Existing Machine Learning Methods Research Articles

Ensemble technique of intrusion detection for IoT-edge platform

Internet of Things (IoT) technology has revolutionized modern industrial sectors. Moreover, IoT technology has been incorporated within several vital domains of applicability. However, security is overlooked due to the limited resources of IoT devices. Intrusion detection methods are crucial for detecting attacks and responding adequately to every IoT attack. Conspicuously, the current study outlines a two-stage procedure for the determination and identification of intrusions. In the first stage, a binary classifier termed an Extra Tree (E-Tree) is used to analyze the flow of IoT data traffic within the network. In the second stage, an Ensemble Technique (ET) comprising of E-Tree, Deep Neural Network (DNN), and Random Forest (RF) examines the invasive events that have been identified. The proposed approach is validated for performance analysis. Specifically, Bot-IoT, CICIDS2018, NSL-KDD, and IoTID20 dataset were used for an in-depth performance assessment. Experimental results showed that the suggested strategy was more effective than existing machine learning methods. Specifically, the proposed technique registered enhanced statistical measures of accuracy, normalized accuracy, recall measure, and stability.

Early autism diagnosis based on path signature and Siamese unsupervised feature compressor.

Autism spectrum disorder has been emerging as a growing public health threat. Early diagnosis of autism spectrum disorder is crucial for timely, effective intervention and treatment. However, conventional diagnosis methods based on communications and behavioral patterns are unreliable for children younger than 2 years of age. Given evidences of neurodevelopmental abnormalities in autism spectrum disorder infants, we resort to a novel deep learning-based method to extract key features from the inherently scarce, class-imbalanced, and heterogeneous structural MR images for early autism diagnosis. Specifically, we propose a Siamese verification framework to extend the scarce data, and an unsupervised compressor to alleviate data imbalance by extracting key features. We also proposed weight constraints to cope with sample heterogeneity by giving different samples different voting weights during validation, and used Path Signature to unravel meaningful developmental features from the two-time point data longitudinally. We further extracted machine learning focused brain regions for autism diagnosis. Extensive experiments have shown that our method performed well under practical scenarios, transcending existing machine learning methods and providing anatomical insights for autism early diagnosis.

Physics-informed machine learning of multi-source monitoring data sparsely measured within a 2D vertical cross-section

Conventional machine learning (ML) methods suffer from several limitations, e.g., overreliance on large training datasets and poor performance in extrapolating beyond observations. Geotechnical monitoring data at specific sites are multi-sourced (e.g., settlement and horizontal displacement data). Moreover, each source of monitoring data may vary temporally and be sparsely measured, e.g., within a two-dimensional (2D) geological cross-section. Thus, effectively learning multi-source and limited monitoring data is difficult using existing ML methods. To address this challenge, this study proposes a physics-informed ML method combining geotechnical numerical models (e.g., the finite element model (FEM)) with sparse dictionary learning (SDL) methods. It uses a range of probable numerical model results corresponding to multiple, temporally varying responses in a 2D spatial range to construct a “dictionary” in SDL and employs multi-source and limited monitoring data to identify a few “atoms” from the constructed dictionary to improve model predictions. An engineering project for embankment construction is used to illustrate the proposed approach. Our results indicate that the proposed approach effectively integrates FEM results with multi-source and sparsely measured monitoring data and improves the predictions of multiple spatio-temporally varying geotechnical responses significantly, particularly those at subsequent time steps and unmonitored spatial locations within a 2D vertical cross-section.

FTG-Net-E: A hierarchical ensemble graph neural network for DDoS attack detection

Distributed Denial-of-Service (DDoS) attacks are a major threat to computer networks. These attacks can be carried out by flooding a network with malicious traffic, overwhelming its resources, and/or making it unavailable to legitimate users. Existing machine learning methods for DDoS attack detection typically use statistical features of network traffic, such as packet sizes and inter-arrival times. However, these methods often fail to capture the complex relationships between different traffic flows. This paper proposes a new DDoS attack detection approach that uses Graph Neural Networks (GNN) ensemble learning. GNN ensemble learning is a type of machine learning that combines multiple GNN models to improve the detection accuracy. We evaluated our approach on the Canadian Institute for Cybersecurity Intrusion Detection Evaluation Dataset (CICIDS2018) and CICIDS2017 datasets, a benchmark dataset for DDoS attack detection. Our work provides two main contributions. First, we extend our DDoS attack detection approach using GNN ensemble learning. Second, we explore the evaluation and fine-tuning of hyperparameter metrics through ensemble learning, significantly enhancing accuracy compared to a single GNN model and achieving an average 3.2% higher F1-score. Additionally, our approach effectively reduces overfitting by incorporating regularization techniques, such as dropout and early stopping. Specifically, we use a hierarchical ensemble of GNN, where each GNN learns the relationships between traffic flows at a different granularity level. We then use bagging and boosting to combine the predictions of the individual GNN, further improving detection accuracy. Results show that our system can achieve 99.67% accuracy, with a F1-score of 99.29%, which is better than state-of-the-art methods, even using single traffic architecture.

A novel procedure for cable damage identification of cable-stayed bridge using particle swarm optimization and machine learning

The cables are crucial components in the ensuring safety of the stayed-cable bridges. The early identification and quantification of cable damage based on the inherent structural health monitoring (SHM) system is a priority to prevent disasters. In this study, a procedure is proposed to identify the cable damage in the cable-stayed bridges using the particle swarm optimization (PSO) and the categorical gradient boosting (CatBoost) algorithm. The PSO-based finite element model updating method is implemented to establish the baseline model while a practical advanced analysis program is used to generate simulation data. As an efficient and up-to-date machine learning (ML) algorithm, CatBoost is utilized to capture the complex nonlinear correlations between the vibration characteristics and the cable damages. A case study of a benchmark bridge where cable damage has been identified is considered to evaluate the efficiency of the proposed procedure. The fivefold cross-validation and grid search methods are used to find the optimal model. The accuracy of the proposed cable damage identification model using CatBoost is also verified through the comparison with three existing ML methods: random forest, decision tree, and extreme gradient boosting. The identification results of both simulation and real cases of cable damage demonstrate that the proposed procedure is a novel and powerful approach for cable damage identification of the cable-stayed bridge using measurement data of the existing SHM system.

A hybrid deep semantic mining method considering fuzzy expressions for the automatic recognition of construction safety hazard information

Safety hazards are a key consideration in construction management. The efficient recognition of safety hazard information can help managers formulate safety hazard management measures and improve the efficiency of construction safety management. However, construction site safety hazard data are stored in semistructured and unstructured text formats, which cannot be directly converted into understandable and usable information. Moreover, safety hazard text contains many fuzzy expressions, thereby increasing the difficulty of text semantic analysis; thus, how to accurately mine safety hazard information from complex and diverse text data is an urgent problem that must be solved. In consideration of this problem, we propose a bidirectional long short-term memory (BiLSTM) method with a fuzzy word vector and self-attention mechanism (FSABiLSTM) to automatically recognize safety hazard information. This method adopts TextRank and Word2vec to calculate the fuzzy word vector and process fuzzy expressions in safety hazard text. The safety hazard text semantic features are deeply extracted based on BiLSTM and a fuzzy word vector, and the extracted semantic features are analyzed via a self-attention mechanism. Actual construction safety hazard text is used to verify the reliability and applicability of the method, and the results indicate that the accuracy of this method, which outperforms existing machine learning methods, is 91.70%. In addition, the FSABiLSTM method can be used to automatically evaluate the risk degree of safety hazards; this use is beneficial to managing and controlling safety hazards. Concerning safety hazard text data, this study provides a new deep mining approach that can enhance safety management efficiency.

Enhancing Regional Climate Downscaling through Advances in Machine Learning

Abstract Despite the sophistication of global climate models (GCMs), their coarse spatial resolution limits their ability to resolve important aspects of climate variability and change at the local scale. Both dynamical and empirical methods are used for enhancing the resolution of climate projections through downscaling, each with distinct advantages and challenges. Dynamical downscaling is physics based but comes with a large computational cost, posing a barrier for downscaling an ensemble of GCMs large enough for reliable uncertainty quantification of climate risks. In contrast, empirical downscaling, which encompasses statistical and machine learning techniques, provides a computationally efficient alternative to downscaling GCMs. Empirical downscaling algorithms can be developed to emulate the behavior of dynamical models directly, or through frameworks such as perfect prognosis in which relationships are established between large-scale atmospheric conditions and local weather variables using observational data. However, the ability of empirical downscaling algorithms to apply their learned relationships out of distribution into future climates remains uncertain, as is their ability to represent certain types of extreme events. This review covers the growing potential of machine learning methods to address these challenges, offering a thorough exploration of the current applications and training strategies that can circumvent certain issues. Additionally, we propose an evaluation framework for machine learning algorithms specific to the problem of climate downscaling as needed to improve transparency and foster trust in climate projections. Significance Statement This review offers a significant contribution to our understanding of how machine learning can offer a transformative change in climate downscaling. It serves as a guide to navigate recent advances in machine learning and how these advances can be better aligned toward inherent challenges in climate downscaling. In this review, we provide an overview of these recent advances with a critical discussion of their advantages and limitations. We also discuss opportunities to refine existing machine learning methods alongside new approaches for the generation of large ensembles of high-resolution climate projections.

PhylaGAN: data augmentation through conditional GANs and autoencoders for improving disease prediction accuracy using microbiome data.

Research is improving our understanding of how the microbiome interacts with the human body and its impact on human health. Existing machine learning methods have shown great potential in discriminating healthy from diseased microbiome states. However, Machine Learning based prediction using microbiome data has challenges such as, small sample size, imbalance between cases and controls and high cost of collecting large number of samples. To address these challenges, we propose a deep learning framework phylaGAN to augment the existing datasets with generated microbiome data using a combination of conditional generative adversarial network (C-GAN) and autoencoder. Conditional generative adversarial networks train two models against each other to compute larger simulated datasets that are representative of the original dataset. Autoencoder maps the original and the generated samples onto a common subspace to make the prediction more accurate. Extensive evaluation and predictive analysis was conducted on two datasets, T2D study and Cirrhosis study showing an improvement in mean AUC using data augmentation by 11% and 5% respectively. External validation on a cohort classifying between obese and lean subjects, with a smaller sample size provided an improvement in mean AUC close to 32% when augmented through phylaGAN as compared to using the original cohort. Our findings not only indicate that the generative adversarial networks can create samples that mimic the original data across various diversity metrics, but also highlight the potential of enhancing disease prediction through machine learning models trained on synthetic data. https://github.com/divya031090/phylaGAN.

Multi‐source data‐driven approach for prediction of melt density during polymer compounding

AbstractMelt density is a crucial quality indicator for polymer composites, yet real‐time measurement remains challenging due to processing complexities. While existing machine learning methods offer solutions, they often fall short in complex compounding scenarios. This study presents a novel multi‐source data‐driven approach for measuring melt density in polycarbonate/acrylonitrile butadiene styrene blends. By incorporating ultrasonic, near‐infrared, and Raman spectra data acquired during melt processing, a deep separable convolutional neural network model is developed to predict melt density accurately. The model effectively fuses multi‐source data to establish the mapping relationship between input data and melt density output. Results demonstrate the model's ability to monitor melt density in real‐time, achieving a prediction accuracy with RMSE and R2 indexes of 0.005 g/cm3 and 0.9841, respectively. The proposed approach outperforms existing methods, showcasing its effectiveness and superiority in melt density prediction for polymer compounding processes.Highlights Establishment of the real‐time monitoring system for polymer extrusion processes. Conversion of multi‐sensor signals into time‐frequency images using wavelet decomposition. Fusion of sensor data into a three‐channel tensor‐image. Development of a data‐driven DSCNN model for predicting melt density. Implementation for online monitoring and prediction in PC/ABS compounding system.

Boost recall in quasi-stellar object selection from highly imbalanced photometric datasets

Context. The identification of bright quasi-stellar objects (QSOs) is of fundamental importance to probe the intergalactic medium and address open questions in cosmology. Several approaches have been adopted to find such sources in the currently available photometric surveys, including machine learning methods. However, the rarity of bright QSOs at high redshifts compared to other contaminating sources (such as stars and galaxies) makes the selection of reliable candidates a difficult task, especially when high completeness is required. Aims. We present a novel technique to boost recall (i.e., completeness within the considered sample) in the selection of QSOs from photometric datasets dominated by stars, galaxies, and low-z QSOs (imbalanced datasets). Methods. Our heuristic method operates by iteratively removing sources whose probability of belonging to a noninteresting class exceeds a user-defined threshold, until the remaining dataset contains mainly high-z QSOs. Any existing machine learning method can be used as the underlying classifier, provided it allows for a classification probability to be estimated. We applied the method to a dataset obtained by cross-matching PanSTARRS1 (DR2), Gaia (DR3), and WISE, and identified the high-z QSO candidates using both our method and its direct multi-label counterpart. Results. We ran several tests by randomly choosing the training and test datasets, and achieved significant improvements in recall which increased from ~50% to ~85% for QSOs with z > 2.5, and from ~70% to ~90% for QSOs with z > 3. Also, we identified a sample of 3098 new QSO candidates on a sample of 2.6 ×106 sources with no known classification. We obtained follow-up spectroscopy for 121 candidates, confirming 107 new QSOs with z > 2.5. Finally, a comparison of our QSO candidates with those selected by an independent method based on Gaia spectroscopy shows that the two samples overlap by more than 90% and that both selection methods are potentially capable of achieving a high level of completeness.

Toward accurate Amazigh part-of-speech tagging

<span lang="EN-US">Part-of-speech (POS) tagging is the process of assigning to each word in a text its corresponding grammatical information POS. It is an important pre-processing step in other natural language processing (NLP) tasks, so the objective of finding the most accurate one. The previous approaches were based on traditional machine learning algorithms, later with the development of deep learning, more POS taggers were adopted. If the accuracy of POS tagging reaches 97%, even with the traditional machine learning, for high resourced language like English, French, it’s far the case in low resource language like Amazigh. The most used approaches are traditional machine learning, and the results are far from those for rich language. In this paper, we present a new POS tagger based on bidirectional long short-term memory for Amazigh language and the experiments that have been done on real dataset shows that it outperforms the existing machine learning methods.</span>

THE CONSTRUCTION OF INTELLIGENT RULE-BASED SYSTEM WITH DEEP-LEARNING FUNCTIONALITY FOR OIL AND GAS WELLS DRILLING

The article is devoted to solving the problems of predictive modeling using machine learning methods under conditions of uncertainty in huge data sets created in various sectors of the economy, in particular, the oil and gas industry, due to the spread of IoT and cloud services. The author notes that existing machine learning methods, including widely used deep learning methods, face limitations in achieving high prediction accuracy due to various uncertainties. The article explores fuzzy learning approaches to overcome uncertainties such as imprecision and ambiguity, but emphasizes their limitations of incompleteness and vagueness. It is argued that rule-based expert systems augmented with deep learning offer a more robust framework for dealing with such uncertainties by integrating associative memory, helping to produce more accurate predictive models, especially in data-intensive scenarios such as oil and gas drilling processes. The study focuses on improving predictive modeling under conditions of uncertainty in large data sets. We discuss the limitations of fuzzy learning when processing incomplete data and propose a hybrid approach to the problem. Integration of the associative memory learning method into the logical inference procedures allows to identify accurate data models, thereby improving the quality of forecasting under uncertainty. This integrated approach aims to improve forecasting accuracy by leveraging the strengths of deep learning in pattern recognition and the ability of rule-based systems to cope with uncertainty. The main result of the research work is the development of a new method that integrates a level of deep processing with associative memory into a rule-based system to improve forecasting accuracy under uncertainty. This was achieved by using a multilayer neural network and adding additional parameters. This method significantly improved the accuracy of predicting the dynamics of operational parameters during drilling. Thus, the presented study is a new contribution to the task of developing forecasting methods under conditions of uncertainty, which is especially relevant for oil and gas drilling.

Predicting S. aureus antimicrobial resistance with interpretable genomic space maps.

Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models based on genomic data to predict resistant phenotypes can serve as a fast screening tool prior to phenotypic testing. Nonetheless, many existing ML methods lack interpretability. Therefore, we present a methodology for visualization of sequence space and AMR prediction based on the non-linear dimensionality reduction method - generative topographic mapping (GTM). This approach, applied to AMR data of >5000 S. aureus isolates retrieved from the PATRIC database, yielded GTM models with reasonable accuracy for all drugs (balanced accuracy values ≥0.75). The Generative Topographic Maps (GTMs) represent data in the form of illustrative maps of the genomic space and allow for antibiotic-wise comparison of resistant phenotypes. The maps were also found to be useful for the analysis of genetic determinants responsible for drug resistance. Overall, the GTM-based methodology is a useful tool for both the illustrative exploration of the genomic sequence space and AMR prediction.

Inception meets Swin Transformer: A Novel Approach for Metal Defect Recognition

The detection of metal defects with high precision and efficiency is a significant challenge in modern industry. Existing machine learning methods for recognizing common metal surface defects heavily rely on expert knowledge for manual feature extraction. Conventional deep learning methods face challenges in capturing global feature information from defect images or defect detection signals.To address this issue, we proposed a metal defect recognition method based on an Inception-fused Swin Transformer model. The method combines the adaptive local feature extraction capability of the Inception structure with the advantage of the Swin Transformer in capturing global feature information from defect signals. Additionally, it utilizes the Channel-Coordinate Attention module (CoordAttention) to highlight important feature channels. Experimental results demonstrate the effectiveness of the proposed method on the Ultrasonic Defect Grayscale Image dataset (ULFSL-DET) and the publicly available Image-based Metal Defect dataset (NEU-CLS), achieving recognition accuracies of 98.1% and 99.8%, respectively. The method exhibits high effectiveness in recognizing metal defect signals in grayscale images, and it demonstrates strong generality for image-based metal defect recognition.

A general framework of high-performance machine learning algorithms: application in structural mechanics

Data-driven models utilizing powerful artificial intelligence (AI) algorithms have been implemented over the past two decades in different fields of simulation-based engineering science. Most numerical procedures involve processing data sets developed from physical or numerical experiments to create closed-form formulae to predict the corresponding systems’ mechanical response. Efficient AI methodologies that will allow the development and use of accurate predictive models for solving computational intensive engineering problems remain an open issue. In this research work, high-performance machine learning (ML) algorithms are proposed for modeling structural mechanics-related problems, which are implemented in parallel and distributed computing environments to address extremely computationally demanding problems. Four machine learning algorithms are proposed in this work and their performance is investigated in three different structural engineering problems. According to the parametric investigation of the prediction accuracy, the extreme gradient boosting with extended hyper-parameter optimization (XGBoost-HYT-CV) was found to be more efficient regarding the generalization errors deriving a 4.54% residual error for all test cases considered. Furthermore, a comprehensive statistical analysis of the residual errors and a sensitivity analysis of the predictors concerning the target variable are reported. Overall, the proposed models were found to outperform the existing ML methods, where in one case the residual error was decreased by 3-fold. Furthermore, the proposed algorithms demonstrated the generic characteristic of the proposed ML framework for structural mechanics problems.

Sentiment Analysis Using Machine Learning

In recent years, sentiment analysis on social media, including Facebook, Twitter and blogs, has grown in popularity. Social media generate large amounts of information, and this has contributed to the growth of sentiment analysis as a field of research. This study demonstrates that sentiment analysis has been thoroughly researched in previous years, and numerous methods have been designed and evaluated. Nevertheless, there is still much room for improvement. This paper reviews the state of art in sentiment analysis. Various machine learning procedures for sentiment analysis are discussed, their potential to increase the level of the analysis accuracy is underscored. This paper introduces sentiment analysis types, methodologies, applications, challenges, and a comparative study of machine learning and sentiment analysis approaches. Performance evaluation parameters, for sentiment analysis, have also been tested and compared using different machine learning classifiers. Performance evaluation points to logistic regression as the model that achieves the best result. In the future, a method that is easy, versatile, and practicable, should be offered as opposed to existing machine learning methods, and more work should be put into improving the algorithms’ performance.

AN UNSUPERVISED-SUPERVISED ENSEMBLE TECHNOLOGY WITH NON-ITERATIVE TRAINING ALGORITHM FOR SMALL BIOMEDICAL DATA ANALYSIS

Improving the accuracy of intelligent data analysis is an important task in various application areas. Existing machine learning methods do not always provide a sufficient level of classification accuracy for their use in practice. That is why, in recent years, hybrid ensemble methods of intellectual data analysis have begun to develop. They are based on the combined use of clustering and classification procedures. This approach provides an increase in the accuracy of the classifier based on machine learning due to the expansion of the space of the input data of the task by the results of the clustering.
 In this paper, the tasks of modification and improvement of such technology for small data analysis are considered. The basis of the modification is the use of clustering with output at the first step of the method to increase the accuracy of the entire technology. Despite the high accuracy of the work, this approach requires a significant expansion of the inputs of the final linear classifier (labels of the obtained clusters are added to the initial inputs). To avoid this shortcoming, the paper proposes an improvement based on the introduction of a new classification procedure at the first step of the method and replaces all the initial inputs of the task with the results of its work. In parallel with it, clustering is performed taking into account the original attribute, the results of which are added to the output of the classifier of the first step. In this way, the formation of an expanded set of data of significantly lower dimensionality in comparison with the existing method takes place (here there is no longer a large number of initial features, which is characteristic of biomedical engineering tasks). This reduces the training time of the method and increases its generalization properties.
 Modeling of the method was based on the use of a short dataset contained in an open repository. After the preprocessing procedures, the dataset has only 294 vectors, each of which was characterized by 18 attributes. Data classification was done using an SGTM-based neural-like structure classifier. This linear classifier provides high accuracy of work. In addition, it does not provide for the implementation of an iterative training procedure and additional adjustment of work parameters. Data clustering was performed using the k-means method. This choice is due to both the simplicity and speed of its work. 
 The search for the optimal number of k-means clusters was carried out using 4 different methods. They all showed different results. That is why, some experiments were conducted to assess the influence of different numbers of clusters (from 3 to 7) on the accuracy of all 4 algorithms of the developed technology. The accuracy of the proposed technology has been established experimentally in comparison with the linear classifier and the existing hybrid method. In addition, by reducing the inputs of the final classifier, the developed technology reduces the duration of the training procedure compared to the basic method. All this ensures the possibility of using the proposed technology when solving various applied problems of medical diagnostics, in particular, based on the analysis of small data.
 Keywords: small data approach, non-iterative training, ensemble learning, unsupervised-supervised technology, biomedical engineering.

Random hierarchical model for estimation of wheat yield in the North China Plain at different spatial scales

Accurate and large-scale wheat yield prediction in the North China Plain (NCP) can provide necessary information for agricultural policies and agricultural trade. Many studies have presented wheat yield estimation methods by using existing machine learning methods and remote sensing (RS) or environmental data. However, these methods only blindly input multi-source data into the model and do not consider the hierarchical relationships and interaction between different data types. In addition, there has been scant attention paid to the consistency of yield estimation models across varying spatial scales. To address these problems, a novel dynamic yield estimation model, known as random hierarchical model (RHM), which takes into account the hierarchical relationship of multi-source data, is constructed to estimate the wheat yield in the NCP. First, the time interval of wheat growth period is refined by using the 24 solar terms in China, and a time series multi-source dataset of climate, soil, and RS is constructed. Second, the hierarchical linear model is used to layer multi-source data and randomly select environmental and RS features with multiple time intervals. Multiple hierarchical models are constructed and optimized for integration, and the interrelationships between data collected at different levels are fully utilized, which can improve the accuracy of the yield estimation models in interannual and large-scale applications. Finally, the RHM at different spatial scales is cross-verified by using the measured and statistical data of the NCP for 4 years. The results indicated that the error of the RHM estimation is smaller than that of widely used machine learning models at different spatial scales of field-level measurement data (R2 = 0.52, nRMSE = 16.43%), county-level measurement integration data (R2 = 0.62, nRMSE = 12.83%), and county-level official statistics (R2 = 0.68, nRMSE = 11.41%). Our proposed RHM that considers the hierarchical structure of multi-source data is a reliable and a promising method for improving yield estimation. In addition, heterogeneity in the hierarchical relationships is observed between the different types of data in the RHM at different spatial scales, resulting in differences in the optimal lead time for estimating yield and the importance of key driving factors in the model, indicating that the cross-spatial scale applications of the model are not allowed. This study provides insights for large-scale wheat yield estimation and yield response to different environments and provided evidence and explanation for the prohibition of generalization of models at different spatial scales.

DDoS family: A novel perspective for massive types of DDoS attacks

Distributed Denial of Service (DDoS) defense is a profound research problem. In recent years, adversaries tend to complicate their attack strategies by crafting vast DDoS variants. On the one hand, this trend exacerbates both extremes of classification granularity (i.e., binary and attack level) in existing machine learning methods. On the other hand, massive attack categories make the filter rule table bulky, as well as cause problems of slow reaction presented in the recent state-of-the-art DDoS mitigation system. Therefore, we propose the concept of a DDoS family to reconcile/cope with these issues. The specific technical roadmap includes traffic pattern characterization, attack fingerprint production, and cross-executed family partition by community detection. Through extensive evaluations, we demonstrate the benefits of the proposal in terms of portraying similarities, guiding model classification/unknown attack detection, optimizing defense strategies, and speeding filtering reactions. For instance, our results show that using only one rule can defend 15 types of attacks due to their homogeneous behavioral representation. Particularly, we find the interesting observation that counting the backward packet is more efficient and robust against some attacks (e.g., Tor's Hammer Attack), which is very different from previous solutions.

Energy Performance Certificate Estimation at Large Scale Based on Open Data

This paper presents an innovative methodology for enhancing energy efficiency assessment procedures in the built environment, with a focus on the Switzerland’s Energy Strategy 2050. The current methodology necessitates intensive expert surveys, leading to substantial time and cost implications. Also, such a process can’t be scaled to a large number of buildings.Using machine learning techniques, the estimation process is augmented and exploit open data resources. Utilizing a robust dataset exceeding 70’000 energy performance certificates (CECB), the method devises a two-stage ML approach to forecast energy performance. The first phase involves data reconstruction from online repositories, while the second employs a regression algorithm to estimate the energy efficiency.The proposed approach addresses the limitations of existing machine learning methods by offering finer prediction granularity and incorporating readily available data. The results show a commendable degree of prediction accuracy, particularly for single-family residences. Despite this, the study reveals a demand for further granular data, and underlines privacy concerns associated with such data collection. In summary, this investigation provides a significant contribution to the enhancement of energy efficiency assessment methodologies and policy-making.