In recent years, multiple techniques have been proposed to defend computing systems against control-oriented attacks that hijack the control-flow of the victim program. data-only attacks, on the other hand, are a less...
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ISBN:
(纸本)9781450393386
In recent years, multiple techniques have been proposed to defend computing systems against control-oriented attacks that hijack the control-flow of the victim program. data-only attacks, on the other hand, are a less common and more subtle type of exploit which are more difficult to detect using traditional mitigation techniques that target control-oriented attacks. In this paper we introduce a novel methodology for the detection of data-only attacks through modeling the execution behavior of an application using low-level hardware information collected as a data series during execution. One unique aspect of the proposed methodology is that it uses a compilation flag based approach to collect hardware counts, eliminating the need for manual code instrumentation. Another unique aspect is the introduction of a data compression algorithm as the classifier. Using several representative real-world data-only exploits, our experiments show that data-only attacks can be detected with high accuracy using the proposed methodology. We also performed analysis on how to select the most relevant hardware events for the detection of the studied data-only attack, as well as a quantitative study of hardware events' sensitivity to interference.
As the penetration rate of new energy increases, the interactions between new energy power stations and grid are becoming stronger. GB 38755-2019 "Code on security and stability for power system" clarifies n...
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With the reform of China's urbanization increasing in popularity, the security issues posed by urban groundwater, especially groundwater in industrial areas, have attracted scholars' attention. This research a...
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With the reform of China's urbanization increasing in popularity, the security issues posed by urban groundwater, especially groundwater in industrial areas, have attracted scholars' attention. This research aimed to predict and quantify the migration process of contaminants in a microconfined aquifer by conducting a groundwater contamination investigation in an abandoned chemical plant in the Jiangsu Province of China. First, data such as regional hydrogeological parameters and types of contaminants were obtained via hydrogeological drilling, groundwater well monitoring, pumping tests, and laboratory permeability tests, which helped identify the most serious pollution factor: chloroform. Then, a groundwater flow model was built using the Groundwater modeling System (GMS) and verified using the general-purpose parameter estimation (PEST) package. In addition, based on the three-dimensional multi-species model for transport (MT3DMS) in GMS, a transport model was established. The results illustrate that the plume range of chloroform diffuses with water flow, but, because of its slow diffusion rate and inability to degrade naturally, the concentration of the contaminant has remained several times higher than the safety standard for a long time. The contaminant spread vertically to the soil layer above the microconfined aquifer under pressure, resulting in direct pollution. In addition, the contaminant in the microconfined aquifer is anticipated to migrate down to the clay layer and become enriched. However, the first confined aquifer has not been seriously polluted in the past 20 years. Finally, a sensitivity analysis of the parameters shows that groundwater contamination in the Yangtze delta region is greatly affected by precipitation recharge and hydraulic conductivity.
This paper presents a comprehensive study on the impacts of false data injection attacks on microgrid operation, and introduces a hybrid approach to ensure stability and reliability for diverse scenarios. Two realisti...
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ISBN:
(数字)9798350318555
ISBN:
(纸本)9798350318562
This paper presents a comprehensive study on the impacts of false data injection attacks on microgrid operation, and introduces a hybrid approach to ensure stability and reliability for diverse scenarios. Two realistic attack models are developed, targeting the load profiles and renewable generation data respectively. Simulation results reveal potential load balance discrepancies during islanded microgrid operation under these attacks. To mitigate these challenges, the proposed hybrid approach integrates optimization-based energy management with adaptive control schemes, ensuring stable microgrid operation in various conditions.
Target positioning has always been a hot research topic in the field of sensors. In recent years, radar detection technology has become increasingly mature, resulting in a large number of radar sensors have begun to b...
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Deep learning methods can extract reliable feature representations from massive processdata to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and eve...
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ISBN:
(数字)9798350361674
ISBN:
(纸本)9798350361681
Deep learning methods can extract reliable feature representations from massive processdata to build accurate soft sensor models. However, the data in actual industrial production is often nonlinear, dynamic, and even limited in label samples due to untimely sampling and analysis. Aiming at the above problems, this paper proposes a semi-supervised soft sensor modeling method (SSACF-LSTM) for industrial dynamic information mining based on two-path weight comparison. An unsupervised long short-term memory network (LSTM) encoder-decoder is utilized to extract the hidden dynamic nonlinear information in unlabeled samples. This information is then spliced and masked with the initial input, which is subsequently fed into a deep neural network encoder-decoder for pretraining to learn a self-supervised task that mines correlation information between sequences as well as between samples that is useful for the model. In addition, the LSTM with attentional convolutional fusion serves as an auxiliary path that mines correlation information between variables directly from the initial input to complement the information extracted by self-supervision. Finally, the results of both are linearly weighted to obtain the final prediction. The method is validated in a debutanizer process. The results show that SSACF-LSTM can utilize both labeled and unlabeled data to extract quality-related features for soft sensor modeling, which is better than the semi-supervised LSTM network with historical feature fusion (HFF-ssLSTM), semi-supervised stacked auto-encoder (SSAE), and variational auto-encoder regression (SVAER).
Urban investment bonds (Chengtou bonds) refers to bonds issued for local economic and social development, are also a major component of implicit local government debt, which is one of the main financing channels for l...
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Source-Mask Co-Optimization (SMO) techniques have significantly supported semiconductor manufacturing quality by enhancing imaging contrast and lithographic processcontrol in advanced lithography nodes over the past ...
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ISBN:
(数字)9798331513351
ISBN:
(纸本)9798331513368
Source-Mask Co-Optimization (SMO) techniques have significantly supported semiconductor manufacturing quality by enhancing imaging contrast and lithographic processcontrol in advanced lithography nodes over the past decade. Through jointly optimizing the design of the illumination source and modifying the reticle patterns on the mask, the SMO techniques have provided a viable method to find the best lithography process for a given design rule. In SMO, process parameters such as the Exposure Latitude (EL), the Depth of Focus (DoF), the Mask Error Factor (MEF) can be improved through the definition of a cost function. In this presentation, we provide an example with a minimum pitch of 40 nm, which is commonly used for the 2~3 nm Back-End-Of-the-Line (BEOL) logic technology nodes. In this example, we will discuss the challenges and potential of our SMO technique and will offer recommendations for EUV SMO compensations for aberration. Our analysis indicates that SMO can continue to improve optimal pattern transfer capabilities by simultaneously optimizing both illumination source and mask design.
The target feature analysis is essential for sea based launch vehicle tracking and control. The analysis method is performed with recognizing gain changes of telemetry antenna pattern in task usually and computing the...
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Sintering process is a critical step in the ironmaking process. Burn-through point (BTP), as a key performance index of sintering ore, has a great influence on the quality of the sintering product. The existing predic...
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Sintering process is a critical step in the ironmaking process. Burn-through point (BTP), as a key performance index of sintering ore, has a great influence on the quality of the sintering product. The existing prediction methods attempt to use a single model to establish the relationship between variables. However, due to the strong volatility, uncertainty, and multivariable coupling of sintering process, the traditional prediction model cannot produce reliable predictions. In order to deal with the complex characteristics of sintering process, this paper proposes a decomposition-based encoder-decoder modeling framework, in which a sequence decomposition module is designed to decompose the input time series into different sub-sequences. Then, these sub-sequences are constructed by the encoder-decoder models separately. The effectiveness of the proposed multi-step ahead prediction modeling framework was evaluated in a real-world sintering process. Compared with the traditional prediction modeling framework, the proposed modeling framework has more accurate results in multi-step ahead prediction.
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