近日,武汉大学国家网络安全学院教授陈晶课题组2022级博士生梁瑞超的研究成果被第46届IEEE/ACM International Conference on Software Engineering(ICSE 2024)会议录用。会议将在2024年4月14日至20日在葡萄牙里斯本举行。梁瑞超为第一...近日,武汉大学国家网络安全学院教授陈晶课题组2022级博士生梁瑞超的研究成果被第46届IEEE/ACM International Conference on Software Engineering(ICSE 2024)会议录用。会议将在2024年4月14日至20日在葡萄牙里斯本举行。梁瑞超为第一作者,陈晶为通讯作者,武汉大学为第一单位。论文题为“PonziGuard:Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph(CRBG)”,在陈晶教授、杜瑞颖教授、何琨副研究员、吴聪博士后联合指导下完成。展开更多
Nowadays,industrial control system(ICS)has begun to integrate with the Internet.While the Internet has brought convenience to ICS,it has also brought severe security concerns.Traditional ICS network traffic anomaly de...Nowadays,industrial control system(ICS)has begun to integrate with the Internet.While the Internet has brought convenience to ICS,it has also brought severe security concerns.Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts.They are not aimed at the original network data,nor can they capture the potential characteristics of network packets.Therefore,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network traffic.Instead of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater depth.It regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted feature.Experimental results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.展开更多
文摘目的探究及观察白葡奈氏菌片联合吸入糖皮质激素+长效β2受体激动剂(inhaled corticosteroid+long-acting β_(2)-agonist,ICS+LABA)治疗中重度慢性阻塞性肺疾病(chronic obstructive pulmonary disease,COPD)急性发作的疗效及对生活质量的影响。方法将2020年6月—2021年12月山东第一医科大学附属省立医院的80例中重度COPD急性发作患者根据随机数字表法分为2组。对照组的40例采用ICS+LABA进行治疗,观察组的40例则在对照组的基础上加用白葡奈氏菌片。比较2组的COPD治疗总有效率、不良反应发生率、治疗前后的症状体征积分、疾病状态[慢性阻塞性肺疾病评分(COPD assessment test,CAT评分)]及生活质量[世界卫生组织生存质量测定量表简表(World Health Organization on quality of life brief scale,WHOQOL-BREF评分)]。结果治疗1、2周后观察组的COPD治疗总有效率显著高于对照组,差异有统计学意义(P<0.05),2组的不良反应发生率比较,差异无统计学意义(P>0.05),治疗1、2周后观察组的COPD相关症状体征积分显著低于对照组,CAT评分构成则显著优于对照组,WHOQOL-BREF评分显著高于对照组,差异有统计学意义(P<0.05)。结论白葡奈氏菌片联合ICS+LABA治疗中重度COPD急性发作的疗效较好,且可显著改善患者的生活质量。
文摘近日,武汉大学国家网络安全学院教授陈晶课题组2022级博士生梁瑞超的研究成果被第46届IEEE/ACM International Conference on Software Engineering(ICSE 2024)会议录用。会议将在2024年4月14日至20日在葡萄牙里斯本举行。梁瑞超为第一作者,陈晶为通讯作者,武汉大学为第一单位。论文题为“PonziGuard:Detecting Ponzi Schemes on Ethereum with Contract Runtime Behavior Graph(CRBG)”,在陈晶教授、杜瑞颖教授、何琨副研究员、吴聪博士后联合指导下完成。
基金supported by the National Natural Science Foundation of China(No.62076042,No.62102049)the Key Research and Development Project of Sichuan Province(No.2021YFSY0012,No.2020YFG0307,No.2021YFG0332)+3 种基金the Science and Technology Innovation Project of Sichuan(No.2020017)the Key Research and Development Project of Chengdu(No.2019-YF05-02028-GX)the Innovation Team of Quantum Security Communication of Sichuan Province(No.17TD0009)the Academic and Technical Leaders Training Funding Support Projects of Sichuan Province(No.2016120080102643).
文摘Nowadays,industrial control system(ICS)has begun to integrate with the Internet.While the Internet has brought convenience to ICS,it has also brought severe security concerns.Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts.They are not aimed at the original network data,nor can they capture the potential characteristics of network packets.Therefore,the following improvements were made in this study:(1)A dataset that can be used to evaluate anomaly detection algorithms is produced,which provides raw network data.(2)A request response-based convolutional neural network named RRCNN is proposed,which can be used for anomaly detection of ICS network traffic.Instead of using statistical features manually extracted by security experts,this method uses the byte sequences of the original network packets directly,which can extract potential features of the network packets in greater depth.It regards the request packet and response packet in a session as a Request-Response Pair(RRP).The feature of RRP is extracted using a one-dimensional convolutional neural network,and then the RRP is judged to be normal or abnormal based on the extracted feature.Experimental results demonstrate that this model is better than several other machine learning and neural network models,with F1,accuracy,precision,and recall above 99%.
