Digital transformation of the world goes very fast during last two decades. Today, data is power and very important. Firstly, magnetic tapes and then digital data storages have been used to collect all data. After thi...
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ISBN:
(纸本)9781538641842
Digital transformation of the world goes very fast during last two decades. Today, data is power and very important. Firstly, magnetic tapes and then digital data storages have been used to collect all data. After this process, big data and its tool machine learning became very popular in both literature and industry. People use machine learning in order to obtain meaningful information from the big data. It brings valuable planning results. However, nowadays it is quite hard to collect and store all digital data to computers. This process is expensive and we will not have enough space to store data in the future. Therefore, we need and propose "Digital Data Forgetting" phrase with machine learning approach. With this digital / software solution, we will have more valuable data and will be able to erase the rest of them. We called this operation "Big Cleaning". In this article, we use a data set to get and extract more valuable data with principal component analysis (PCA), deep autoencoder and k-nearest neighbor machine learning methods in the experimental analysis section.
We investigate the optimisation capabilities of an algorithm inspired by the Evolutionary Transitions in Individuality. In these transitions, the natural evolutionary process is repeatedly rescaled through successive ...
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In today's dynamic business landscape, the integration of supply chain management and financial risk forecasting is imperative for sustained success. This research paper introduces a groundbreaking approach that s...
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In today's dynamic business landscape, the integration of supply chain management and financial risk forecasting is imperative for sustained success. This research paper introduces a groundbreaking approach that seamlessly merges deep autoencoder (DAE) models with reinforcement learning (RL) techniques to enhance financial risk forecasting within the realm of supply chain management. The primary objective of this research is to optimize financial decision-making processes by extracting key feature representations from financial data and leveraging RL for decision optimization. To achieve this, the paper presents the PSO-SDAE model, a novel and sophisticated approach to financial risk forecasting. By incorporating advanced noise reduction features and optimization algorithms, the PSO-SDAE model significantly enhances the accuracy and reliability of financial risk predictions. Notably, the PSO-SDAE model goes beyond traditional forecasting methods by addressing the need for real-time decision-making in the rapidly evolving landscape of financial risk management. This is achieved through the utilization of a distributed RL algorithm, which expedites the processing of supply chain data while maintaining both efficiency and accuracy. The results of our study showcase the exceptional precision of the PSO-SDAE model in predicting financial risks, underscoring its efficacy for proactive risk management within supply chain operations. Moreover, the augmented processing speed of the model enables real-time analysis and decision-making - a critical capability in today's fast-paced business environment.
Feature selection is an essential task in machine learning and data mining that involves identifying a subset of relevant features from a larger set. This paper proposes a novel technique for unsupervised feature sele...
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ISBN:
(纸本)9783031539688;9783031539695
Feature selection is an essential task in machine learning and data mining that involves identifying a subset of relevant features from a larger set. This paper proposes a novel technique for unsupervised feature selection based on a Neural Network in conjunction with an evolutionary algorithm. The proposed method aims to extract subsets of the most discriminative and relevant features from high-dimensional data, which can be eventually used for efficient and accurate machine learning. An evolutionary algorithm is employed to generate the feature subsets, and the goodness of a feature subset is evaluated through the ability of a neural network to reconstruct the whole original input space by mean squared error minimization (in an auto-encoder fashion). Experimental results demonstrate the effectiveness of the proposed approach in finding relevant feature subsets for successive learning tasks, achieving better classification and regression accuracy compared to state-of-the-art feature selection methods.
The insider threat is a significant security concern for both organizations and government sectors. Traditional machine learning-based insider threat detection approaches usually rely on domain focused feature enginee...
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ISBN:
(纸本)9783030369385;9783030369378
The insider threat is a significant security concern for both organizations and government sectors. Traditional machine learning-based insider threat detection approaches usually rely on domain focused feature engineering, which is expensive and impractical. In this paper, we propose an autoencoder-based approach aiming to automatically learn the discriminative features of the insider behaviours, thus alleviating security experts from tedious inspection tasks. Specifically, a Word2vec model is trained with a corpus transformed from various security logs to generate event representations. Instead of manually selecting Word2vec model parameters, we develop an autoencoder-based "parameter tuner" for the model to produce an optimal feature set. Then, the detection is undertaken by examining the reconstruction error of an autoencoder for each transformed event using the Carnegie Mellon University (CMU) CERT Programs insider threat database. Experimental results demonstrate that our proposed approach could achieve an extremely low false-positive rate (FPR) with all malicious events identified.
作者：
Ma RuiZhou ZhipingJiangnan Univ
Sch Internet Things Engn Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ
Engn Res Ctr Internet Things Technol Applicat Min Wuxi 214122 Jiangsu Peoples R China
Multi-view subspace clustering aims to find the inherent structure of data as much as possible by fusing complementary information of multiple views to achieve better clustering results. However, most of the tradition...
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ISBN:
(纸本)9789881563804
Multi-view subspace clustering aims to find the inherent structure of data as much as possible by fusing complementary information of multiple views to achieve better clustering results. However, most of the traditional multi-view subspace clustering algorithms are only shallow clustering algorithms, which does not capture the deep information of the data well, and does not conduct in- depth research at the self- representation level of the data. To this end, this paper proposes a novel deep multi-view subspace clustering model that introduces exclusive constraints. A deep autoencoder is used to perform nonlinear low-dimensional subspace mapping for each view to learn the deep structure of the original data. To better retain multiple views' local structure and better reflect the complementarity, the exclusive constraints are introduced into the self-representation matrix which located in the middle layer of the deep autoencoder. The multi-view consensus self-representation matrix is used to capture the consistency information between the multi-view data. The update of autoencoder parameters and clustering parameters are iteratively optimized under the same learning framework to improve the clustering performance. Experiments on multi-view data sets prove that this method can better dig out the inherent complementary structure of multi-view data, which reflects the superiority of this method.
Recommendation systems have been used widely in many industries, including online retail, movies, and news media. Indeed, video game recommendation systems are one of the most important tools available to users and ga...
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ISBN:
(纸本)9781450371056
Recommendation systems have been used widely in many industries, including online retail, movies, and news media. Indeed, video game recommendation systems are one of the most important tools available to users and game distribution platforms today. A good recommender can help customers find games they might like faster, not only making it easier for them but also helping game distributors and developers to increase their sales and improve customer satisfaction ratings. One such platform that can greatly benefit from a recommendation system is Steam, the largest digital PC game distribution platform. Steam sees over a dozen million users login every day. It collects a considerable amount of data on each user, and this data may be used to help make better game recommendations. This paper proposes STEAMer, a solution for a new video game recommendation system for the Steam platform. STEAMer utilizes the Steam user data in conjunction with a deep autoencoder learning model to generate potential recommendations;we also apply the additional user data to an existing deep neural network-based recommendation system. Performance evaluation shows that the additional user data does indeed improve recommendation performance. Furthermore, when both systems use the additional user data, the deep autoencoder-based STEAMer still proves superior to the baseline deep neural network-based system in both mean average precision @ 10 (MAP@10) and normalized discounted cumulative gain @ 10 (NDCG@10) scores and in diversity.
Network covert channels are becoming exploited by a wide-range of threats to avoid detection. Such offensive schemes are expected to be also used against IoT deployments, for instance to exfiltrate data or to covertly...
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ISBN:
(纸本)9783031165641;9783031165634
Network covert channels are becoming exploited by a wide-range of threats to avoid detection. Such offensive schemes are expected to be also used against IoT deployments, for instance to exfiltrate data or to covertly orchestrate botnets composed of simple devices. Therefore, we illustrate a solution based on deep Learning for the detection of covert channels targeting the TTL field of IPv4 datagrams. To this aim, we take advantage of an autoencoder ensemble to reveal anomalous traffic behaviors. An experimentation on realistic traffic traces demonstrates the effectiveness of our approach.
Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edg...
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ISBN:
(纸本)9781450355520
Massive and dynamic networks arise in many practical applications such as social media, security and public health. Given an evolutionary network, it is crucial to detect structural anomalies, such as vertices and edges whose "behaviors" deviate from underlying majority of the network, in a real-time fashion. Recently, network embedding has proven a powerful tool in learning the low-dimensional representations of vertices in networks that can capture and preserve the network structure. However, most existing network embedding approaches are designed for static networks, and thus may not be perfectly suited for a dynamic environment in which the network representation has to be constantly updated. In this paper, we propose a novel approach, NETWALK, for anomaly detection in dynamic networks by learning network representations which can be updated dynamically as the network evolves. We first encode the vertices of the dynamic network to vector representations by clique embedding, which jointly minimizes the pairwise distance of vertex representations of each walk derived from the dynamic networks, and the deep autoencoder reconstruction error serving as a global regularization. The vector representations can be computed with constant space requirements using reservoir sampling. On the basis of the learned low-dimensional vertex representations, a clustering-based technique is employed to incrementally and dynamically detect network anomalies. Compared with existing approaches, NETWALK has several advantages: 1) the network embedding can be updated dynamically, 2) streaming network nodes and edges can be encoded efficiently with constant memory space usage, 3). flexible to be applied on different types of networks, and 4) network anomalies can be detected in real-time. Extensive experiments on four real datasets demonstrate the effectiveness of NETWALK.
Aiming at the problem of limited fault-type samples, an ensemble of deep autoencoders (DAE) based fault detection approach for gas turbine engines was proposed. The proposed structure first transferred the measurement...
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ISBN:
(纸本)9781665473880
Aiming at the problem of limited fault-type samples, an ensemble of deep autoencoders (DAE) based fault detection approach for gas turbine engines was proposed. The proposed structure first transferred the measurement features into residual features through the physical model of gas turbine engines. Then a set of one-class classifiers based on DAEs and one-class support vector machines (OCSVM) were constructed as the base classifiers of the ensemble. The final prediction result was determined by majority voting. This structure only uses the flight data of the engine under health conditions and limited degraded conditions. The fault detection system was simulated using a dynamic model of a twin-shaft turbofan engine. The results show that the proposed approach can obtain more accurate and reliable fault detection results than the compared fault detection algorithms.
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