent random forest provided performance improvisation in terms of accuracy with 95.21%, specificity with 93%, and sensitivity with 94.29% which outperformed all the other existing multiclass classification algorithms.

A heuristic method for discovering multi-class classification rules from multi-source data in cloud–edge system

The integration of diverse devices has made the establishment of multi-class classification models for multi-source data a primary concern for data mining in cloud–edge system. Developing rule-based classifiers is essential because they present results that express the reasons for classification. However, existing rule learning methods are not compatible with multi-source tabular data containing mixed features, imbalanced labels and missing values, making it difficult to build cross-data source and cross-device data mining models. We developed a heuristic multi-class rule learning method that can handle complex tabular datasets without relying too much on cumbersome preprocessing techniques. We abstract the training process of the classifier into a multi-objective optimization problem and design a novel hybrid evolutionary algorithm to obtain Pareto-optimal solutions as rule-based classifier. Compared with the existing explainable classification methods, this method has obvious advantages in the classification performance of tabular data.

Wavelet Scattering Transform for ECG Cardiovascular Disease Classification

Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this field is increasingly on prediction, with a growing dependence on machine learning techniques. This study aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet database by employing machine learning (ML). The study proposed several multi-class classification models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral features, and wavelet scattering, to extract features and capture unique characteristics from the ECG dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease. Furthermore, it could prove to be a valuable resource for future medical research projects aimed at improving the diagnosis and treatment of cardiovascular diseases.

Machine Learning-Based Decision-Making in Geriatrics: Aging Phenotype Calculator and Survival Prognosis.

Aging is a natural process with varying effects. As we grow older, our bodies become more susceptible to aging-associated diseases. These diseases, individually or collectively, lead to the formation of distinct aging phenotypes. Identifying these aging phenotypes and understanding the complex interplay between coexistent diseases would facilitate more personalized patient management, a better prognosis, and a prolonged lifespan. Many studies distinguish between successful aging and frailty. However, this simple distinction fails to reflect the diversity of underlying causes. In this study, we sought to establish the underlying causes of frailty and determine the patterns in which these causes converge to form aging phenotypes. We conducted a comprehensive geriatric examination, cognitive assessment, and survival analysis of 2,688 long-living adults (median age = 92 years). The obtained data were clustered and used as input data for the Aging Phenotype Calculator, a multiclass classification model validated on an independent dataset of 96 older adults. The accuracy of the model was assessed using the receiver operating characteristic curve and the area under the curve. Additionally, we analyzed socioeconomic factors that could contribute to specific aging patterns. We identified five aging phenotypes: non-frailty, multimorbid frailty, metabolic frailty, cognitive frailty, and functional frailty. For each phenotype, we determined the underlying diseases and conditions and assessed the survival rate. Additionally, we provided management recommendations for each of the five phenotypes based on their distinct features and associated challenges. The identified aging phenotypes may facilitate better-informed decision-making. The Aging Phenotype Calculator (ROC AUC = 92%) may greatly assist geriatricians in patient management.

DDoS Attacks Detection Approach based on Ensemble Model using Spark

We live in an era when time is a precious resource. Thus, dealing with the vast amount of data collected from different resources for various purposes requires creating systems that can process the data in a reasonable time to make it worthwhile. Accessing big data in machine learning and artificial intelligence models creates efficient, robust models. In this work, we present a method to create a multi-class classification model using Apache-spark. The model is built and trained with the CIC-DDOS2019 dataset to build a DDoS Attack detection model. Ensemble modeling was used to improve the accuracy and robustness of the model. At the same time, Apache-spark was used to distribute the vast amount of training and testing data over the models used to create the intrusion detection model. The proposed model has achieved high accuracy (99.94\\%) while reducing the execution time to almost the half when applied without Apache-spark.