In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This pap...In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware variants.This model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors.The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware behavior.From these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under scrutiny.The outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final decision.The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of features.The efficacy of our proposed APIbased hybrid model is evident in its performance metrics.It outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.展开更多
Due to the proliferation of internet-enabled smartphones,many people,particularly young people in Arabic society,have widely adopted social media platforms as a primary means of communication,interaction and friendshi...Due to the proliferation of internet-enabled smartphones,many people,particularly young people in Arabic society,have widely adopted social media platforms as a primary means of communication,interaction and friendship mak-ing.The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world.However,such networks expose young people to cyberbullying and offen-sive content that puts their safety and emotional well-being at serious risk.Although,many solutions have been proposed to automatically detect cyberbully-ing,most of the existing solutions have been designed for English speaking con-sumers.The morphologically rich languages-such as the Arabic language-lead to data sparsity problems.Thus,render solutions developed for another language are ineffective once applied to the Arabic language content.To this end,this study focuses on improving the efficacy of the existing cyberbullying detection models for Arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model.A diverse set of heterogeneous classifiers from the traditional machine and deep learning technique have been trained using Arabic cyberbullying labeled dataset collected fromfive different platforms.The outputs of the selected classifiers are combined using consensus-based decision-making in which the F1-Score of each classifier was used to rank the classifiers.Then,the Sigmoid function,which can reproduce human-like decision making,is used to infer thefinal decision.The outcomes show the efficacy of the proposed model comparing to the other studied classifiers.The overall improvement gained by the proposed model reaches 1.3%comparing with the best trained classifier.Besides its effectiveness for Arabic language content,the proposed model can be generalized to improve cyberbullying detection in other languages.展开更多
Malicious software(malware)is one of the main cyber threats that organizations and Internet users are currently facing.Malware is a software code developed by cybercriminals for damage purposes,such as corrupting the ...Malicious software(malware)is one of the main cyber threats that organizations and Internet users are currently facing.Malware is a software code developed by cybercriminals for damage purposes,such as corrupting the system and data as well as stealing sensitive data.The damage caused by malware is substantially increasing every day.There is a need to detect malware efficiently and automatically and remove threats quickly from the systems.Although there are various approaches to tackle malware problems,their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks.The deep learning-based approach is recently gaining attention as a suitable method that effectively detects malware.In this paper,a novel approach based on deep learning for detecting malware proposed.Furthermore,the proposed approach deploys novel feature selection,feature co-relation,and feature representations to significantly reduce the feature space.The proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware families.It achieved 96.01%accuracy and outperformed the existing techniques of malware detection.展开更多
Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance ...Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance and scalability,as current trends require the distribution of computation across network nodes/points.In this paper,we survey a large number of mapping and scheduling techniques designed for NoC architectures.This time,we concentrated on 3D systems.We take a systematic literature review approach to analyze existing methods across static,dynamic,hybrid,and machine-learning-based approaches,alongside preliminary AI-based dynamic models in recent works.We classify them into several main aspects covering power-aware mapping,fault tolerance,load-balancing,and adaptive for dynamic workloads.Also,we assess the efficacy of each method against performance parameters,such as latency,throughput,response time,and error rate.Key challenges,including energy efficiency,real-time adaptability,and reinforcement learning integration,are highlighted as well.To the best of our knowledge,this is one of the recent reviews that identifies both traditional and AI-based algorithms for mapping over a modern NoC,and opens research challenges.Finally,we provide directions for future work toward improved adaptability and scalability via lightweight learned models and hierarchical mapping frameworks.展开更多
基金supported by the Deanship of Scientific Research at Northern Border University for funding work through Research Group No.(RG-NBU-2022-1724).
文摘In an era marked by escalating cybersecurity threats,our study addresses the challenge of malware variant detection,a significant concern for amultitude of sectors including petroleum and mining organizations.This paper presents an innovative Application Programmable Interface(API)-based hybrid model designed to enhance the detection performance of malware variants.This model integrates eXtreme Gradient Boosting(XGBoost)and an Artificial Neural Network(ANN)classifier,offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors.The model’s design capitalizes on the benefits of both static and dynamic analysis to extract API-based features,providing a holistic and comprehensive view of malware behavior.From these features,we construct two XGBoost predictors,each of which contributes a valuable perspective on the malicious activities under scrutiny.The outputs of these predictors,interpreted as malicious scores,are then fed into an ANN-based classifier,which processes this data to derive a final decision.The strength of the proposed model lies in its capacity to leverage behavioral and signature-based features,and most importantly,in its ability to extract and analyze the hidden relations between these two types of features.The efficacy of our proposed APIbased hybrid model is evident in its performance metrics.It outperformed other models in our tests,achieving an impressive accuracy of 95%and an F-measure of 93%.This significantly improved the detection performance of malware variants,underscoring the value and potential of our approach in the challenging field of cybersecurity.
基金This research was funded by Deanship of Scientific Research at Northern Border University,Grant No.SAT-2018-3-9F-7871.
文摘Due to the proliferation of internet-enabled smartphones,many people,particularly young people in Arabic society,have widely adopted social media platforms as a primary means of communication,interaction and friendship mak-ing.The technological advances in smartphones and communication have enabled young people to keep in touch and form huge social networks from all over the world.However,such networks expose young people to cyberbullying and offen-sive content that puts their safety and emotional well-being at serious risk.Although,many solutions have been proposed to automatically detect cyberbully-ing,most of the existing solutions have been designed for English speaking con-sumers.The morphologically rich languages-such as the Arabic language-lead to data sparsity problems.Thus,render solutions developed for another language are ineffective once applied to the Arabic language content.To this end,this study focuses on improving the efficacy of the existing cyberbullying detection models for Arabic content by designing and developing a Consensus-based Ensemble Cyberbullying Detection Model.A diverse set of heterogeneous classifiers from the traditional machine and deep learning technique have been trained using Arabic cyberbullying labeled dataset collected fromfive different platforms.The outputs of the selected classifiers are combined using consensus-based decision-making in which the F1-Score of each classifier was used to rank the classifiers.Then,the Sigmoid function,which can reproduce human-like decision making,is used to infer thefinal decision.The outcomes show the efficacy of the proposed model comparing to the other studied classifiers.The overall improvement gained by the proposed model reaches 1.3%comparing with the best trained classifier.Besides its effectiveness for Arabic language content,the proposed model can be generalized to improve cyberbullying detection in other languages.
基金This research was funded by the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia through Project Number 1385.
文摘Malicious software(malware)is one of the main cyber threats that organizations and Internet users are currently facing.Malware is a software code developed by cybercriminals for damage purposes,such as corrupting the system and data as well as stealing sensitive data.The damage caused by malware is substantially increasing every day.There is a need to detect malware efficiently and automatically and remove threats quickly from the systems.Although there are various approaches to tackle malware problems,their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks.The deep learning-based approach is recently gaining attention as a suitable method that effectively detects malware.In this paper,a novel approach based on deep learning for detecting malware proposed.Furthermore,the proposed approach deploys novel feature selection,feature co-relation,and feature representations to significantly reduce the feature space.The proposed approach has been evaluated using a Microsoft prediction dataset with samples of 21,736 malware composed of 9 malware families.It achieved 96.01%accuracy and outperformed the existing techniques of malware detection.
文摘Network-on-Chip(NoC)systems are progressively deployed in connecting massively parallel megacore systems in the new computing architecture.As a result,application mapping has become an important aspect of performance and scalability,as current trends require the distribution of computation across network nodes/points.In this paper,we survey a large number of mapping and scheduling techniques designed for NoC architectures.This time,we concentrated on 3D systems.We take a systematic literature review approach to analyze existing methods across static,dynamic,hybrid,and machine-learning-based approaches,alongside preliminary AI-based dynamic models in recent works.We classify them into several main aspects covering power-aware mapping,fault tolerance,load-balancing,and adaptive for dynamic workloads.Also,we assess the efficacy of each method against performance parameters,such as latency,throughput,response time,and error rate.Key challenges,including energy efficiency,real-time adaptability,and reinforcement learning integration,are highlighted as well.To the best of our knowledge,this is one of the recent reviews that identifies both traditional and AI-based algorithms for mapping over a modern NoC,and opens research challenges.Finally,we provide directions for future work toward improved adaptability and scalability via lightweight learned models and hierarchical mapping frameworks.
