Deep neural networks improve MIMO detection accuracy and efficiency.
problem Improving MIMO detection accuracy and efficiency.
method Introduced two deep architectures: a standard fully connected network and a DetNet designed for MIMO detection.
result Achieved state-of-the-art performance with low computational requirements.
Graph energy helps detect communities in networks better than traditional methods.
problem Detecting communities in sparse networks where traditional methods fail.
method Using graph energy based on the full spectrum of adjacency matrices.
result The difference in graph energy between a planted partition model and an Erdős--Rényi network has a distinct transition at the detectability threshold.
Oddnet detects anomalies in dynamic networks using time series methods.
problem Detecting anomalies in temporal networks (e.g., transport, social networks).
method Feature-based network anomaly detection using time series methods.
result Demonstrated effectiveness on synthetic and real-world datasets.
Detects change-points in similarity networks to identify anomalous nodes.
problem Detecting changes in network structure that affect node similarity.
method Sequential node-wise average similarity measures for change detection; community detection for anomaly isolation.
result Simple sequential procedure effectively identifies change-points and anomalous nodes.
Efficient neural network ensembles detect outliers.
problem Outlier detection in neural networks.
method Variational Bayesian neural networks with gradient descent sampling.
result Outlier detection results comparable to other methods.
Flexible pipeline for 3D vehicle detection from 2D images.
problem Current methods lack 3D perception of vehicles and other objects.
method Adopt any 2D detection network, fuse with 3D point cloud, develop model fitting algorithm, refine with CNN.
result 3D detection results rank second among algorithms, demonstrating competencies.
This paper uses deep learning to improve network threat detection in finance.
problem Detecting unknown threats in large-scale data applications.
method Uses deep learning for advanced threat detection.
result Improves protective measures in the financial industry.
Paper revisits PCA for anomaly detection in network security.
problem Understanding and improving anomaly detection in network security.
method Revisit probabilistic PCA model and its connection to MSNM framework.
result Mathematical model connects PCA to MSNM for anomaly detection.
Unified model for signed networks separates balance and anomaly effects.
problem Ignoring sign information in signed networks leads to inaccurate analysis.
method Low rank plus sparse matrix decomposition with regularized formulation.
result The model accurately detects communities and anomalies in signed networks.
Study on detecting hierarchical community structures in networks.
problem Detecting hierarchical community structures in networks.
method Analysis of planted hierarchies of partitions in networks, identifying additional detectability phases.
result There are additional phases in which the presence of multiple consistent partitions can either help or hinder detection of hierarchical structures.
Proposes a neural network model for detecting collective anomalies in network security.
problem Traditional anomaly detection struggles with new, unknown intrusion types.
method Trains a Long Short-Term Memory Recurrent Neural Network (LSTM RNN) on normal data to predict anomalies and uses prediction errors over time to detect collective anomalies.
result The proposed model efficiently detects collective anomalies in network security.
Generative model improves local community detection in networks.
problem Finding a single community in a large network using only a small part of it.
method Starting from a generative model for networks with community structure, approximating the unobserved parts to detect local communities.
result The proposed methods show comparable or improved results compared to state-of-the-art local community detection algorithms.
Detects anomalous inputs in neural networks using subset scanning.
problem Detecting adversarial noise and out-of-distribution samples in neural networks.
method Subset scanning applied to neural network activations using non-parametric scan statistics.
result Identifies the most anomalous subset of node activations in neural networks.
Graph change-point detection method learns graph similarity from data.
problem Detect abrupt changes in dynamic networks.
method Siamese graph neural network for graph similarity learning.
result Method detects changes in diverse types of networks with minimal data history.
ResGCN detects anomalies in attributed networks by capturing sparsity and nonlinearity.
problem Detecting anomalous nodes in attributed networks.
method Attention-based deep residual modeling using Graph Convolutional Networks.
result ResGCN effectively detects anomalies in attributed networks.
Capsule networks improve anomaly detection in high-dimensional datasets.
problem Anomaly detection in high-dimensional, class-imbalanced datasets.
method Used a capsule network architecture with autoencoder pre-training and dynamic routing.
result Capsule network outperformed other models in anomaly detection.
A new method detects communities in multi-relational networks.
problem Detect communities in heterogeneous multi-relational networks.
method Message passing based algorithm on a hierarchical structure of homogeneous networks.
result Effectiveness of the proposed method confirmed on synthetic and real-world networks.
A new probabilistic model detects communities in networks using both structure and node features.
problem Detecting communities in networks with node features for more accurate results.
method Generative probabilistic model considering network structure and node features.
result The model accurately detects communities and determines feature strength.
A new model detects complex network communities using node attributes.
problem Lack of methods integrating node attributes for community detection in attributed networks.
method BCSBM model that integrates betweenness centrality and clustering coefficient of nodes.
result BCSBM model outperforms other methods in detecting various network structures.
Network detection is an important capability in many areas of applied research in which data can be represented as a graph of entities and relationships. Oftentimes the object of interest is a relatively small subgraph in an enormous, potentially uninteresting background. This aspect characterizes network detection as …
Paper proposes CNN-based time series anomaly detection with transfer learning.
problem Time series anomaly detection in automated monitoring systems.
method CNN for segmentation, transfer learning framework, fine-tuning on unseen classes.
result Successfully tested on multiple synthetic and real data sets.
Proposes OCGNN for detecting anomalies in graph data.
problem Detecting anomalies in graph-structured data.
method One Class Graph Neural Network (OCGNN) combining Graph Neural Networks and one-class classification.
result Significant improvements in anomaly detection compared to baselines.
New method uses node features for better network community detection.
problem Lack of node feature consideration in existing community detection methods.
method Joint community detection criterion using both network edges and node features.
result Asymptotically consistent under block model with flexible feature influence.
GeoTrackNet detects maritime anomalies from AIS tracks using neural networks and a contrario detection.
problem Detecting anomalies in maritime traffic patterns for better situational awareness.
method Probabilistic neural network representation of AIS tracks and a contrario detection.
result Demonstrated relevance compared to state-of-the-art schemes on a large AIS dataset.
New deep neural network method improves change point detection.
problem Change point detection in data streams.
method Deep density ratio estimation using gradient descent objective functions.
result Deep neural network outperforms other methods on seizure detection.
Detects adversarial inputs in deep learning models without modifying the main network.
problem Vulnerability of deep learning models to adversarial inputs.
method Augment main network with observer networks that classify inputs as clean or adversarial.
result 99.5% detection accuracy on MNIST and 97.5% on CIFAR-10 datasets.
A new method detects interactions in neural networks using topological analysis.
problem Detecting interactions between input features in neural networks.
method Topological analysis of neural network connectivity to quantify interaction strength.
result The PID algorithm outperforms state-of-the-art baselines in interaction detection tasks.
Paper detects anomalous edges in social networks using edge exchangeability.
problem Detecting anomalous edges in directed social networks.
method Exploits edge exchangeability and uses conformal prediction theory.
result Proposed anomaly detector has a guaranteed upper bound for false positives.
GraphPrints detects anomalies in network flow data by analyzing graphlets.
problem Detecting anomalies in network flow data.
method Representing network flow as graphs, counting graphlets, and detecting outliers.
result Initial testing shows low false positive rates and high true positive rates.
TGN outperforms static GNNs in detecting financial fraud.
problem Anomaly detection in dynamic financial networks.
method Temporal Graph Networks (TGN) for capturing edge dynamics.
result TGN significantly outperforms static GNNs in AUC metrics.
GEE detects and explains network anomalies without labeled data.
problem Detecting and explaining network anomalies without labeled data.
method GEE combines VAE for anomaly detection and gradient-based fingerprinting for explanation.
result GEE effectively detects and explains various network anomalies.
NN-CUSUM detects changes in high-dimensional data using neural networks.
problem Detecting abrupt changes in high-dimensional data.
method Neural network-based CUSUM for online change-point detection.
result NN-CUSUM performs well in detecting changes in high-dimensional data.
S4ND detects lung nodules faster and more accurately.
problem Efficient lung nodule detection from CT scans.
method Single-Shot Single-Scale 3D Convolutional Neural Network (CNN) trained end-to-end.
result S4ND outperforms state-of-the-art methods in terms of efficiency and accuracy.
AnomalyDAE detects anomalies in networks by learning cross-modality interactions.
problem Detecting anomalies in attributed networks where structure and attributes interact.
method Dual autoencoder framework with attention mechanism for joint learning of structure and attribute embeddings.
result AnomalyDAE effectively detects anomalies by reconstructing node attributes and structures.
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.
GANs improve anomaly detection, faster and better.
problem Anomaly detection in high-dimensional data.
method Leveraging recent GAN models for anomaly detection.
result State-of-the-art performance on image and network intrusion datasets, 400x faster.
New CUSUM method detects changes in Hawkes networks efficiently.
problem Detecting abrupt changes in Hawkes networks.
method Efficient online recursive implementation of CUSUM statistic.
result Proposed CUSUM method outperforms existing methods.
A new modularity density measure improves community detection in heterogeneous networks.
problem Detecting meaningful communities in heterogeneous networks.
method Formulated a novel metric, modularity density, for undirected, weighted networks.
result Maximization of modularity density is free from bias and better at detecting weakly-separated communities.
Enhanced neural networks detect thin boundaries between different types of anomalies.
problem Detecting thin boundaries between different types of anomalies in outlier detection.
method Defined new types of anomalies, enhanced Multi-Layer Perceptron Neural Network with Genetic Algorithm.
result Reduced error in anomaly detection compared to baseline methods.
Enhances Infomap algorithm to prioritize metadata in community detection.
problem Balancing metadata importance in community detection.
method Introduces a tuning parameter to the Infomap algorithm.
result Improves mutual information with metadata at the cost of structural detectability.
Method detects shared and private communities in multilayer networks.
problem Detecting shared and private communities in multilayer networks.
method Variational Bayes approach for jointly inferring shared and unshared hidden communities.
result Our method outperforms state-of-the-art algorithms in detecting communities.
Enhances anomaly detection in high dimensions with pretrained networks.
problem Difficult to characterize anomaly in high-dimensional data.
method Residual adaptation to adjust pretrained networks for anomaly detection.
result Significantly outperforms existing methods on anomaly detection benchmarks.
Real-time anomaly detection and prediction system using Bayesian Networks.
problem Detect and predict anomalies in real-time systems.
method Develops a supervised machine learning model using Bayesian Network framework.
result Simultaneous anomaly detection and prediction achieved.
RACDNN improves saliency detection by iteratively refining attention to multiple scales.
problem Saliency detection struggles with objects of varying scales.
method Recurrent attentional convolutional-deconvolution network (RACDNN) using spatial transformer and recurrent units.
result RACDNN outperforms state-of-the-art methods on saliency detection datasets.
Novel active learning detects network nodes for community detection.
problem Detecting community structure in networks.
method Maximal Expected Model Change (MEMC) criterion for querying network nodes.
result MEMC detects nodes that maximize community assignment likelihood changes.
Study shows RNNs are effective for trend detection in time series.
problem Detecting trends in noisy time series data.
method Empirical investigation of standard RNNs for trend detection using simulated data.
result Standard RNNs structures outperform other estimators in trend detection.
Integrates outlier detection into neural networks for improved performance.
problem Lack of competency awareness in machine learning systems, especially in detecting outliers.
method Null Space Analysis (NuSA) of neural networks, computing and controlling null space projection.
result NuSA-trained networks maintain classification performance and detect outliers effectively.
Paper uses GNNs to automatically detect botnets from network data.
problem Detecting botnets from network data using traditional methods is challenging.
method Synthesized botnet connections, tailored graph neural networks (GNNs), and deep learning.
result GNNs outperform traditional methods in botnet detection.