The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of con...The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.展开更多
Ransomware is a type of malicious software that blocks access to a computer by encrypting user’s files until a ransom is paid to the attacker.There have been several reported high-profile ransomware attacks including...Ransomware is a type of malicious software that blocks access to a computer by encrypting user’s files until a ransom is paid to the attacker.There have been several reported high-profile ransomware attacks including WannaCry,Petya,and Bad Rabbit resulting in losses of over a billion dollars to various individuals and businesses in the world.The analysis of ransomware is often carried out via sandbox environments;however,the initial setup and configuration of such environments is a challenging task.Also,it is difficult for an ordinary computer user to correctly interpret the complex results presented in the reports generated by such environments and analysis tools.In this research work,we aim to develop a user-friendly model to understand the taxonomy and analysis of ransomware attacks.Also,we aim to present the results of analysis in the form of summarized reports that can easily be understood by an ordinary computer user.Our model is built on top of the well-known Cuckoo sandbox environment for identification of the ransomware as well as generation of the summarized reports.In addition,for evaluating the usability and accessibility of our proposed model,we conduct a comprehensive user survey consisting of participants from various fields,e.g.,professional developers from software houses,people from academia(professors,students).Our evaluation results demonstrate a positive feedback of approximately 92%on the usability of our proposed model.展开更多
基金This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2021R1I1A1A01055652).
文摘The analysis of overcrowded areas is essential for flow monitoring,assembly control,and security.Crowd counting’s primary goal is to calculate the population in a given region,which requires real-time analysis of congested scenes for prompt reactionary actions.The crowd is always unexpected,and the benchmarked available datasets have a lot of variation,which limits the trained models’performance on unseen test data.In this paper,we proposed an end-to-end deep neural network that takes an input image and generates a density map of a crowd scene.The proposed model consists of encoder and decoder networks comprising batch-free normalization layers known as evolving normalization(EvoNorm).This allows our network to be generalized for unseen data because EvoNorm is not using statistics from the training samples.The decoder network uses dilated 2D convolutional layers to provide large receptive fields and fewer parameters,which enables real-time processing and solves the density drift problem due to its large receptive field.Five benchmark datasets are used in this study to assess the proposed model,resulting in the conclusion that it outperforms conventional models.
基金support of Security Testing-Innovative Secured Systems Lab(ISSL)established at University of Engineering&Technology,Peshawar,Pakistan under the Higher Education Commission initiative of National Center for Cyber Security(Grant No.2(1078)/HEC/M&E/2018/707).
文摘Ransomware is a type of malicious software that blocks access to a computer by encrypting user’s files until a ransom is paid to the attacker.There have been several reported high-profile ransomware attacks including WannaCry,Petya,and Bad Rabbit resulting in losses of over a billion dollars to various individuals and businesses in the world.The analysis of ransomware is often carried out via sandbox environments;however,the initial setup and configuration of such environments is a challenging task.Also,it is difficult for an ordinary computer user to correctly interpret the complex results presented in the reports generated by such environments and analysis tools.In this research work,we aim to develop a user-friendly model to understand the taxonomy and analysis of ransomware attacks.Also,we aim to present the results of analysis in the form of summarized reports that can easily be understood by an ordinary computer user.Our model is built on top of the well-known Cuckoo sandbox environment for identification of the ransomware as well as generation of the summarized reports.In addition,for evaluating the usability and accessibility of our proposed model,we conduct a comprehensive user survey consisting of participants from various fields,e.g.,professional developers from software houses,people from academia(professors,students).Our evaluation results demonstrate a positive feedback of approximately 92%on the usability of our proposed model.
