The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the ...The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.展开更多
由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉...由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉的识别;其次,将训练好的模型进行封装,上传至云服务器;最后,在云服务器上进行识别,通过超文本传输协议(Hyper Text Transfer Protocol,HTTP)与微信小程序进行通信,实现了拍照上传即可识别花卉种类和了解花卉特性的小程序设计。展开更多
为突破传统人工阅片诊断的局限性,提高对宫颈癌变的筛查效率与准确率,提出一种利用改进后的视觉几何群网络(visual geometry group network,VGG-16)实现女性宫颈病变分级预测的方法,并对原始图像中女性宫颈部位进行感兴趣区域提取及病...为突破传统人工阅片诊断的局限性,提高对宫颈癌变的筛查效率与准确率,提出一种利用改进后的视觉几何群网络(visual geometry group network,VGG-16)实现女性宫颈病变分级预测的方法,并对原始图像中女性宫颈部位进行感兴趣区域提取及病变位置的定位与分割。在宫颈病变二分类的研究中,通过多次对比试验后,最终测得宫颈病变分级预测的准确率高达92.95%,与未经改进的方法相比,在时间复杂度与空间复杂度上都有明显的降低。试验表明:文中方法不仅能辅助放射科医生进行病变等级诊断,还可提高诊断的效率与准确率,在临床实践中具有重要意义。展开更多
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Funding Program,Grant No.(FRP-1443-15).
文摘The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches,such as signature-based detection,are no longer effective due to the continuously advancing level of sophistication.To resolve this problem,efficient and flexible malware detection tools are needed.This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations.Moreover,the dataset used in this study is the CIC-AndMal2017,which contains 20,000 instances of network traffic across five distinct malware categories:a.Trojan,b.Adware,c.Ransomware,d.Spyware,e.Worm.These network traffic features are then converted to image formats for deep learning,which is applied in a CNN framework,including the VGG16 pre-trained model.In addition,our approach yielded high performance,yielding an accuracy of 0.92,accuracy of 99.1%,precision of 98.2%,recall of 99.5%,and F1 score of 98.7%.Subsequent improvements to the classification model through changes within the VGG19 framework improved the classification rate to 99.25%.Through the results obtained,it is clear that CNNs are a very effective way to classify Android malware,providing greater accuracy than conventional techniques.The success of this approach also shows the applicability of deep learning in mobile security along with the direction for the future advancement of the real-time detection system and other deeper learning techniques to counter the increasing number of threats emerging in the future.
文摘由于花卉种类繁多,花卉的识别需要人们掌握深厚的植物学知识和长期观察的经验总结,而利用深度学习可实现花卉种类的智能识别。首先,通过迁移学习在视觉几何群网络(Visual Geometry Group Network,VGG-16)算法的基础上进行改进,实现花卉的识别;其次,将训练好的模型进行封装,上传至云服务器;最后,在云服务器上进行识别,通过超文本传输协议(Hyper Text Transfer Protocol,HTTP)与微信小程序进行通信,实现了拍照上传即可识别花卉种类和了解花卉特性的小程序设计。
文摘为突破传统人工阅片诊断的局限性,提高对宫颈癌变的筛查效率与准确率,提出一种利用改进后的视觉几何群网络(visual geometry group network,VGG-16)实现女性宫颈病变分级预测的方法,并对原始图像中女性宫颈部位进行感兴趣区域提取及病变位置的定位与分割。在宫颈病变二分类的研究中,通过多次对比试验后,最终测得宫颈病变分级预测的准确率高达92.95%,与未经改进的方法相比,在时间复杂度与空间复杂度上都有明显的降低。试验表明:文中方法不仅能辅助放射科医生进行病变等级诊断,还可提高诊断的效率与准确率,在临床实践中具有重要意义。
