Machine learning detects geyser eruptions in noisy data.
problem Discerning geyser eruptions from background noise.
method Random Forests (RF) on filtered seismic data.
result RF achieves >90% accuracy in geyser state classification.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “CO2 Intrusion”
In this note, we present an existence result of a Nash equilibrium between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity…
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approachcs.CR
Geometric Graph Alignment enhances IoT intrusion detection using NID data.
problem Data scarcity hinders IoT intrusion detection accuracy.
method Geometric Graph Alignment (GGA) approach to transfer knowledge between network intrusion detection and IoT intrusion detection domains.
result GGA approach boosts IoT intrusion detection performance on multiple datasets.
Study finds cherry-picking load shaping strategies outperforms others in reducing grid CO2 emissions.
problem Lack of detailed counterfactual data makes it hard to assess load shaping strategies' effectiveness.
method Calibrated granular ERCOT simulations for counterfactual analysis of load shaping strategies.
result LMP-based load shaping outperforms other strategies in reducing grid CO2 emissions.
In this paper, we analyze Nash equilibria between electricity producers selling their production on an electricity market and buying CO2 emission allowances on an auction carbon market. The producers' strategies integrate the coupling of the two markets via the cost functions of the electricity production. We set out a…
Machine learning predicts CO2 emissions in power grids, reducing uncertainty.
problem Forecasting CO2 emission intensities in power grids.
method Developed a machine learning algorithm using LASSO, feature selection, and Softmax weighted average.
result Marginal emissions are independent of DK2 zone conditions, suggesting external generators.
CO2 algorithm creates coresets for generic smooth divergences efficiently.
problem Efficiently creating coresets for generic smooth divergences.
method CO2 algorithm using functional Taylor expansion and maximum mean discrepancy minimization.
result Poly-logarithmically many data points suffice for Sinkhorn divergence approximation.
OCLEP+ detects anomalies and intrusions with short patterns.
problem Detecting anomalies and intrusions in minimal data.
method One-class Classification using Length statistics of Emerging Patterns Plus.
result Effective detection of anomalies and intrusions with minimal data.
Paper proposes mimic learning to share intrusion detection models without private data.
problem Difficulty in obtaining labelled training data for intrusion detection models due to privacy concerns.
method Use of mimic learning to transfer knowledge from a teacher model trained on private data to a student model.
result Student model mimics teacher model without access to private data.
Proposes ML-RBM for non-intrusive load monitoring without appliance-level data.
problem Non-intrusive load monitoring without appliance-level data.
method Multi-label Restricted Boltzmann Machine (ML-RBM)
result Experimental evaluation of proposed and state-of-the-art techniques.
This paper explores deep learning for improved intrusion detection.
problem Improving intrusion detection systems using deep learning.
method Discusses deep learning models and neural networks for intrusion detection.
result Promises enhanced performance in intrusion detection.
This paper shows diffusion models improve intrusion detection by purifying adversarial examples.
problem Adversarial examples mislead ML intrusion detection systems, leading to false alerts or evasion.
method Used diffusion models to purify adversarial examples in intrusion detection.
result Diffusion models enhance adversarial robustness in intrusion detection without significantly impacting normal performance.
Deep Learning Framework for History Matching CO2 Storage with 4D Seismic and Monitoring Well Datacs.LG
Deep learning calibrates CO2 storage formations from seismic and well data.
problem Uncertainty in CO2 storage formation properties.
method Two deep learning models for well and seismic data, integrated into MCMC history matching.
result Significant uncertainty reduction in key parameters and accurate CO2 plume predictions.
We introduce two simple models of forward-backward stochastic differential equations with a singular terminal condition and we explain how and why they appear naturally as models for the valuation of CO2 emission allowances. Single phase cap-and-trade schemes lead readily to terminal conditions given by indicator funct…
Enhances network intrusion detection in noisy data.
problem Robustness against contaminated and noisy data inputs in network intrusion detection.
method Probabilistic Temporal Graph Network Support Vector Data Description (TGN-SVDD) model.
result Significant improvements in detection performance with synthetic noise.
Deep neural network predicts multiphase flow in heterogeneous domains.
problem Predicting multiphase flow in complex, heterogeneous systems.
method Deep neural network model for handling permeability heterogeneity and learning interplay of forces.
result Highly accurate predictions of CO2 saturation distribution with computational efficiency.
Improved non-intrusive load monitoring with a novel neural network.
problem Accurately disaggregating household electricity consumption without dedicated meters.
method Developed a scale- and context-aware network with multi-scale features and contextual information.
result Significantly improved accuracy compared to state-of-the-art methods.
Proposes SOMDAGMM for more accurate network intrusion detection.
problem Inaccurate network intrusion detection in secure network environments.
method Integrates self-organizing map with deep autoencoding Gaussian mixture model.
result SOMDAGMM outperforms state-of-the-art DAGMM with up to 15.58% improvement in F1 score.
Non-intrusive algorithm corrects corrupted data in classification models.
problem Classification problems with corrupted training data.
method Post-processes a trained classification model with a correction procedure.
result Corrected models deliver correct classification results even with corrupted data.
Project compares KDDCup99 and NSL-KDD datasets using ML classifiers.
problem Quality and bias in IoT network intrusion detection datasets.
method Evaluation of various ML classifiers on both datasets.
result NSL-KDD dataset is of higher quality with lower bias.
A tree-based IDS detects cyber-attacks in AV networks.
problem Cyber-attacks in AV and IoV networks.
method Tree-structure machine learning models, ensemble learning, feature selection.
result High detection rate and low computational cost achieved.
Tall wheatgrass outperforms rye in energy and environmental metrics, marginally improving economic viability.
problem Finding sustainable alternatives for marginal agricultural areas.
method Economic assessment using profit margin and Life Cycle Assessment (LCA) for energy and environmental performance.
result Tall wheatgrass shows better environmental and energy performance with reduced inputs and lower energy consumption.
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimizationcs.LG
Study on deep learning IDS resistance against adversarial attacks.
problem Vulnerabilities in deep learning-based IDS against adversarial attacks.
method Apply min-max optimization to train IDS against adversarial samples.
result Adversarial attack methods can be used in continuous domains and boost IDS robustness.
User authentication and intrusion detection differ from standard classification problems in that while we have data generated from legitimate users, impostor or intrusion data is scarce or non-existent. We review existing techniques for dealing with this problem and propose a novel alternative based on a principled sta…
We present a novel approach to the pricing of financial instruments in emission markets, for example, the EU ETS. The proposed structural model is positioned between existing complex full equilibrium models and pure reduced form models. Using an exogenously specified demand for a polluting good it gives a causal explan…
AutoIDS uses auto-encoders to detect network intrusions efficiently.
problem Detecting network intrusions with high accuracy and efficiency.
method Semi-supervised machine learning with cascading encoder-decoder neural networks.
result AutoIDS achieves 90.17% accuracy on NSL-KDD dataset, superior to other methods.
Deep learning detects cyber-attacks in smart grid systems.
problem Cyber-attacks on smart grid systems.
method Deep learning-based intrusion detection system trained on industrial dataset.
result Proposed system outperforms Naive Bayes, SVM, and Random Forest.
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion Detectioncs.LG
EagerNet detects network attacks quickly with less resources.
problem Efficiently detecting network attacks with minimal resources.
method Proposes a new architecture that trades prediction speed for accuracy, evaluating only a subset of layers.
result Comparable accuracies to simple FCNNs achieved with early predictions, saving energy and computational efforts.
Operator inference for non-intrusive model reduction of systems with non-polynomial nonlinear termsmath.NA
New method learns low-dimensional models for systems with non-polynomial terms.
problem Modeling systems with non-polynomial nonlinear terms that are spatially local and given in analytic form.
method Non-intrusive model reduction method that learns operators for linear and polynomially nonlinear dynamics via a least-squares problem incorporating given non-polynomial terms.
result Comparable accuracy to intrusive methods that require full knowledge of governing equations.
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulationphysics.comp-ph
A novel algorithm uses Gaussian process regression to interpret non-intrusive ROMs.
problem Lack of interpretability in non-intrusive ROMs.
method Latent-space interpolation using Gaussian process regression.
result Interpretability of ROMs improved with continuous time evolution.
In this contribution we describe an approach to evolve composite covariance functions for Gaussian processes using genetic programming. A critical aspect of Gaussian processes and similar kernel-based models such as SVM is, that the covariance function should be adapted to the modeled data. Frequently, the squared expo…
Method constructs hedging portfolio for carbon risk but not ESG risk.
problem Hedging carbon risk with ESG risk.
method Triangulated Maximally Filtered Graph and node2vec algorithms.
result Efficient hedging portfolio strategy for carbon risk but not ESG risk.
Defines an implied CO2-price to cover climate change costs, finding it significantly higher than the SCC.
problem The social cost of carbon (SCC) does not fully cover climate change costs.
method Defines an implied CO2-price as a 'polluter pays principle' and calculates its value using a DICE model.
result The cost-implied CO2 price is around 500/tCO2 for SCC.
Bayesian coresets help reduce network intrusion detection data size.
problem Large network intrusion detection data sets hinder Bayesian learning.
method Use Bayesian coresets to reduce data size while maintaining accuracy.
result Bayesian coresets improve learning accuracy and reduce storage needs.
Adversarial attacks hide cyber-physical attacks in ICS.
problem Hiding cyber-physical attacks in industrial control systems.
method Modeling an attacker compromising sensors, manipulating data, and evaluating attacks on both continuous and mixed data.
result Successfully hides cyber-physical attacks with 2.87 out of 12 sensors compromised on average.
New method combines neural networks and data assimilation for indoor air quality prediction.
problem Accurate and fast prediction of indoor air quality using real-time data.
method Combines Data Assimilation and Machine Learning using a Convolutional neural network and Long-Short-Term-Memory.
result Improved accuracy of dynamic system representation by integrating real data.
Improves NILM with multi-label SRC, outperforming state-of-the-art.
problem Non-intrusive load monitoring (NILM) for energy disaggregation.
method Modified multi-label sparse representation based classification (SRC).
result Significant improvement over state-of-the-art techniques with minimal training data.
New method optimizes searchers' allocation on perimeters over time.
problem Optimizing searchers' allocation on perimeters to detect intrusions.
method Combinatorial multi-armed bandit (CMAB) with upper confidence bound approach.
result Upper and lower bounds on expected performance of the method.
Research compares MLP and MLR models for Balkan energy consumption predictions.
problem Predicting energy consumption in the Balkans using demographic and economic parameters.
method Applied multiple linear regression and multilayer perceptron models to data from 1995-2014.
result Multilayer perceptron model predicts energy consumption better than multiple regression model.
Improved IDS using LSTM and embedding for network traffic data.
problem Network security threats and need for IDS to detect attacks.
method Proposes IDS models using LSTM for time-series and embedding for categorical network traffic data.
result Improves binary classification accuracy to 99.72%.
New NIDS uses hypergraphs for real-time detection of evolving port scans.
problem Challenges in offline NIDS for auto-generated port scans.
method Hypergraph-based metrics for real-time ML ensemble NIDS.
result High accuracy, precision, and recall in detecting port scans and attacks.
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networkscs.CR
This research improves network security by detecting attacks using SVM and dimension reduction.
problem Detecting and preventing attacks on computer networks.
method Combining SVM and SVD for anomaly detection.
result The proposed method outperforms other methods in detecting network attacks.
Generative adversarial networks generate synthetic anomalies for host-based intrusion detection.
problem Imbalanced datasets in host-based intrusion detection.
method Cycle-GAN to generate synthetic anomalies from normal data.
result Improved anomaly detection with higher AUC and anomaly detection rate.
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion. These methods often neglect the dynamic nature of network data which requires to use adaptive feature selection techniques. In this paper, we present a simple technique based on incremental le…
A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partiall…
Active learning improves network intrusion detection with minimal labeled data.
problem Finding new attack vectors in low-volume traffic.
method Proposes an active learning framework to minimize labeled data while improving detection quality.
result Active learning with minimal labeled data significantly improves anomaly detection in network traffic.
New online learning algorithms improve cyberattack detection in industrial control systems.
problem Detecting cyberattacks in industrial control systems with limited resources.
method Online learning algorithms to process continuous data streams and address class imbalance.
result Improved detection rate of cyberattacks in industrial control systems.
EUREKA builds classifiers that use surprising features.
problem Building classifiers that are interesting, not just accurate.
method Uses large language models to rank features by interestingness and builds interpretable classifiers using only selected features.
result EUREKA discovers non-obvious yet predictive features, improving accuracy and offering insights.