Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity,in the context of the rapid increase in the number of malware variants and the frequ...Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity,in the context of the rapid increase in the number of malware variants and the frequency of attacks targeting Android devices.In this paper,we propose a novel intelligent computational method to enhance the effectiveness of Android malware detection models.The proposed method combines two main techniques:(1)constructing a malware behavior profile and(2)extracting features from the malware behavior profile using graph neural networks.Specifically,to effectively construct an Android malware behavior profile,this paper proposes an information enrichment technique for the function call graph of malware files,based on new graph-structured features and semantic features of the malware’s source code.Additionally,to extract significant features from the constructed behavior profile,the study proposes using the GraphSAGE graph neural network.With this novel intelligent computational method,a variety of significant features of the malware have been effectively represented,synthesized,and extracted.The approach to detecting Android malware proposed in this paper is a new study and has not been explored in previous research.The experimental results on a dataset of 40,819 Android software indicate that the proposed method performs well across all metrics,with particularly impressive accuracy and recall scores of 99.03%and 99.19%,respectively,which outperforms existing state-of-the-art methods.展开更多
Given the glut of information on the web,it is crucially important to have a system,which will parse the information appropriately and recommend users with relevant information,this class of systems is known as Recomm...Given the glut of information on the web,it is crucially important to have a system,which will parse the information appropriately and recommend users with relevant information,this class of systems is known as Recommendation Systems(RS)-it is one of the most extensively used systems on the web today.Recently,Deep Learning(DL)models are being used to generate recommendations,as it has shown state-of-the-art(SoTA)results in the field of Speech Recognition and Computer Vision in the last decade.However,the RS is a much harder problem,as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their interest.These user-item interactions cannot be fully represented in the Euclidean-Space,as it will trivialize the interaction and undermine the implicit interactions patterns.So to preserve the implicit as well as explicit interactions of user and items,we propose a new graph based recommendation framework.The fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the users.In this paper,we propose the first step,a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as AutoEncoders.To generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.展开更多
Graph-structured data are pervasive in the real-world such as social networks,molecular graphs and transaction networks.Graph neural networks(GNNs)have achieved great success in representation learning on graphs,facil...Graph-structured data are pervasive in the real-world such as social networks,molecular graphs and transaction networks.Graph neural networks(GNNs)have achieved great success in representation learning on graphs,facilitating various downstream tasks.However,GNNs have several drawbacks such as lacking interpretability,can easily inherit the bias of data and cannot model casual rela-tions.Recently,counterfactual learning on graphs has shown promising results in alleviating these drawbacks.Various approaches have been proposed for counterfactual fairness,explainability,link prediction and other applications on graphs.To facilitate the develop-ment of this promising direction,in this survey,we categorize and comprehensively review papers on graph counterfactual learning.We divide existing methods into four categories based on problems studied.For each category,we provide background and motivating ex-amples,a general framework summarizing existing works and a detailed review of these works.We point out promising future research directions at the intersection of graph-structured data,counterfactual learning,and real-world applications.To offer a comprehensive view of resources for future studies,we compile a collection of open-source implementations,public datasets,and commonly-used evalu-ation metrics.This survey aims to serve as a“one-stop-shop”for building a unified understanding of graph counterfactual learning cat-egories and current resources.展开更多
文摘Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity,in the context of the rapid increase in the number of malware variants and the frequency of attacks targeting Android devices.In this paper,we propose a novel intelligent computational method to enhance the effectiveness of Android malware detection models.The proposed method combines two main techniques:(1)constructing a malware behavior profile and(2)extracting features from the malware behavior profile using graph neural networks.Specifically,to effectively construct an Android malware behavior profile,this paper proposes an information enrichment technique for the function call graph of malware files,based on new graph-structured features and semantic features of the malware’s source code.Additionally,to extract significant features from the constructed behavior profile,the study proposes using the GraphSAGE graph neural network.With this novel intelligent computational method,a variety of significant features of the malware have been effectively represented,synthesized,and extracted.The approach to detecting Android malware proposed in this paper is a new study and has not been explored in previous research.The experimental results on a dataset of 40,819 Android software indicate that the proposed method performs well across all metrics,with particularly impressive accuracy and recall scores of 99.03%and 99.19%,respectively,which outperforms existing state-of-the-art methods.
基金This work is supported by The National Natural Science Foundation of China[61502082].
文摘Given the glut of information on the web,it is crucially important to have a system,which will parse the information appropriately and recommend users with relevant information,this class of systems is known as Recommendation Systems(RS)-it is one of the most extensively used systems on the web today.Recently,Deep Learning(DL)models are being used to generate recommendations,as it has shown state-of-the-art(SoTA)results in the field of Speech Recognition and Computer Vision in the last decade.However,the RS is a much harder problem,as the central variable in the recommendation system’s environment is the chaotic nature of the human’s purchasing/consuming behaviors and their interest.These user-item interactions cannot be fully represented in the Euclidean-Space,as it will trivialize the interaction and undermine the implicit interactions patterns.So to preserve the implicit as well as explicit interactions of user and items,we propose a new graph based recommendation framework.The fundamental idea behind this framework is not only to generate the recommendations in the unsupervised fashion but to learn the dynamics of the graph and predict the short and long term interest of the users.In this paper,we propose the first step,a heuristic multi-layer high-dimensional graph which preserves the implicit and explicit interactions between users and items using SoTA Deep Learning models such as AutoEncoders.To generate recommendation from this generated graph a new class of neural network architecture-Graph Neural Network-can be used.
基金supported by,or in part by the National Science Foundation(NSF),USA(No.IIS-1909702)Army Research Office(ARO),USA(No.W911NF-21-10198),and Cisco Faculty Research Award.
文摘Graph-structured data are pervasive in the real-world such as social networks,molecular graphs and transaction networks.Graph neural networks(GNNs)have achieved great success in representation learning on graphs,facilitating various downstream tasks.However,GNNs have several drawbacks such as lacking interpretability,can easily inherit the bias of data and cannot model casual rela-tions.Recently,counterfactual learning on graphs has shown promising results in alleviating these drawbacks.Various approaches have been proposed for counterfactual fairness,explainability,link prediction and other applications on graphs.To facilitate the develop-ment of this promising direction,in this survey,we categorize and comprehensively review papers on graph counterfactual learning.We divide existing methods into four categories based on problems studied.For each category,we provide background and motivating ex-amples,a general framework summarizing existing works and a detailed review of these works.We point out promising future research directions at the intersection of graph-structured data,counterfactual learning,and real-world applications.To offer a comprehensive view of resources for future studies,we compile a collection of open-source implementations,public datasets,and commonly-used evalu-ation metrics.This survey aims to serve as a“one-stop-shop”for building a unified understanding of graph counterfactual learning cat-egories and current resources.
