Metal nanoclusters(MNCs)possess distinct chemical properties due to their diverse electronic structures.As a class of promising model catalysts,it is of importance to explore the relationship between their structures ...Metal nanoclusters(MNCs)possess distinct chemical properties due to their diverse electronic structures.As a class of promising model catalysts,it is of importance to explore the relationship between their structures and properties.However,it is still challenging to get highly active and stable MNCs as surface ligands can hinder their activities,while a complete lack of surface ligand protection can lead to instability.To address this concern,here a series of Pd_(6)nanoclusters(NCs)with varying ligand amounts were synthesized by using Pd_(6)(SC_(6)(C_(2))H_(17))_(12)as precursor and single-walled carbon nanotube(s-CNT)as carrier through treating the composite at different temperatures and times.展开更多
With the continuous improvement of product quality and function,the quality control of mold steel is becoming increasingly stricter. Argon protection is essential for ensuring casting quality during ingot casting. The...With the continuous improvement of product quality and function,the quality control of mold steel is becoming increasingly stricter. Argon protection is essential for ensuring casting quality during ingot casting. The development of argon protection in ingot casting and the production process of enclosed argon protection in 40 t line is discussed,w ith particular focus given the factors affecting the flow of oxygen in the argon protection cover are discussed. The influence of some related factors on the oxygen content is analyzed. On the basis of the online measurements of the oxygen content,the optimized operational approaches for improving the effect of argon protection are developed. This can decrease the liquid steel via secondary oxidation,and improve the quality of the ingots.展开更多
Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,t...Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats,potentially leading to operational failures and data breaches.Furthermore,CPS faces significant threats related to unauthorized access,improper management,and tampering of the content it generates.In this paper,we propose an intrusion detection system(IDS)optimized for CPS environments using a hybrid approach by combining a natureinspired feature selection scheme,such as Grey Wolf Optimization(GWO),in connection with the emerging Light Gradient Boosting Machine(LightGBM)classifier,named as GWO-LightGBM.While gradient boosting methods have been explored in prior IDS research,our novelty lies in proposing a hybrid approach targeting CPS-specific operational constraints,such as low-latency response and accurate detection of rare and critical attack types.We evaluate GWO-LightGBM against GWO-XGBoost,GWO-CatBoost,and an artificial neural network(ANN)baseline using the NSL-KDD and CIC-IDS-2017 benchmark datasets.The proposed models are assessed across multiple metrics,including accuracy,precision,recall,and F1-score,with an emphasis on class-wise performance and training efficiency.The proposed GWO-LightGBM model achieves the highest overall accuracy(99.73%)for NSL-KDD and(99.61%)for CIC-IDS-2017,demonstrating superior performance in detecting minority classes such as Remote-to-Local(R2L)and Other attacks—commonly overlooked by other classifiers.Moreover,the proposed model consumes lower training time,highlighting its practical feasibility and scalability for real-time CPS deployment.展开更多
Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is increasing.Hiding secure data in these transmitted data has emerged broad security issue,such as authentication an...Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is increasing.Hiding secure data in these transmitted data has emerged broad security issue,such as authentication and copyright protection.On the other hand,considering the transmission efficiency issue,image transmission usually involves image compression in Internet-based applications.To address both issues,this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding(AMBTC).First,an image is divided into nonoverlapping blocks through AMBTC compression,the blocks are classified four types,namely smooth,semi-smooth,semi-complex,and complex.The secret data are embedded into the smooth blocks by using a simple replacement strategy.The proposed method respectively embeds nine bits(and five bits)of secret data into the bitmap of the semi-smooth blocks(and semicomplex blocks)through the exclusive-or(XOR)operation.The secret data are embedded into the complex blocks by using a hidden function.After the embedding phase,the direct binary search(DBS)method is performed to improve the image qualitywithout damaging the secret data.The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.展开更多
Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanis...Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders.In this paper,based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours,we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers.By adding a marker to each Uniform Resource Locator(URL),we can trace the page that leads to the access of this URL and the user identity who accesses this URL.With this supporting information,we can not only perform more accurate heuristic detection using the path related features,but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings.In addition to effectively detecting crawlers at the earliest stage,PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected.We deploy our approach on an online forum website,and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers,plus two external crawlers(i.e.,Googlebots and Yahoo Slurp).展开更多
Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanis...Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders.In this paper,based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours,we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers.By adding a marker to each Uniform Resource Locator(URL),we can trace the page that leads to the access of this URL and the user identity who accesses this URL.With this supporting information,we can not only perform more accurate heuristic detection using the path related features,but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings.In addition to effectively detecting crawlers at the earliest stage,PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected.We deploy our approach on an online forum website,and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers,plus two external crawlers(i.e.,Googlebots and Yahoo Slurp).展开更多
基金financially supported by the Natural Science Foundation of Guangxi,China(Nos.2019GXNSFGA245003 and 2021GXNSFBA220058)the National Natural Science Foundation of China(Nos.22272036 and 22362008)+2 种基金Guangxi Technology Base and Talent Subject,China(GUIKE AD23026272)Guangxi Normal University Research Grant,China(No.2022TD)Innovation Project of Guangxi Graduate Education(No.XYCSR2023018)。
文摘Metal nanoclusters(MNCs)possess distinct chemical properties due to their diverse electronic structures.As a class of promising model catalysts,it is of importance to explore the relationship between their structures and properties.However,it is still challenging to get highly active and stable MNCs as surface ligands can hinder their activities,while a complete lack of surface ligand protection can lead to instability.To address this concern,here a series of Pd_(6)nanoclusters(NCs)with varying ligand amounts were synthesized by using Pd_(6)(SC_(6)(C_(2))H_(17))_(12)as precursor and single-walled carbon nanotube(s-CNT)as carrier through treating the composite at different temperatures and times.
文摘With the continuous improvement of product quality and function,the quality control of mold steel is becoming increasingly stricter. Argon protection is essential for ensuring casting quality during ingot casting. The development of argon protection in ingot casting and the production process of enclosed argon protection in 40 t line is discussed,w ith particular focus given the factors affecting the flow of oxygen in the argon protection cover are discussed. The influence of some related factors on the oxygen content is analyzed. On the basis of the online measurements of the oxygen content,the optimized operational approaches for improving the effect of argon protection are developed. This can decrease the liquid steel via secondary oxidation,and improve the quality of the ingots.
基金supported by Culture,Sports and Tourism R&D Program through the Korea Creative Content Agency grant funded by the Ministry of Culture,Sports and Tourism in 2024(Project Name:Global Talent Training Program for Copyright Management Technology in Game Contents,Project Number:RS-2024-00396709,Contribution Rate:100%).
文摘Cyber-physical systems(CPS)represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing,healthcare,and autonomous infrastructure.However,their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats,potentially leading to operational failures and data breaches.Furthermore,CPS faces significant threats related to unauthorized access,improper management,and tampering of the content it generates.In this paper,we propose an intrusion detection system(IDS)optimized for CPS environments using a hybrid approach by combining a natureinspired feature selection scheme,such as Grey Wolf Optimization(GWO),in connection with the emerging Light Gradient Boosting Machine(LightGBM)classifier,named as GWO-LightGBM.While gradient boosting methods have been explored in prior IDS research,our novelty lies in proposing a hybrid approach targeting CPS-specific operational constraints,such as low-latency response and accurate detection of rare and critical attack types.We evaluate GWO-LightGBM against GWO-XGBoost,GWO-CatBoost,and an artificial neural network(ANN)baseline using the NSL-KDD and CIC-IDS-2017 benchmark datasets.The proposed models are assessed across multiple metrics,including accuracy,precision,recall,and F1-score,with an emphasis on class-wise performance and training efficiency.The proposed GWO-LightGBM model achieves the highest overall accuracy(99.73%)for NSL-KDD and(99.61%)for CIC-IDS-2017,demonstrating superior performance in detecting minority classes such as Remote-to-Local(R2L)and Other attacks—commonly overlooked by other classifiers.Moreover,the proposed model consumes lower training time,highlighting its practical feasibility and scalability for real-time CPS deployment.
基金This work is funded in part by the Ministry of Science and Technology,Taiwan,under grant MOST 108-2221-E-011-162-MY2.
文摘Nowadays since the Internet is ubiquitous,the frequency of data transfer through the public network is increasing.Hiding secure data in these transmitted data has emerged broad security issue,such as authentication and copyright protection.On the other hand,considering the transmission efficiency issue,image transmission usually involves image compression in Internet-based applications.To address both issues,this paper presents a data hiding scheme for the image compression method called absolute moment block truncation coding(AMBTC).First,an image is divided into nonoverlapping blocks through AMBTC compression,the blocks are classified four types,namely smooth,semi-smooth,semi-complex,and complex.The secret data are embedded into the smooth blocks by using a simple replacement strategy.The proposed method respectively embeds nine bits(and five bits)of secret data into the bitmap of the semi-smooth blocks(and semicomplex blocks)through the exclusive-or(XOR)operation.The secret data are embedded into the complex blocks by using a hidden function.After the embedding phase,the direct binary search(DBS)method is performed to improve the image qualitywithout damaging the secret data.The experimental results demonstrate that the proposed method yields higher quality and hiding capacity than other reference methods.
基金This work is supported by U.S.Office of Naval Research under grants N00014-16-1-3214 and N00014-16-1-3216.
文摘Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders.In this paper,based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours,we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers.By adding a marker to each Uniform Resource Locator(URL),we can trace the page that leads to the access of this URL and the user identity who accesses this URL.With this supporting information,we can not only perform more accurate heuristic detection using the path related features,but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings.In addition to effectively detecting crawlers at the earliest stage,PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected.We deploy our approach on an online forum website,and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers,plus two external crawlers(i.e.,Googlebots and Yahoo Slurp).
基金supported by U.S.Office of Naval Research under grants N00014-16-1-3214 and N00014-16-1-3216.
文摘Web crawlers have been misused for several malicious purposes such as downloading server data without permission from the website administrator.Moreover,armoured crawlers are evolving against new anti-crawler mechanisms in the arm races between crawler developers and crawler defenders.In this paper,based on one observation that normal users and malicious crawlers have different short-term and long-term download behaviours,we develop a new anti-crawler mechanism called PathMarker to detect and constrain persistent distributed crawlers.By adding a marker to each Uniform Resource Locator(URL),we can trace the page that leads to the access of this URL and the user identity who accesses this URL.With this supporting information,we can not only perform more accurate heuristic detection using the path related features,but also develop a Support Vector Machine based machine learning detection model to distinguish malicious crawlers from normal users via inspecting their different patterns of URL visiting paths and URL visiting timings.In addition to effectively detecting crawlers at the earliest stage,PathMarker can dramatically suppress the scraping efficiency of crawlers before they are detected.We deploy our approach on an online forum website,and the evaluation results show that PathMarker can quickly capture all 6 open-source and in-house crawlers,plus two external crawlers(i.e.,Googlebots and Yahoo Slurp).
