RESTful API fuzzing is a promising method for automated vulnerability detection in Kubernetes *** tools struggle with generating lengthy,high-semantic request sequences that can pass Kubernetes API gateway *** address...
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RESTful API fuzzing is a promising method for automated vulnerability detection in Kubernetes *** tools struggle with generating lengthy,high-semantic request sequences that can pass Kubernetes API gateway *** address this,we propose KubeFuzzer,a black-box fuzzing tool designed for Kubernetes RESTful *** utilizes Natural Language Processing(NLP)to extract and integrate semantic information from API specifications and response messages,guiding the generation of more effective request *** evaluation of KubeFuzzer on various Kubernetes clusters shows that it improves code coverage by 7.86%to 36.34%,increases the successful response rate by 6.7%to 83.33%,and detects 16.7%to 133.3%more bugs compared to three leading *** identified over 1000 service crashes,which were narrowed down to 7 unique *** tested these bugs on 10 real-world Kubernetes projects,including major providers like AWS(EKS),Microsoft Azure(AKS),and Alibaba Cloud(ACK),and confirmed that these issues could trigger service *** have reported and confirmed these bugs with the Kubernetes community,and they have been addressed.
Lignin-derived porous carbons have emerged as promising electrode materials for ***,the challenge remains in designing and controlling their structure to achieve ideal electrochemical performance due to the complex mo...
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Lignin-derived porous carbons have emerged as promising electrode materials for ***,the challenge remains in designing and controlling their structure to achieve ideal electrochemical performance due to the complex molecular structure of lignin and its intricate chemical reactions during the activation *** this study,three porous carbons were synthesized from lignin by spray drying and chemical activation with vary-ing KOH *** specific surface area and structural order of the prepared porous carbon continued to increase with the increase of the KOH ***-mass spectrometry(TG-MS)was employed to track the molecular fragments generated during the pyrolysis of KOH-activated lignin,and the mechanism of the thermochemical conversion was *** the thermochemical conversion of lignin,KOH facili-tated the removal of H2 and CO,leading to the formation of not only more micropores and mesopores,but also more ordered carbon *** pore structure exhibited a greater impact than the carbon structure on the electrochemical performance of porous *** optimized porous carbon exhibited a capacitance of 256 F g-1 at a current density of 0.2 A g-1,making it an ideal electrode material for high-performance supercapacitors.
Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread atte...
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Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production,which benefits food *** detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating ***,existing methods are still limited to single crop disease *** importantly,the existing model has a large number of parameters,which is not conducive to deploying it to agricultural mobile ***,reducing the number of model parameters tends to cause a decrease in model *** solve these problems,we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple *** detail,we design 2 strategies to build 4 different lightweight models as student models:the YOLOR-Light-v1,YOLOR-Light-v2,Mobile-YOLOR-v1,and Mobile-YOLOR-v2 models,and adopt the YOLOR model as the teacher *** develop a multistage knowledge distillation method to improve lightweight model performance,achieving 60.4%mAP@.5 in the PlantDoc dataset with small model parameters,outperforming existing ***,the multistage knowledge distillation technique can make the model lighter while maintaining high *** only that,the technique can be extended to other tasks,such as image classification and image segmentation,to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart *** code is available at https://***/QDH/MSKD.
This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-thresho...
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This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized *** proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown *** system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its *** numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.
Frequent road incidents cause significant physical harm and economic losses globally. The key to ensuring road safety lies in accurately perceiving surrounding road incidents. However, the highly dynamic nature o...
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Classic image features were once widely used in image classification but have been almost entirely replaced by neural networks today. While the performance of neural networks, especially convolutional neural networks ...
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Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a ***-b...
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Considering the stealthiness and persistence of Advanced Persistent Threats(APTs),system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a ***-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks,and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection,which requires lots of manual efforts to locate attack *** paper proposes an APT-exploited process detection approach called ThreatSniffer,which constructs the benign provenance graph from attack-free audit logs,fits normal system entity interactions and then detects APT-exploited processes by predicting the rationality of entity ***,ThreatSniffer understands system entities in terms of their file paths,interaction sequences,and the number distribution of interaction types and uses the multi-head self-attention mechanism to fuse these ***,based on the insight that APT-exploited processes interact with system entities they should not invoke,ThreatSniffer performs negative sampling on the benign provenance graph to generate non-existent edges,thus characterizing irrational entity interactions without requiring APT attack *** last,it employs a heterogeneous graph neural network as the interaction prediction model to aggregate the contextual information of entity interactions,and locate processes exploited by attackers,thereby achieving fine-grained APT *** results demonstrate that anomaly-based detection enables ThreatSniffer to identify all attack *** to the node-level APT detection method APT-KGL,ThreatSniffer achieves a 6.1%precision improvement because of its comprehensive understanding of entity semantics.
Dear Editor, This letter is concerned with the data-driven fault compensation tracking control for a coupled wastewater treatment process(WWTP)subject to sensor faults. Invariant set theory is introduced to eliminate ...
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Dear Editor, This letter is concerned with the data-driven fault compensation tracking control for a coupled wastewater treatment process(WWTP)subject to sensor faults. Invariant set theory is introduced to eliminate the completely bounded and differentiable conditions of coupled non-affine dynamics and to explicitly express the control *** adaptive fault compensation mechanism is constructed to accommodate the effects of sensor faults. By employing a cubic absolutevalue Lyapunov criteria。
Blockchain technologies pave a promising way for implementing the inter-organizational processes. Most of the current research works translate the execution logic in the process models into the smart contracts, which ...
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In the process of iron and steel smelting, steel slag is inevitably produced as a byproduct. Accurately identifying steel slag is a prerequisite for controlling the content of steel slag. Conventional vision-based ste...
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