Network Datasets Research Articles

A Robust Intrusion Detection System Empowered by Generative Adversarial Networks

There is a very bleak outlook on cyber security due to the rapid expansion of the Internet and the ever-changing terrain of cyber-attacks. This paper explores the field of intrusion detection through network analysis, with a particular emphasis on applying machine learning (ML) and deep learning (DL) approaches. For every ML/DL technique, a thorough tutorial overview is given together with a review of pertinent research publications. These studies were read, indexed, and summarised according to their thermal or temporal correlations with great care. The paper also provides information on frequently used network datasets in this field, which is relevant given the critical role that data plays in ML/DL techniques. It also discusses the difficulties in using ML/DL for cyber security and provides insightful recommendations for future lines of inquiry. Interestingly, the KDD data set shows up as a reputable industry standard for intrusion detection methods. A lot of work is being done to improve intrusion detection techniques, and both training and evaluating the detection model's quality depend equally on the quality of the data. The KDD data collection is thoroughly analysed in this research, with a special emphasis on four different attribute classes: Basic, Content, Traffic, and Host. We use the Modified Random Forest (MRF) technique to classify these properties.

Urban Road Network Connectivity Analysis Based on GIS for Aurangabad City, Maharashtra

Urban road network plays an important role in urban local network. The urban road network plays a key role in the urban spatial structure. It is the main city social-economy activities and transportation carrier. An important event of the cultural landscape is that transportation serves as a powerful factor in regional as well as national development. In connectivity network analysis, the term connectivity is known as the quantity of connections between nodes in a given network, so it derives the structure of the road network. Road network, major junctions, retail junctions, major corridors, road density and connectivity can be analyzed using the network connectivity indicators on the GIS platform. Road network dataset is created in ArcGIS which provides the number of transport lines and number of Junctions in study region. One of the most basic features of a transportation network is the network index, such as the alpha, beta, gamma index, etc. There are various indices, and each index has its individual equation for different purposes. It requires road network (line), junction (nodes) to draw connectivity index. Roads density is highest in most of the zones in Aurangabad city and lowest road density recorded in zone A in the north and E is south-west direction. he averages score of the gamma index has been increased from 0.50 in 2001 to 0.53 in 2011 where the average value also increased from 0.44 to 0.48 during this period.

Advancements and Trends in CNN-Based Handwritten Text Recognition: A Comprehensive Survey

Handwritten Text Recognition (HTR) is pivotal in transforming handwritten documents into digital format, enabling efficient search, storage, and information retrieval. In this project, we explore the application of Convolutional Neural Networks (CNNs) for HTR tasks. We comprehensively analyzed existing literature surveys to understand the current state-of-the-art techniques, methodologies, and challenges in HTR using CNNs. The survey encompassed various aspects including network architectures, dataset compositions, preprocessing techniques, and evaluation metrics. Our findings reveal the evolution of CNN-based HTR systems and highlight key trends in research, such as the integration of attention mechanisms and recurrent neural networks to enhance recognition accuracy and contextual understanding. Through this analysis, we provide insights into the advancements and future directions in CNN-based HTR methodologies.

A hybrid deep learning model for short‐term load forecasting of distribution networks integrating the channel attention mechanism

AbstractOptimizing short‐term load forecasting performance is a challenge due to the randomness of nonlinear power load and variability of system operation mode. The existing methods generally ignore how to reasonably and effectively combine the complementary advantages among them and fail to capture enough internal information from load data, resulting in accuracy reduction. To achieve accurate and efficient short‐term load forecasting, an integral implementation framework is proposed based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. CNN and GRU are first combined to fully extract the highly complicated dynamic characteristics and learn time compliance relationships of load sequence. Based on CNN‐GRU network, the channel attention mechanism is introduced to further reduce the loss of historical information and enhance the impact of important characteristics. Then, the overall framework of short‐term load forecasting based on CNN‐GRU‐Attention network is proposed, and the coupling relationship between each stage is revealed. Finally, the developed framework is implemented on realistic load dataset of distribution networks, and the experimental results verify the effectiveness of the proposed method. Compared with the state‐of‐the‐art models, the CNN‐GRU‐Attention model outperforms in different evaluation metrics.

Pure ZrO2 Ferroelectric Thin Film for Nonvolatile Memory and Neural Network Computing.

The recent discovery of ferroelectricity in pure ZrO2 has drawn much attention, but the information storage and processing performances of ferroelectric ZrO2-based nonvolatile devices remain open for further exploration. Here, a ZrO2 (∼8 nm)-based ferroelectric capacitor using RuO2 oxide electrodes is fabricated, and the ferroelectric orthorhombic phase evolution under electric field cycling is studied. A ferroelectric remnant polarization (2Pr) of >30 μC/cm2, leakage current density of ∼2.79 × 10-8 A/cm2 at 1 MV/cm, and estimated polarization retention of >10 years are achieved. When the ferroelectric capacitor is connected with a transistor, a memory window of ∼0.8 V and eight distinct states can be obtained in such a ferroelectric field-effect transistor (FeFET). Through the conductance manipulation of the FeFET, a high object image recognition accuracy of ∼93.32% is achieved on the basis of the CIFAR-10 dataset in the convolutional neural network (CNN) simulation, which is close to the result of ∼94.20% obtained by floating-point-based CNN software. These results demonstrate the potential of ferroelectric ZrO2 devices for nonvolatile memory and artificial neural network computing.

User tendency-based rating scaling in online trading networks.

Social networks often involve the users rating each other based on their beliefs, abilities, and other characteristics. This is particularly common in e-commerce platforms where buyers rate sellers based on their trustworthiness. However, the rating tends to vary between users due to differences in their individual scoring criteria. For example, in a transaction network, a positive user may give a high rating unless the transaction was unsatisfactory while a neutral user may give a mid-rating, consequently giving the same numeric score to different levels of satisfaction. In this paper, we propose a novel method called user tendency-based rating scaling, which adjusts the current rating (its score) based on the pattern of past ratings. We investigate whether this rating scaling method can classify between "good users" and "bad users" in online trade social networks better when compared with using the original rating scores without scaling. Classifying between good users and bad users is especially important for anonymous rating networks like Bitcoin transaction networks, where users' reputations must be recorded to preclude fraudulent and risky users. We evaluate the proposed rating scaling method by performing user classification, link prediction, and clustering tasks, using three real-world online rating network datasets. We use both the original ratings and the scaled ratings as weights of graphs and use a weighted graph embedding method to find node representations that reflect users' positive and negative information. The experimental results showed that using the proposed rating scaling method outperformed using the original (i.e., unscaled) ratings by up to 17% in classification accuracy, and by up to 2.5% in link prediction based on the AUC ROC measure, and by up to 21% in the clustering tasks based on the Dunn-index.

Toward Efficient Fire Detection in IoT Environment: A Modified Attention Network and Large-Scale Data Set

Toward Efficient Fire Detection in IoT Environment: A Modified Attention Network and Large-Scale Data Set

Comparing the Robustness of Simple Network Scale-Up Method Estimators.

The network scale-up method (NSUM) is a cost-effective approach to estimating the size or prevalence of a group of people that is hard to reach through a standard survey. The basic NSUM involves two steps: estimating respondents’ degrees and estimating the prevalence of the hard-to-reach population of interest using respondents’ estimated degrees and the number of people they report knowing in the hard-to-reach group. Each of these two steps involves taking either an average of ratios or a ratio of averages. Using the ratio of averages for each step has so far been the most common approach. However, the authors present theoretical arguments that using the average of ratios at the second, prevalence-estimation step often has lower mean squared error when the random mixing assumption is violated, which seems likely in practice; this estimator was proposed early in NSUM development but has largely been unexplored and unused. Simulation results using an example network data set also support these findings. On the basis of this theoretical and empirical evidence, the authors suggest that future surveys that use a simple estimator may want to use this mixed estimator, and estimation methods based on this estimator may produce new improvements.

Field studies of the Artificial Intelligence model for defining indoor thermal comfort to acknowledge the adaptive aspect

Numerous Artificial Intelligence (AI) solutions are available for achieving thermal comfort. They were either trained with limited datasets or using personalized training with limited field studies. This work assessed the model that used the ASHRAE multiple databases as the shallow supervised learning dataset for an Artificial Neural Network (ANN) based controller suitable for the residential dwellings' node. The learning accuracy can be increased to 96.1%. This paper presented the field studies to show the model performances for the common UK dwellings: the prior 1970s, the new, modular, refurbished, and the use of new materials to improve indoor thermal performance. The result shows that the model was able to perform in different environments and able to acknowledge adaptive human comfort. This was shown by the ability to represent 98.90% of the ASHRAE Standard 55 data, 6.06% improvement from the previous research. As a result, the broader comfort zone acknowledgement can lead to energy saving whilst maintaining comfort by the possibility of lowering the temperature set point. This study also proves that further energy savings can be acquired from the occupants’ presence, scheduling, and activities. These factors can increase the comfort probability to more than 10%.

Optimization of the Artificial Neural Network Algorithm with Genetic Algorithm in Stroke Prediction

This study aims to optimize Artificial Neural Network with Genetic Algorithm in predicting stroke. This research is motivated by health problems in the community that are less considered that cause a disease such as stroke. Factors of lifestyle, poor diet and other factors that can be the cause of stroke. Therefore, where later the data that has been obtained will be processed to see what factors determine the cause of stroke. The data used, namely kaggle and mendeley, will be processed using RapidMiner, with a development method (CRISP-DM) and a testing method using a Confusion Matrix. The results of this study, stroke disease classification model accuracy kaggle Artificial Neural Network dataset with Genetic Algorithm accuracy 95.13% and AUC 0.667 and mendeley dataset accuracy 98.20% and AUC 0.712. For model evaluation with Artificial Neural Network algorithm with Artificial Neural Network algorithm with kaggle dataset genetic algorithm using X-fold validation average accuracy of 95.14% and AUC 0.686.7 and mendeley dataset resulted in accuracy of 98.20% and AUC 0.712.5. So as to produce from an algorithm a new attribute from the results of the classification model that has been carried out, namely heart disease, ever married, work type and residence type

Node2Vec and Machine Learning: A Powerful Duo for Link Prediction in Social Network

Link prediction in social networks is a challenging task that attempts to uncover hidden linkages and forecast future connections. The link prediction problem is addressed in this research article by utilizing the capabilities of Node2Vec and machine learning algorithms. To learn high-dimensional node representations that capture both local and global network structures, the Node2Vec technique is used. Then, in order to forecast potential connections, these node embedding’s are put into various machine learning models. Two real-world social network datasets are used to test the suggested methodology, and the findings show a considerable improvement in link prediction accuracy. It achieves a deeper comprehension of the hidden relationships in social networks by fusing the semantic richness of Node2Vec embedding’s with the predictive powers of machine learning methods. The results of this study extend link prediction approaches in social networks by revealing hidden ties and providing insightful predictions for upcoming connections. The suggested method indicates the potential for real-world applications in a number of fields, including recommender systems, targeted advertising, and social influence studies.

Optimized Multi-Scale Mixed Dense Graph Convolution Network for Career Impression Management in College Students Based on Social Network Analysis

This investigation attempts to increase success rate of employment, entrepreneurship by addressing the dangers that college students face when seeking work and starting their own businesses in rural areas. It offers a way of social environment analysis based on employment, entrepreneurship of college students in rural areas. This approach comprehends the employment and social environment of college students' rural employment, entrepreneurship as research goal. It thoroughly examines, detects component parts of the college students' rural employment, entrepreneurship environment. In this manuscript, Optimized Multi-Scale Mixed Dense Graph Convolution Network for Career Impression Management in College Students Based on Social Network Analysis (MSMDGCN-CIM-CS-SNA) is proposed. Initially input data are gathered from Xing social network Dataset. To execute this, input data is pre-processed using Distributed Adaptive Cubature Information Filtering(DACIF) and it removes the noise from collected data; then the Pre-processed data are fed to MSMDGCN for effectively categorize Career Impression Management in College Students. In general, MSMDGCN does not express adapting optimization strategies to determine optimal parameters to ensure accurate Career Impression Management in College Students. Hence, the Sand Cat swarms optimization(SCSOA) to optimize Quantum Conditional Generative Adversarial Network which accurately Career Impression Management in College Students based on Social Media.Then the proposed MSMDGCN-CIM-CS-SNA is implemented and the performance metrics like Student Origin; Proportion of Daily Living Expenses, Student’s Graduation Trend, Selection of Career Areas for College Students, Units of College Student’s Career Intention and Computation Time the World are analyzed. Performance of the MSMDGCN-CIM-CS-SNA approach attains 18.75%, 26.89% and 32.57% higher Student’s Graduation Trend; 16.87%, 24.57% and 32.94% lower Units of College Student’s Career Intention and 18.43%, 25.64% and 31.40% lower Computation Time when analyzed through existing techniques like analysis on social environment of college students’ rural employment with entrepreneurship (ASE-CS-REE), recommendation model for college career entrepreneurship projects depend on deep learning (RM-CCE-DL), group relationship mining of college students depend on predictive social network (GRM-CS-PSN) methods respectively.

Residual Attention Augmentation Graph Neural Network for Improved Node Classification

Graph Neural Networks (GNNs) have emerged as a powerful tool for node representation learning within graph structures. However, designing a robust GNN architecture for node classification remains a challenge. This study introduces an efficient and straightforward Residual Attention Augmentation GNN (RAA-GNN) model, which incorporates an attention mechanism with skip connections to discerningly weigh node features and overcome the over-smoothing problem of GNNs. Additionally, a novel MixUp data augmentation method was developed to improve model training. The proposed approach was rigorously evaluated on various node classification benchmarks, encompassing both social and citation networks. The proposed method outperformed state-of-the-art techniques by achieving up to 1% accuracy improvement. Furthermore, when applied to the novel Twitch social network dataset, the proposed model yielded remarkably promising results. These findings provide valuable insights for researchers and practitioners working with graph-structured data.

Estimation of electrical muscle activity during gait using inertial measurement units with convolution attention neural network and small-scale dataset

In general, muscle activity can be directly measured using Electromyography (EMG) or calculated with musculoskeletal models. However, both methods are not suitable for non-technical users and unstructured environments. It is desired to establish more portable and easy-to-use muscle activity estimation methods. Deep learning (DL) models combined with inertial measurement units (IMUs) have shown great potential to estimate muscle activity. However, it frequently occurs in clinical scenarios that a very small amount of data is available and leads to limited performance of the DL models, while the augmentation techniques to efficiently expand a small sample size for DL model training are rarely used. The primary aim of the present study was to develop a novel DL model to estimate the EMG envelope during gait using IMUs with high accuracy. A secondary aim was to develop a novel model-based data augmentation method to improve the performance of the estimation model with small-scale dataset. Therefore, in the present study, a time convolutional network-based generative adversarial network, namely MuscleGAN, was proposed for data augmentation. Moreover, a subject-independent regression DL model was developed to estimate EMG envelope. Results suggested that the proposed two-stage method has better generalization and estimation performance than the commonly used existing methods. Pearson correlation coefficient and normalized root-mean-square errors derived from the proposed method reached up to 0.72 and 0.13, respectively. It was indicated that the MuscleGAN indeed improved the estimation accuracy of lower limb EMG envelope from 70% to 72%. Thus, even using only two IMUs and a very small-scale dataset, the proposed model is still capable of accurately estimating lower limb EMG envelope, demonstrating considerable potential for its application in clinical and daily life scenarios.

WalkGAN: Network Representation Learning With Sequence-Based Generative Adversarial Networks.

Network representation learning, also known as network embedding, aims to learn the low-dimensional representations of vertices while capturing and preserving the network structure. For real-world networks, the edges that represent some important relationships between the vertices of a network may be missed and may result in degenerated performance. The existing methods usually treat missing edges as negative samples, thereby ignoring the true connections between two vertices in a network. To capture the true network structure effectively, we propose a novel network representation learning method called WalkGAN, where random walk scheme and generative adversarial networks (GAN) are incorporated into a network embedding framework. Specifically, WalkGAN leverages GAN to generate the synthetic sequences of the vertices that sufficiently simulate random walk on a network and further learn vertex representations from these vertex sequences. Thus, the unobserved links between the vertices are inferred with high probability instead of treating them as nonexistence. Experimental results on the benchmark network datasets demonstrate that WalkGAN achieves significant performance improvements for vertex classification, link prediction, and visualization tasks.

Deep Self-Supervised Attributed Graph Clustering for Social Network Analysis

Deep graph clustering is an unsupervised learning task that divides nodes in a graph into disjoint regions with the help of graph auto-encoders. Currently, such methods have several problems, as follows. (1) The deep graph clustering method does not effectively utilize the generated pseudo-labels, resulting in sub-optimal model training results. (2) Each cluster has a different confidence level, which affects the reliability of the pseudo-label. To address these problems, we propose a Deep Self-supervised Attribute Graph Clustering model (DSAGC) to fully leverage the information of the data itself. We divide the proposed model into two parts: an upstream model and a downstream model. In the upstream model, we use the pseudo-label information generated by spectral clustering to form a new high-confidence distribution with which to optimize the model for a higher performance. We also propose a new reliable sample selection mechanism to obtain more reliable samples for downstream tasks. In the downstream model, we only use the reliable samples and the pseudo-label for the semi-supervised classification task without the true label. We compare the proposed method with 17 related methods on four publicly available citation network datasets, and the proposed method generally outperforms most existing methods in three performance metrics. By conducting a large number of ablative experiments, we validate the effectiveness of the proposed method.

CapMax: A Framework for Dynamic Network Representation Learning From the View of Multiuser Communication.

In this article, a modified mutual information maximization (InfoMax) framework, named channel capacity maximization (CapMax), is proposed and applied to learn informative representations for dynamic networks with time-varying topology and/or time-evolving node attributes. The CapMax is based on the network information theory for multiuser communication, where the representation model is treated as a multiaccess communication channel with memory and feedback. Without requirements of the backbone structure, the learning objective of our CapMax is maximizing the channel capacity, which is measured by directed information (DI) rather than mutual information. For efficient implementation, we design an estimator of the channel capacity through the combination of graph neural networks (GNNs) and recurrent neural networks (RNNs). Under some mild conditions, we theoretically prove that DI is a better measure than mutual information in capturing useful information. The experiments are conducted on multiple real-world dynamic network datasets, and the outperformance of our CapMax on different backbone models on link detection and prediction validates the effectiveness of modeling the representation model as a communication channel.

Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT

The proliferation of the Internet of Things (IoT) in various sectors, including healthcare, smart cities, and industrial automation, has significantly enhanced operational efficiency and service delivery. However, this widespread adoption has introduced new vulnerabilities, making IoT networks a prime target for cyberattacks. Traditional security mechanisms often fall short in protecting IoT devices due to their limited computational resources and the unique nature of IoT network traffic. This paper introduces a novel intrusion detection system (IDS) that leverages network traffic profiling and machine learning techniques tailored for the IoT ecosystem. By analyzing the behavioral patterns of network traffic, the proposed system can accurately identify malicious activities and potential threats in real-time, ensuring the integrity and confidentiality of IoT networks. The methodology encompasses data collection, feature extraction, model training, and evaluation stages, employing a combination of supervised and unsupervised machine learning algorithms to optimize detection accuracy. Experimental results, conducted on real-world IoT network datasets, demonstrate the effectiveness of our approach in detecting a wide range of cyber threats with high precision and recall rates. This research contributes to the cybersecurity domain by providing a scalable, efficient, and adaptive IDS framework that can be integrated into various IoT infrastructures to mitigate the risk of cyber intrusions.

Community detection in the human connectome: Method types, differences and their impact on inference.

Community structure is a fundamental topological characteristic of optimally organized brain networks. Currently, there is no clear standard or systematic approach for selecting the most appropriate community detection method. Furthermore, the impact of method choice on the accuracy and robustness of estimated communities (and network modularity), as well as method-dependent relationships between network communities and cognitive and other individual measures, are not well understood. This study analyzed large datasets of real brain networks (estimated from resting-state fMRI from = 5251 pre/early adolescents in the adolescent brain cognitive development [ABCD] study), and = 5338 synthetic networks with heterogeneous, data-inspired topologies, with the goal to investigate and compare three classes of community detection methods: (i) modularity maximization-based (Newman and Louvain), (ii) probabilistic (Bayesian inference within the framework of stochastic block modeling (SBM)), and (iii) geometric (based on graph Ricci flow). Extensive comparisons between methods and their individual accuracy (relative to the ground truth in synthetic networks), and reliability (when applied to multiple fMRI runs from the same brains) suggest that the underlying brain network topology plays a critical role in the accuracy, reliability and agreement of community detection methods. Consistent method (dis)similarities, and their correlations with topological properties, were estimated across fMRI runs. Based on synthetic graphs, most methods performed similarly and had comparable high accuracy only in some topological regimes, specifically those corresponding to developed connectomes with at least quasi-optimal community organization. In contrast, in densely and/or weakly connected networks with difficult to detect communities, the methods yielded highly dissimilar results, with Bayesian inference within SBM having significantly higher accuracy compared to all others. Associations between method-specific modularity and demographic, anthropometric, physiological and cognitive parameters showed mostly method invariance but some method dependence as well. Although method sensitivity to different levels of community structure may in part explain method-dependent associations between modularity estimates and parameters of interest, method dependence also highlights potential issues of reliability and reproducibility. These findings suggest that a probabilistic approach, such as Bayesian inference in the framework of SBM, may provide consistently reliable estimates of community structure across network topologies. In addition, to maximize robustness of biological inferences, identified network communities and their cognitive, behavioral and other correlates should be confirmed with multiple reliable detection methods.

Powerful tool or too powerful? Early public discourse about ChatGPT across 4 million tweets.

This paper investigates initial exuberance and emotions surrounding ChatGPT's first three months of launch (1 December 2022-1 March 2023). The impetus for studying active discussions surrounding its implications, fears, and opinions is motivated by its nascent popularity and potential to disrupt existing professions; compounded by its significance as a crucial inflexion point in history. Capturing the public zeitgeist on new innovations-much like the advent of the printing press, radio, newspapers, or the internet-provides a retrospective overview of public sentiments, common themes, and issues. Since launch, few big data studies delved into initial public discourse surrounding the chatbot. This report firstly identifies highest-engagement issues and themes that generated the most interaction; secondly, identifies the highest-engaged keywords on both sides of the sentiment valence scale (positive and negative) associated with ChatGPT. We interrogate a large twitter corpus (n = 4,251,662) of all publicly available English-language tweets containing the ChatGPT keyword. Our first research aim utilizes a prominent peaks model (upper-quartile significance threshold of prominence>20,000). Our second research aim utilized sentiment analysis to identify, week-on-week, highest-frequency negative, and positive keywords and emojis. Six prominent peaks were identified with the following themes: 'hype and hesitance', 'utility and misuse in professional and academic settings', 'demographic bias', 'philosophical thought experiments on morality' and 'artificial intelligence as a mirror of human knowledge'. Of high-frequency valence, negativity included credibility concerns, implicit bias, environmental ethics, employment rights of data annotators and programmers, the ethicality of neural network datasets. Positivity included excitement over application, especially in coding, as a creative tool, education, and personal productivity. Overall, sentiments and themes were double-edged, expressing excitement over this powerful new tool and wariness toward its potential for misuse.