Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications i...Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.展开更多
As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be ...As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background.To alleviate these issues,this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure,in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box,and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model,and reducing the running time by adding a total weights constraint.This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87%average Dice-Sørensen coefficient(DSC)accuracy,which is 0.94%higher than 86.93%,result of the state-of-the-art pancreas segmentation methods.Moreover,this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.展开更多
声学黑洞(Acoustic Black Holes,ABH)以结构厚度的幂律变化实现弹性波的汇聚,结合阻尼层能较好地抑制结构振动。为进一步实现结构的低频振动控制,将声学黑洞与约束阻尼复合,建立附加约束阻尼的二维声学黑洞薄板模型,采用数值方法计算加...声学黑洞(Acoustic Black Holes,ABH)以结构厚度的幂律变化实现弹性波的汇聚,结合阻尼层能较好地抑制结构振动。为进一步实现结构的低频振动控制,将声学黑洞与约束阻尼复合,建立附加约束阻尼的二维声学黑洞薄板模型,采用数值方法计算加速度响应与结构损耗因子,研究二维声学黑洞板附加约束阻尼后的减振特性,并通过二维声学黑洞薄板振动试验开展验证,最后探究约束层材料、厚度及约束阻尼半径对声学黑洞板低频减振性能的影响规律。结果表明:相比于附加自由阻尼,约束阻尼使声学黑洞薄板在第一阶共振峰处的加速度导纳降低12.61 dB;当约束层厚度为截断厚度的2倍左右时,薄板整体可以达到较佳的减振效果。研究可为声学黑洞薄板结构的低频减振应用提供重要参考。展开更多
基金This research was partly supported by the National Science and Technology Council,Taiwan with Grant Numbers 112-2221-E-992-045,112-2221-E-992-057-MY3 and 112-2622-8-992-009-TD1.
文摘Since its inception,the Internet has been rapidly evolving.With the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the rise.Many applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the internet.Concurrently,malicious threats on the internet are on the rise as well.Distributed Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet today.The scale and complexity of DDoS attacks are constantly growing.Intrusion Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those threats.In addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant attention.However,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown attacks.These attacks,which are not encountered during the system’s training,can lead to misclassification with significant errors.In this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location Constraint Prototype Loss(SLCPL)and Fuzzy C-Means(FCM).With the proposed method,we achieved promising results compared to traditional methods.The proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(CICIDS2017)dataset.Particularly,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(CICDDoS2019)dataset.The success of the proposed method is due to the combination of SLCPL,an advanced Open-Set Recognition(OSR)technique,and FCM,a traditional yet highly applicable clustering technique.This has yielded a novel method in the field of unknown attack detection.This further expands the trend of applying DL and ML techniques in the development of intrusion detection systems and cybersecurity.Finally,implementing the proposed method in real-world systems can enhance the security capabilities against increasingly complex threats on computer networks.
基金supported by the National Natural Science Foundation of China[61772242,61976106,61572239]the China Postdoctoral Science Foundation[2017M611737]+3 种基金the Six Talent Peaks Project in Jiangsu Province[DZXX-122]the Jiangsu Province EmergencyManagement Science and Technology Project[YJGL-TG-2020-8]the Key Research and Development Plan of Zhenjiang City[SH2020011]Postgraduate Innovation Fund of Jiangsu Province[KYCX18_2257].
文摘As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background.To alleviate these issues,this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure,in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box,and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model,and reducing the running time by adding a total weights constraint.This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87%average Dice-Sørensen coefficient(DSC)accuracy,which is 0.94%higher than 86.93%,result of the state-of-the-art pancreas segmentation methods.Moreover,this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.
文摘声学黑洞(Acoustic Black Holes,ABH)以结构厚度的幂律变化实现弹性波的汇聚,结合阻尼层能较好地抑制结构振动。为进一步实现结构的低频振动控制,将声学黑洞与约束阻尼复合,建立附加约束阻尼的二维声学黑洞薄板模型,采用数值方法计算加速度响应与结构损耗因子,研究二维声学黑洞板附加约束阻尼后的减振特性,并通过二维声学黑洞薄板振动试验开展验证,最后探究约束层材料、厚度及约束阻尼半径对声学黑洞板低频减振性能的影响规律。结果表明:相比于附加自由阻尼,约束阻尼使声学黑洞薄板在第一阶共振峰处的加速度导纳降低12.61 dB;当约束层厚度为截断厚度的2倍左右时,薄板整体可以达到较佳的减振效果。研究可为声学黑洞薄板结构的低频减振应用提供重要参考。
