By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the...
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By the emergence of the fourth industrial revolution,interconnected devices and sensors generate large-scale,dynamic,and inharmonious data in Industrial Internet of Things(IIoT)*** vast heterogeneous data increase the challenges of security risks and data analysis *** IIoT grows,cyber-attacks become more diverse and complex,making existing anomaly detection models less effective to *** this paper,an ensemble deep learning model that uses the benefits of the Long Short-Term Memory(LSTM)and the AutoEncoder(AE)architecture to identify out-of-norm activities for cyber threat hunting in IIoT is *** this model,the LSTM is applied to create a model on normal time series of data(past and present data)to learn normal data patterns and the important features of data are identified by AE to reduce data *** addition,the imbalanced nature of IIoT datasets has not been considered in most of the previous literature,affecting low accuracy and *** solve this problem,the proposed model extracts new balanced data from the imbalanced datasets,and these new balanced data are fed into the deep LSTM AE anomaly detection *** this paper,the proposed model is evaluated on two real IIoT datasets-Gas Pipeline(GP)and Secure Water Treatment(SWaT)that are imbalanced and consist of long-term and short-term dependency on *** results are compared with conventional machine learning classifiers,Random Forest(RF),Multi-Layer Perceptron(MLP),Decision Tree(DT),and Super Vector Machines(SVM),in which higher performance in terms of accuracy is obtained,99.3%and 99.7%based on GP and SWaT datasets,***,the proposed ensemble model is compared with advanced related models,including Stacked Auto-Encoders(SAE),Naive Bayes(NB),Projective Adaptive Resonance Theory(PART),Convolutional Auto-Encoder(C-AE),and Package Signatures(PS)based LSTM(PS-LSTM)model.
With the increasingly complex blockchain technology environment and emerging security threats, the detection and prevention of vulnerabilities in blockchain smart contracts have become crucial for ensuring the healthy...
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In this paper, we present an enhanced convolutional model for indoor radio map generation, focusing on the integration of a novel ray-marching feature. We describe our machine learning pipeline developed for the ICASS...
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In this paper we formulate the problem of predicting the outcome (winner) of an ongoing National Basketball Association (NBA) match as a supervised machine learning problem. In this approach, as the match progresses, ...
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Orthogonal time frequency space (OTFS) is envisioned as a highly promising modulation technique due to its superior performance in high-mobility scenarios. Meanwhile, non-orthogonal multiple access (NOMA) stands out a...
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The study of carbon-enhanced metal-poor (CEMP) stars is of great significance for understanding the chemical evolution of the early universe and stellar *** stars are characterized by carbon overabundance and are clas...
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The study of carbon-enhanced metal-poor (CEMP) stars is of great significance for understanding the chemical evolution of the early universe and stellar *** stars are characterized by carbon overabundance and are classified into several subclasses based on the abundance patterns of neutron-capture elements,including CEMP-s,CEMP-no,CEMP-r,and CEMP-r/*** subclasses provide important insights into the formation of thefirst stars,early stellar nucleosynthesis,and supernova ***,one of the major challenges in CEMP star research is the relatively small sample size of identified stars,which limits statistical analyses and hinders a comprehensive understanding of their ***,a series of large-scale spectroscopic survey projects have been launched and developed in recent years,providing unprecedented opportunities and technical challenges for the search and study of CEMP *** this end,this paper draws on the progress and future prospects of existing methods in constructing large CEMP data sets and offers an in-depth discussion from a technical standpoint,focusing on the strengths and *** addition,we review recent advancements in the identification of CEMP stars,emphasizing the growing role of machine learning in processing and analyzing the increasingly large data sets generated by modern astronomical *** to traditional spectral analysis methods,machine learning offers greater efficiency in handling complex data,automatic extraction of stellar parameters,and improved prediction *** these advancements,the research faces persistent challenges,including the scarcity of labeled samples,limitations imposed by low-resolution spectra,and the lack of interpretability in machine learning *** address these issues,the paper proposes potential solutions and future research directions aimed at advancing the study of CEMP stars and enhancing our understanding of their role in the chemical evolutio
In this paper, we study the performance of few-shot learning, specifically meta learning empowered few-shot relation networks, over supervised deep learning and conventional machine learning approaches in the problem ...
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The existing method of using large pre-trained models with prompts for zero-shot text classification possesses powerful representation ability and scalability. However, its commercial availability is relatively limite...
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In this paper, we propose a digital twin (DT) assisted-user device (UD)-base station (BS) association for cellular-supported internet of things (IoT) networks, and scheduling of uplink transmission of these devices to...
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With the rise of the Internet of Things(IoT),the word“intelligent medical care”has increasingly become a major *** medicine adopts the most advanced IoT technology to realize the interaction between patients and peo...
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With the rise of the Internet of Things(IoT),the word“intelligent medical care”has increasingly become a major *** medicine adopts the most advanced IoT technology to realize the interaction between patients and people,medical institutions,andmedical ***,with the openness of network transmission,the security and privacy of information transmission have become a major ***,Masud et *** a lightweight anonymous user authentication protocol for IoT medical treatment,claiming that their method can resist various ***,through analysis of the protocol,we observed that their protocol cannot effectively resist privileged internal attacks,sensor node capture attacks,and stolen authentication attacks,and their protocol does not have perfect forward ***,we propose a new protocol to resolve the security vulnerabilities in Masud’s protocol and remove some redundant parameters,so as tomake the protocolmore compact and *** addition,we evaluate the security and performance of the new protocol and prove that the overall performance of the new protocol is better than that of other related protocols.
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