The continuous advancement of DDoS attack technology and an increasing number of IoT devices connected on 5G networks escalate the level of difficulty for DDoS mitigation. A growing number of researchers have started ...
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The continuous advancement of DDoS attack technology and an increasing number of IoT devices connected on 5G networks escalate the level of difficulty for DDoS mitigation. A growing number of researchers have started to utilise Deep Learning algorithms to improve the performance of DDoS mitigation systems. Real DDoS attack data has no labels, and hence, we present an intelligent attack mitigation (IAM) system, which takes an ensemble approach by employing Recurrent Autonomous autoencoders (RAA) as basic learners with a majority voting scheme. The RAA is a target-driven, distributionenabled, and imbalanced clustering algorithm, which is designed to work with the ISP's blackholing mechanism for DDoS flood attack mitigation. It can dynamically select features, decide a reference target (RT), and determine an optimal threshold to classify network traffic. A novel Comparison-Max Random Walk algorithm is used to determine the RT, which is used as an instrument to direct the model to classify the data so that the predicted positives are close or equal to the RT. We also propose Estimated Evaluation Metrics (EEM) to evaluate the performance of unsupervised models. The IAM system is tested with UDP flood, TCP flood, ICMP flood, multi-vector and a real UDP flood attack data. Additionally, to check the scalability of the IAM system, we tested it on every subdivided data set for distributed computing. The average Recall on all data sets was above 98%.
The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever ***,the exploration of IoT services also means that people ...
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The era of the Internet of things(IoT)has marked a continued exploration of applications and services that can make people’s lives more convenient than ever ***,the exploration of IoT services also means that people face unprecedented difficulties in spontaneously selecting the most appropriate ***,there is a paramount need for a recommendation system that can help improve the experience of the users of IoT services to ensure the best quality of *** of the existing techniques—including collaborative filtering(CF),which is most widely adopted when building recommendation systems—suffer from rating sparsity and cold-start problems,preventing them from providing high quality *** by the great success of deep learning in a wide range of fields,this work introduces a deep-learning-enabled autoencoder architecture to overcome the setbacks of CF *** proposed deep learning model is designed as a hybrid architecture with three key networks,namely autoencoder(AE),multilayered perceptron(MLP),and generalized matrix factorization(GMF).The model employs two AE networks to learn deep latent feature representations of users and items respectively and in ***,MLP and GMF networks are employed to model the linear and non-linear user-item interactions respectively with the extracted latent user and item ***,the rating prediction is performed based on the idea of ensemble learning by fusing the output of the GMF and MLP *** conducted extensive experiments on two benchmark datasets,MoiveLens100K and MovieLens1M,using four standard evaluation *** experiments were conducted to confirm the validity of the proposed model and the contribution of each of its components in achieving better recommendation *** analyses were also carried out to demonstrate the potential of the proposed model in gaining better accuracy than the existing CF methods with resistance to rat
Anomaly detection (AD) is a crucial task for detecting salient objects in cluttered backgrounds. Classical AD algorithms based on statistical models or geometric models have achieved acceptable detection results. Howe...
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Anomaly detection (AD) is a crucial task for detecting salient objects in cluttered backgrounds. Classical AD algorithms based on statistical models or geometric models have achieved acceptable detection results. However, most of them do not hierarchically extract deep features or consider the spatial structure of the images. We propose a method that combines the reconstruction error of autoencoder (AE) and spatial morphological characteristics to estimate anomalousness. The reconstruction errors and the spatially dominant information are comprehensively considered. Specifically, given the compression capability of the AE, we use the dimensionality reduction results obtained by encoding for analyzing local pixel difference;an adaptive dual window is employed in this process. The morphological transformation commonly used in edge detection is utilized to refine the small space anomalies. Experimental results on different hyperspectral images show that the proposed AE and spatial morphology extraction-based approach significantly surpasses several traditional alternatives. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)
Network embedding aims to represent vertices in the network with low-dimensional dense real number vectors, so that the attained vertices can acquire the ability of representation and inference in vector space. With t...
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Network embedding aims to represent vertices in the network with low-dimensional dense real number vectors, so that the attained vertices can acquire the ability of representation and inference in vector space. With the expansion of the scale of complex networks, how to make the high-dimensional network represented in low-dimensional vector space through network becomes an important issue. The typical algorithms of current autoencoder-based network embedding methods include DNGR and SDNE. DNGR method trains the Positive Pointwise Mutual Information (PPMI) matrix with the Stacked Denosing autoencoder (SDAE), which is lacking in depth thereby attaining less satisfactory representation of network. Besides, SDNE used a semi-supervised autoencoder for embedding the adjacency matrix, whose sparsity may generate more cost in the learning process. In order to solve these problems, we propose a novel autoencoder-based Network Embedding Algorithm (AENEA). AENEA is mainly divided into three steps. First, the random surfing model is used to process the original network to obtain the Probabilistic Co-occurrence (PCO) matrix between the nodes. Secondly, the Probabilistic Co-occurrence (PCO) matrix is processed to generate the corresponding Positive Pointwise Mutual Information (PPMI) matrix. Finally, the PPMI matrix is used to learn the representation of vertices in the network by using a semi-supervised autoencoder. We implemented a series of experiments to test the performance of AENEA, DNGR, SDNE and so on, on the standardized datasets 20-NewsGroup and Wine. The experimental results show that the performance of AENEA is obviously superior to the existing algorithms in clustering, classification and visualization tasks.
Conventional image formats have limited information conveyance, while Hyperspectral Imaging (HSI) offers a broader representation through continuous spectral bands, capturing hundreds of spectral features. However, th...
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Conventional image formats have limited information conveyance, while Hyperspectral Imaging (HSI) offers a broader representation through continuous spectral bands, capturing hundreds of spectral features. However, this abundance leads to redundant information, posing a computational challenge for deep learning models. Thus, models must effectively extract indicative features. HSI's non-linear nature, influenced by environmental factors, necessitates both linear and non-linear modeling techniques for feature extraction. While PCA and ICA, being linear methods, may overlook complex patterns, autoencoders (AE) can capture and represent non-linear features. Yet, AEs can be biased by unbalanced datasets, emphasizing majority class features and neglecting minority class characteristics, highlighting the need for careful dataset preparation. To address this, the Dual-Path AE (D-Path-AE) model has been proposed, which enhances non-linear feature acquisition through concurrent encoding pathways. This model also employs a down-sampling strategy to reduce bias towards majority classes. The study compared the efficacy of dimensionality reduction using the Na & iuml;ve autoencoder (Na & iuml;ve AE) and D-Path-AE. Classification capabilities were assessed using Decision Tree, Support Vector Machine, and K-Nearest Neighbors (KNN) classifiers on datasets from Pavia Center, Salinas, and Kennedy Space Center. Results demonstrate that the D-Path-AE outperforms both linear dimensionality reduction models and Na & iuml;ve AE, achieving an Overall Accuracy of up to 98.31% on the Pavia Center dataset using the KNN classifier, indicating superior classification capabilities.
autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'***,existing methods still have two significant limitations:i)Exte...
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autoencoder-based rating prediction methods with external attributes have received wide attention due to their ability to accurately capture users'***,existing methods still have two significant limitations:i)External attributes are often unavailable in the real world due to privacy issues,leading to low quality of representations;and ii)existing methods lack considering complex associations in users'rating behaviors during the encoding *** meet these challenges,this paper innovatively proposes an inherent-attribute-aware dual-graph autoencoder,named IADGAE,for rating *** address the low quality of representations due to the unavailability of external attributes,we propose an inherent attribute perception module that mines inductive user active patterns and item popularity patterns from users'rating behaviors to strengthen user and item *** exploit the complex associations hidden in users’rating behaviors,we design an encoder on the item-item co-occurrence graph to capture the co-occurrence frequency features among ***,we propose a dual-graph feature encoder framework to simultaneously encode and fuse the high-order representations learned from the user-item rating graph and item-item co-occurrence *** experiments on three real datasets demonstrate that IADGAE is effective and outperforms existing rating prediction methods,which achieves a significant improvement of 4.51%~41.63%in the RMSE metric.
Due to the high complexity and dynamics of the semiconductor manufacturing process, various process abnormality could result in wafer map defects in many work stations. Thus, wafer map pattern recognition (WMPR) in th...
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Due to the high complexity and dynamics of the semiconductor manufacturing process, various process abnormality could result in wafer map defects in many work stations. Thus, wafer map pattern recognition (WMPR) in the semiconductor manufacturing process can help operators to troubleshoot root causes of the out-of-control process and then accelerate the process adjustment. This article proposes a novel deep neural network (DNN), two-dimensional principal component analysis-based convolutional autoencoder (PCACAE) for wafer map defect recognition. First, a new convolution kernel based on conditional two-dimensional principal component analysis is developed to construct the first convolutional block of PCACAE. Second, a convolutional autoencoder is cascaded by considering the nonlinearity of data representation. The second convolutional block of PCACAE is constructed based on the encoding part. Finally, the pretrained PCACAE is fine-tuned to obtain the final classifier. PCACAE is successfully applied for feature learning and recognition of wafer map defects. The experimental results on a real-world case demonstrate that PCACAE is superior to other well-known convolutional neural networks (e.g., GoogLeNet, PCANet) on WMPR.
With the development of social networks, the spread of fake news brings great negative effects to people's daily life, and even causes social panic. Fake news can be regarded as an anomaly on social networks, and ...
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With the development of social networks, the spread of fake news brings great negative effects to people's daily life, and even causes social panic. Fake news can be regarded as an anomaly on social networks, and autoencoder can be used as the basic unsupervised learning method. So, an unsupervised fake news detection method based on autoencoder (UFNDA) is proposed. This paper firstly considers some forms of news in social networks, integrates the text content, images, propagation, and user information of publishing news to improve the performance of fake news detection. Next, to obtain the hidden information and internal relationship between features, Bidirectional GRU(Bi-GRU) layer and Self-Attention layer are added into the autoencoder, and then reconstruct residual to detect fake news. The experimental results compared with the existence of other four methods, on two real-world datasets, show that UFNDA obtains the more positive results.
A hyperspectral image (HSI) contains hundreds of spectral bands, which provide detailed spectral information, thus offering an inherent advantage in classification. The successful launch of the Gaofen-5 and ZY-1 02D h...
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A hyperspectral image (HSI) contains hundreds of spectral bands, which provide detailed spectral information, thus offering an inherent advantage in classification. The successful launch of the Gaofen-5 and ZY-1 02D hyperspectral satellites has promoted the need for large-scale geological applications, such as mineral and lithological mapping (LM). In recent years, following the success of computer vision, deep learning methods have shown their advantage in solving the problem of hyperspectral classification. However, the combination of deep learning and HSI to solve the problem of geological mapping is insufficient. We propose a new 3D convolutional autoencoder for LM. A pixel-based and cube-based 3D convolutional neural network architecture is designed to extract spatial-spectral features. Traditional and machine learning methods are employed as competing methods, trained on two real hyperspectral datasets, and evaluated according to the overall accuracy, F1 score, and other metrics. Results indicate that the proposed method can provide convincing results for LM applications on the basis of the hyperspectral data provided by the ZY-1 02D satellite. Compared with traditional methods, the combination of deep learning and hyperspectral can provide more efficient and highly accurate results. The proposed method has better robustness than supervised learning methods and shows great promise under small sample conditions. As far as we know, this work is the first attempt to apply unsupervised spatial-spectral feature learning technology in LM applications, which is of great significance for large-scale applications. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License.
Traffic classification is a crucial technique in network management that aims to identify and manage data packets to optimize network efficiency, ensure quality of service, enhance network security, and implement poli...
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Traffic classification is a crucial technique in network management that aims to identify and manage data packets to optimize network efficiency, ensure quality of service, enhance network security, and implement policy management. As graph convolutional networks (GCNs) take into account not only the features of the data itself, but also the relationships among sets of data during classification. Many researchers have proposed their own traffic classification methods based on GCN in recent years. However, most of the current approaches use two-layer GCN primarily due to the over-smoothing problem associated with deeper GCN. In scenarios with small samples, a two-layer GCN may not adequately capture relationships among traffic data, leading to limited classification performance. Additionally, during graph construction, traffic usually needs to be trimmed to a uniform length, and for traffic with insufficient length, zero-padding is typically applied to extension. This zero-padding strategy poses significant challenges in traffic classification with small samples. In this paper, we propose a method based on autoencoder (AE) and deep graph convolutional networks (ADGCN) for traffic classification for few-shot datasets. ADGCN first utilizes an AE to reconstruct the traffic. AE enables shorter traffic to learn abstract feature representations from longer traffic of the same class to replace zeros, mitigating the adverse effects of zero-padding. The reconstructed traffic is then classified using GCNII, a deep GCN model that addresses the challenge of insufficient data samples. ADGCN is an end-to-end traffic classification method applicable to various scenarios. According to experimental results, ADGCN can achieve a classification accuracy improvement of 3.5 to 24% compared to existing state-of-the-art methods. The code is available at https://***/han20011019/ADGCN.
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