Malicious Portable Document Format(PDF)files represent one of the largest threats in the computer security space.Significant research has been done using handwritten signatures and machine learning based on detection ...Malicious Portable Document Format(PDF)files represent one of the largest threats in the computer security space.Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction.These approaches are time consuming,require substantial prior knowledge,and the list of features must be updated with each newly discovered vulnerability individually.In this study,we propose two models for PDF malware detection.The first model is a convolutional neural network(CNN)integrated into a standard deviation based regularization model to detect malicious PDF documents.The second model is a support vector machine(SVM)based ensemble model with three different kernels.The two models were trained and tested on two different datasets.The experimental results show that the accuracy of both models is approximately 100%,and the robustness against evasive samples is excellent.Further,the robustness of the models was evaluated with malicious PDF documents generated using Mimicus.Both models can distinguish the different vulnerabilities exploited in malicious files and achieve excellent performance in terms of generalization ability,accuracy,and robustness.展开更多
Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern era.Twitter,a widely used microblogging site where individuals share their thoughts in the form of t...Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern era.Twitter,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment analysis.In recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets.Opinions or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing opinions.The process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods.The deep learning-based model long-short-term memory(LSTM)performed better than machine learning approaches.Long short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.展开更多
基金This research work was funded by Makkah Digital Gate Initiative under Grant No.(MDP-IRI-16-2020).Therefore,authors gratefully acknowledge technical and financial support from Emirate Of Makkah Province and King Abdulaziz University,Jeddah,Saudi Arabia.
文摘Malicious Portable Document Format(PDF)files represent one of the largest threats in the computer security space.Significant research has been done using handwritten signatures and machine learning based on detection via manual feature extraction.These approaches are time consuming,require substantial prior knowledge,and the list of features must be updated with each newly discovered vulnerability individually.In this study,we propose two models for PDF malware detection.The first model is a convolutional neural network(CNN)integrated into a standard deviation based regularization model to detect malicious PDF documents.The second model is a support vector machine(SVM)based ensemble model with three different kernels.The two models were trained and tested on two different datasets.The experimental results show that the accuracy of both models is approximately 100%,and the robustness against evasive samples is excellent.Further,the robustness of the models was evaluated with malicious PDF documents generated using Mimicus.Both models can distinguish the different vulnerabilities exploited in malicious files and achieve excellent performance in terms of generalization ability,accuracy,and robustness.
基金The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4400257DSR01).
文摘Web-blogging sites such as Twitter and Facebook are heavily influenced by emotions,sentiments,and data in the modern era.Twitter,a widely used microblogging site where individuals share their thoughts in the form of tweets,has become a major source for sentiment analysis.In recent years,there has been a significant increase in demand for sentiment analysis to identify and classify opinions or expressions in text or tweets.Opinions or expressions of people about a particular topic,situation,person,or product can be identified from sentences and divided into three categories:positive for good,negative for bad,and neutral for mixed or confusing opinions.The process of analyzing changes in sentiment and the combination of these categories is known as“sentiment analysis.”In this study,sentiment analysis was performed on a dataset of 90,000 tweets using both deep learning and machine learning methods.The deep learning-based model long-short-term memory(LSTM)performed better than machine learning approaches.Long short-term memory achieved 87%accuracy,and the support vector machine(SVM)classifier achieved slightly worse results than LSTM at 86%.The study also tested binary classes of positive and negative,where LSTM and SVM both achieved 90%accuracy.
