An Efficient Modelling of Oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

Anomaly-Based Intrusion DetectionModel Using Deep Learning for IoT Networks

The rapid growth of Internet of Things(IoT)devices has brought numerous benefits to the interconnected world.However,the ubiquitous nature of IoT networks exposes them to various security threats,including anomaly intrusion attacks.In addition,IoT devices generate a high volume of unstructured data.Traditional intrusion detection systems often struggle to cope with the unique characteristics of IoT networks,such as resource constraints and heterogeneous data sources.Given the unpredictable nature of network technologies and diverse intrusion methods,conventional machine-learning approaches seem to lack efficiency.Across numerous research domains,deep learning techniques have demonstrated their capability to precisely detect anomalies.This study designs and enhances a novel anomaly-based intrusion detection system(AIDS)for IoT networks.Firstly,a Sparse Autoencoder(SAE)is applied to reduce the high dimension and get a significant data representation by calculating the reconstructed error.Secondly,the Convolutional Neural Network(CNN)technique is employed to create a binary classification approach.The proposed SAE-CNN approach is validated using the Bot-IoT dataset.The proposed models exceed the performance of the existing deep learning approach in the literature with an accuracy of 99.9%,precision of 99.9%,recall of 100%,F1 of 99.9%,False Positive Rate(FPR)of 0.0003,and True Positive Rate(TPR)of 0.9992.In addition,alternative metrics,such as training and testing durations,indicated that SAE-CNN performs better.

A Power Data Anomaly Detection Model Based on Deep Learning with Adaptive Feature Fusion

With the popularisation of intelligent power,power devices have different shapes,numbers and specifications.This means that the power data has distributional variability,the model learning process cannot achieve sufficient extraction of data features,which seriously affects the accuracy and performance of anomaly detection.Therefore,this paper proposes a deep learning-based anomaly detection model for power data,which integrates a data alignment enhancement technique based on random sampling and an adaptive feature fusion method leveraging dimension reduction.Aiming at the distribution variability of power data,this paper developed a sliding window-based data adjustment method for this model,which solves the problem of high-dimensional feature noise and low-dimensional missing data.To address the problem of insufficient feature fusion,an adaptive feature fusion method based on feature dimension reduction and dictionary learning is proposed to improve the anomaly data detection accuracy of the model.In order to verify the effectiveness of the proposed method,we conducted effectiveness comparisons through elimination experiments.The experimental results show that compared with the traditional anomaly detection methods,the method proposed in this paper not only has an advantage in model accuracy,but also reduces the amount of parameter calculation of the model in the process of feature matching and improves the detection speed.

Enhancing Log Anomaly Detection with Semantic Embedding and Integrated Neural Network Innovations

System logs,serving as a pivotal data source for performance monitoring and anomaly detection,play an indispensable role in assuring service stability and reliability.Despite this,the majority of existing log-based anomaly detection methodologies predominantly depend on the sequence or quantity attributes of logs,utilizing solely a single Recurrent Neural Network(RNN)and its variant sequence models for detection.These approaches have not thoroughly exploited the semantic information embedded in logs,exhibit limited adaptability to novel logs,and a single model struggles to fully unearth the potential features within the log sequence.Addressing these challenges,this article proposes a hybrid architecture based on amultiscale convolutional neural network,efficient channel attention and mogrifier gated recurrent unit networks(LogCEM),which amalgamates multiple neural network technologies.Capitalizing on the superior performance of robustly optimized BERT approach(RoBERTa)in the realm of natural language processing,we employ RoBERTa to extract the original word vectors from each word in the log template.In conjunction with the enhanced Smooth Inverse Frequency(SIF)algorithm,we generate more precise log sentence vectors,thereby achieving an in-depth representation of log semantics.Subsequently,these log vector sequences are fed into a hybrid neural network,which fuses 1D Multi-Scale Convolutional Neural Network(MSCNN),Efficient Channel Attention Mechanism(ECA),and Mogrifier Gated Recurrent Unit(GRU).This amalgamation enables themodel to concurrently capture the local and global dependencies of the log sequence and autonomously learn the significance of different log sequences,thereby markedly enhancing the efficacy of log anomaly detection.To validate the effectiveness of the LogCEM model,we conducted evaluations on two authoritative open-source datasets.The experimental results demonstrate that LogCEM not only exhibits excellent accuracy and robustness,but also outperforms the current mainstream log anomaly detection methods.

COVAD: Content-oriented video anomaly detection using a self attention-based deep learning model

Background Video anomaly detection has always been a hot topic and has attracted increasing attention.Many of the existing methods for video anomaly detection depend on processing the entire video rather than considering only the significant context. Method This paper proposes a novel video anomaly detection method called COVAD that mainly focuses on the region of interest in the video instead of the entire video. Our proposed COVAD method is based on an autoencoded convolutional neural network and a coordinated attention mechanism,which can effectively capture meaningful objects in the video and dependencies among different objects. Relying on the existing memory-guided video frame prediction network, our algorithm can significantly predict the future motion and appearance of objects in a video more effectively. Result The proposed algorithm obtained better experimental results on multiple datasets and outperformed the baseline models considered in our analysis. Simultaneously, we provide an improved visual test that can provide pixel-level anomaly explanations.

Anomalous node detection in attributed social networks using dual variational autoencoder with generative adversarial networks

Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.

Explainable Anomaly Detection Using Vision Transformer Based SVDD

Explainable AI extracts a variety of patterns of data in the learning process and draws hidden information through the discovery of semantic relationships.It is possible to offer the explainable basis of decision-making for inference results.Through the causality of risk factors that have an ambiguous association in big medical data,it is possible to increase transparency and reliability of explainable decision-making that helps to diagnose disease status.In addition,the technique makes it possible to accurately predict disease risk for anomaly detection.Vision transformer for anomaly detection from image data makes classification through MLP.Unfortunately,in MLP,a vector value depends on patch sequence information,and thus a weight changes.This should solve the problem that there is a difference in the result value according to the change in the weight.In addition,since the deep learning model is a black box model,there is a problem that it is difficult to interpret the results determined by the model.Therefore,there is a need for an explainablemethod for the part where the disease exists.To solve the problem,this study proposes explainable anomaly detection using vision transformerbasedDeep Support Vector Data Description(SVDD).The proposed method applies the SVDD to solve the problem of MLP in which a result value is different depending on a weight change that is influenced by patch sequence information used in the vision transformer.In order to draw the explainability of model results,it visualizes normal parts through Grad-CAM.In health data,both medical staff and patients are able to identify abnormal parts easily.In addition,it is possible to improve the reliability of models and medical staff.For performance evaluation normal/abnormal classification accuracy and f-measure are evaluated,according to whether to apply SVDD.Evaluation Results The results of classification by applying the proposed SVDD are evaluated excellently.Therefore,through the proposed method,it is possible to improve the reliability of decision-making by identifying the location of the disease and deriving consistent results.

Performance Analysis of Hybrid RR Algorithm for Anomaly Detection in Streaming Data

Automated live video stream analytics has been extensively researched in recent times.Most of the traditional methods for video anomaly detection is supervised and use a single classifier to identify an anomaly in a frame.We propose a 3-stage ensemble-based unsupervised deep reinforcement algorithm with an underlying Long Short Term Memory(LSTM)based Recurrent Neural Network(RNN).In the first stage,an ensemble of LSTM-RNNs are deployed to generate the anomaly score.The second stage uses the least square method for optimal anomaly score generation.The third stage adopts award-based reinforcement learning to update the model.The proposed Hybrid Ensemble RR Model was tested on standard pedestrian datasets UCSDPed1,USDPed2.The data set has 70 videos in UCSD Ped1 and 28 videos in UCSD Ped2 with a total of 18560 frames.Since a real-time stream has strict memory constraints and storage issues,a simple computing machine does not suffice in performing analytics with stream data.Hence the proposed research is designed to work on a GPU(Graphics Processing Unit),TPU(Tensor Processing Unit)supported framework.As shown in the experimental results section,recorded observations on framelevel EER(Equal Error Rate)and AUC(Area Under Curve)showed a 9%reduction in EER in UCSD Ped1,a 13%reduction in ERR in UCSD Ped2 and a 4%improvement in accuracy in both datasets.

Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

Presently,video surveillance is commonly employed to ensure security in public places such as traffic signals,malls,railway stations,etc.A major chal-lenge in video surveillance is the identification of anomalies that exist in it such as crimes,thefts,and so on.Besides,the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety.The recent advances of Deep Learning(DL)models have received considerable attention in different processes such as object detec-tion,image classification,etc.In this aspect,this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking(PFPN-ADT)model for pedestrian walkways.The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles,skaters,etc.The proposed model involves panoptic seg-mentation model,called Panoptic Feature Pyramid Network(PFPN)is employed for the object recognition process.For object classification,Compact Bat Algo-rithm(CBA)with Stacked Auto Encoder(SAE)is applied for the classification of recognized objects.For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique,a comparison study is made using Uni-versity of California San Diego(UCSD)Anomaly data and other benchmark data-sets(such as Cityscapes,ADE20K,COCO),and the outcomes are compared with the Mask Recurrent Convolutional Neural Network(RCNN)and Faster Convolu-tional Neural Network(CNN)models.The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.

An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

In the present technological world,surveillance cameras generate an immense amount of video data from various sources,making its scrutiny tough for computer vision specialists.It is difficult to search for anomalous events manually in thesemassive video records since they happen infrequently and with a low probability in real-world monitoring systems.Therefore,intelligent surveillance is a requirement of the modern day,as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies.In this article,we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video(ADSV).At the input of the ADSV,a shots boundary detection technique is used to segment prominent frames.Next,The Lightweight ConvolutionNeuralNetwork(LWCNN)model receives the segmented frames to extract spatial and temporal information from the intermediate layer.Following that,spatial and temporal features are learned using Long Short-Term Memory(LSTM)cells and Attention Network from a series of frames for each anomalous activity in a sample.To detect motion and action,the LWCNN received chronologically sorted frames.Finally,the anomaly activity in the video is identified using the proposed trained ADSV model.Extensive experiments are conducted on complex and challenging benchmark datasets.In addition,the experimental results have been compared to state-ofthe-artmethodologies,and a significant improvement is attained,demonstrating the efficiency of our ADSV method.

Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection

The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspec-tives.The meme stock mania of 2021 brought together stock traders and investors that were also active on social media.This mania was in good part driven by retail investors’discussions on investment strategies that occurred on social media plat-forms such as Reddit during the COVID-19 lockdowns.The stock trades by these retail investors were then executed using services like Robinhood.In this paper,machine learning models are used to try and predict the stock price movements of two meme stocks:GameStop($GME)and AMC Entertainment($AMC).Two sentiment metrics of the daily social media discussions about these stocks on Red-dit are generated and used together with 85 other fundamental and technical indi-cators as the feature set for the machine learning models.It is demonstrated that through the use of a carefully chosen mix of a meme stock’s fundamental indica-tors,technical indicators,and social media sentiment scores,it is possible to pre-dict the stocks’next-day closing prices.Also,using an anomaly detection model,and the daily Reddit discussions about a meme stock,it was possible to identify potential market manipulators.

A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

An anomaly-based intrusion detection system(A-IDS)provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered.It prevalently utilizes several machine learning algorithms(ML)for detecting and classifying network traffic.To date,lots of algorithms have been proposed to improve the detection performance of A-IDS,either using individual or ensemble learners.In particular,ensemble learners have shown remarkable performance over individual learners in many applications,including in cybersecurity domain.However,most existing works still suffer from unsatisfactory results due to improper ensemble design.The aim of this study is to emphasize the effectiveness of stacking ensemble-based model for A-IDS,where deep learning(e.g.,deep neural network[DNN])is used as base learner model.The effectiveness of the proposed model and base DNN model are benchmarked empirically in terms of several performance metrics,i.e.,Matthew’s correlation coefficient,accuracy,and false alarm rate.The results indicate that the proposed model is superior to the base DNN model as well as other existing ML algorithms found in the literature.

ADS-B Anomaly Data Detection Model Based on Deep Learning and Difference of Gaussian Approach

Due to the influence of terrain structure,meteorological conditions and various factors,there are anomalous data in automatic dependent surveillance-broadcast(ADS-B)message.The ADS-B equipment can be used for positioning of general aviation aircraft.Aim to acquire the accurate position information of aircraft and detect anomaly data,the ADS-B anomaly data detection model based on deep learning and difference of Gaussian(DoG)approach is proposed.First,according to the characteristic of ADS-B data,the ADS-B position data are transformed into the coordinate system.And the origin of the coordinate system is set up as the take-off point.Then,based on the kinematic principle,the ADS-B anomaly data can be removed.Moreover,the details of the ADS-B position data can be got by the DoG approach.Finally,the long short-term memory(LSTM)neural network is used to optimize the recurrent neural network(RNN)with severe gradient reduction for processing ADS-B data.The position data of ADS-B are reconstructed by the sequence to sequence(seq2seq)model which is composed of LSTM neural network,and the reconstruction error is used to detect the anomalous data.Based on the real flight data of general aviation aircraft,the simulation results show that the anomaly data can be detected effectively by the proposed method of reconstructing ADS-B data with the seq2seq model,and its running time is reduced.Compared with the RNN,the accuracy of anomaly detection is increased by 2.7%.The performance of the proposed model is better than that of the traditional anomaly detection models.

Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems

The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels.With the massive number of connected devices,opportunities for potential network attacks are nearly unlimited.An additional problem is that many low-cost devices are not equippedwith effective security protection so that they are easily hacked and applied within a network of bots(botnet)to perform distributed denial of service(DDoS)attacks.In this paper,we propose a novel intrusion detection system(IDS)based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems.The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies.An additional feature of the proposed IDS is that it is trained with an optimized dataset,where the number of features is reduced by 94%without classification accuracy loss.Thus,the proposed IDS remains stable in response to slight system perturbations,which do not represent network anomalies.The proposed approach is evaluated under different simulation scenarios and provides a 99%detection accuracy over known datasets while reducing the training time by an order of magnitude.

A Hybrid Deep Learning-Based Unsupervised Anomaly Detection in High Dimensional Data

Anomaly detection in high dimensional data is a critical research issue with serious implication in the real-world problems.Many issues in this field still unsolved,so several modern anomaly detection methods struggle to maintain adequate accuracy due to the highly descriptive nature of big data.Such a phenomenon is referred to as the“curse of dimensionality”that affects traditional techniques in terms of both accuracy and performance.Thus,this research proposed a hybrid model based on Deep Autoencoder Neural Network(DANN)with five layers to reduce the difference between the input and output.The proposed model was applied to a real-world gas turbine(GT)dataset that contains 87620 columns and 56 rows.During the experiment,two issues have been investigated and solved to enhance the results.The first is the dataset class imbalance,which solved using SMOTE technique.The second issue is the poor performance,which can be solved using one of the optimization algorithms.Several optimization algorithms have been investigated and tested,including stochastic gradient descent(SGD),RMSprop,Adam and Adamax.However,Adamax optimization algorithm showed the best results when employed to train theDANNmodel.The experimental results show that our proposed model can detect the anomalies by efficiently reducing the high dimensionality of dataset with accuracy of 99.40%,F1-score of 0.9649,Area Under the Curve(AUC)rate of 0.9649,and a minimal loss function during the hybrid model training.

Review of Anomaly Detection Systems in Industrial Control Systems Using Deep Feature Learning Approach

Industrial Control Systems (ICS) or SCADA networks are increasingly targeted by cyber-attacks as their architectures shifted from proprietary hardware, software and protocols to standard and open sources ones. Furthermore, these systems which used to be isolated are now interconnected to corporate networks and to the Internet. Among the countermeasures to mitigate the threats, anomaly detection systems play an important role as they can help detect even unknown attacks. Deep learning which has gained a great attention in the last few years due to excellent results in image, video and natural language processing is being used for anomaly detection in information security, particularly in SCADA networks. The salient features of the data from SCADA networks are learnt as hierarchical representation using deep architectures, and those learnt features are used to classify the data into normal or anomalous ones. This article is a review of various architectures such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Stacked Autoencoder (SAE), Long Short Term Memory (LSTM), or a combination of those architectures, for anomaly detection purpose in SCADA networks.

Arithmetic Optimization with Deep Learning Enabled Anomaly Detection in Smart City

In recent years,Smart City Infrastructures(SCI)have become familiar whereas intelligent models have been designed to improve the quality of living in smart cities.Simultaneously,anomaly detection in SCI has become a hot research topic and is widely explored to enhance the safety of pedestrians.The increasing popularity of video surveillance system and drastic increase in the amount of collected videos make the conventional physical investigation method to identify abnormal actions,a laborious process.In this background,Deep Learning(DL)models can be used in the detection of anomalies found through video surveillance systems.The current research paper develops an Internet of Things Assisted Deep Learning Enabled Anomaly Detection Technique for Smart City Infrastructures,named(IoTAD-SCI)technique.The aim of the proposed IoTAD-SCI technique is to mainly identify the existence of anomalies in smart city environment.Besides,IoTAD-SCI technique involves Deep Consensus Network(DCN)model design to detect the anomalies in input video frames.In addition,Arithmetic Optimization Algorithm(AOA)is executed to tune the hyperparameters of the DCN model.Moreover,ID3 classifier is also utilized to classify the identified objects in different classes.The experimental analysis was conducted for the proposed IoTADSCI technique upon benchmark UCSD anomaly detection dataset and the results were inspected under different measures.The simulation results infer the superiority of the proposed IoTAD-SCI technique under different metrics.

Deep Industrial Image Anomaly Detection: A Survey

The recent rapid development of deep learning has laid a milestone in industrial image anomaly detection(IAD).In this pa-per,we provide a comprehensive review of deep learning-based image anomaly detection techniques,from the perspectives of neural net-work architectures,levels of supervision,loss functions,metrics and datasets.In addition,we extract the promising setting from indus-trial manufacturing and review the current IAD approaches under our proposed setting.Moreover,we highlight several opening chal-lenges for image anomaly detection.The merits and downsides of representative network architectures under varying supervision are discussed.Finally,we summarize the research findings and point out future research directions.More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

Anomaly detection of earthquake precursor data using long short-term memory networks

Earthquake precursor data have been used as an important basis for earthquake prediction.In this study,a recurrent neural network(RNN)architecture with long short-term memory(LSTM)units is utilized to develop a predictive model for normal data.Furthermore,the prediction errors from the predictive models are used to indicate normal or abnormal behavior.An additional advantage of using the LSTM networks is that the earthquake precursor data can be directly fed into the network without any elaborate preprocessing as required by other approaches.Furthermore,no prior information on abnormal data is needed by these networks as they are trained only using normal data.Experiments using three groups of real data were conducted to compare the anomaly detection results of the proposed method with those of manual recognition.The comparison results indicated that the proposed LSTM network achieves promising results and is viable for detecting anomalies in earthquake precursor data.

Multi-Layer Reconstruction Errors Autoencoding and Density Estimate for Network Anomaly Detection

Anomaly detection is an important method for intrusion detection.In recent years,unsupervised methods have been widely researched because they do not require labeling.For example,a nonlinear autoencoder can use reconstruction errors to attain the discrimination threshold.This method is not effective when the model complexity is high or the data contains noise.The method for detecting the density of compressed features in a hidden layer can be used to reduce the influence of noise on the selection of the threshold because the density of abnormal data in hidden layers is smaller than normal data.However,compressed features may lose some of the high-dimensional distribution information of the original data.In this paper,we present an efficient anomaly detection framework for unsupervised anomaly detection,which includes network data capturing,processing,feature extraction,and anomaly detection.We employ a deep autoencoder to obtain compressed features and multi-layer reconstruction errors,and feeds them the same to the Gaussian mixture model to estimate the density.The proposed approach is trained and tested on multiple current intrusion detection datasets and real network scenes,and performance indicators,namely accuracy,recall,and F1-score,are better than other autoencoder models.