Spyware Detection: Using Data mining for Windows Portable Executable Files

 
9783659488832: Spyware Detection: Using Data mining for Windows Portable Executable Files

Malware represents a significant problem that threatens the security of computer systems. Spyware is one of the recent types of malware that represents a serious threat to confidentiality. The traditional approaches using signature-based to detect spyware programs fails in detecting new and unknown spyware. Many of the malware detection techniques which work well in detecting malware are not investigated in terms of spyware detection. In this research, we investigate the spyware detection by using data mining techniques based on mining Application Programming Interface (API) calls. 2084 spyware and 1065 benign windows Portable Executable (PE) file samples were collected in order to create binary data set. API call statically extracted from binary file, then generate a set of features and features selection was performed, these features are then used to train a classifier. We evaluated a variety of classification algorithms. The accuracy and the area under ROC curve are used for the evaluation of classifier performance. The results show that we achieved an overall accuracy of 98.09% with an area under the ROC curve of 0.995.

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About the Author:

Fadel O. Shaban has received his MSc in information Technology from the Islamic University of Gaza - Palestine. Currently working as a general manager of Computer and Information Technology Unit at ministry of national economy, Palestine. Research Interest: data mining, software development, databases and computer and network security.

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Book Description Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Using Data mining for Windows Portable Executable Files | Malware represents a significant problem that threatens the security of computer systems. Spyware is one of the recent types of malware that represents a serious threat to confidentiality. The traditional approaches using signature-based to detect spyware programs fails in detecting new and unknown spyware. Many of the malware detection techniques which work well in detecting malware are not investigated in terms of spyware detection. In this research, we investigate the spyware detection by using data mining techniques based on mining Application Programming Interface (API) calls. 2084 spyware and 1065 benign windows Portable Executable (PE) file samples were collected in order to create binary data set. API call statically extracted from binary file, then generate a set of features and features selection was performed, these features are then used to train a classifier. We evaluated a variety of classification algorithms. The accuracy and the area under ROC curve are used for the evaluation of classifier performance. The results show that we achieved an overall accuracy of 98.09% with an area under the ROC curve of 0.995. | Format: Paperback | Language/Sprache: english | 92 pp. Bookseller Inventory # K9783659488832

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Book Description LAP Lambert Academic Publishing Nov 2013, 2013. Taschenbuch. Book Condition: Neu. Neuware - Malware represents a significant problem that threatens the security of computer systems. Spyware is one of the recent types of malware that represents a serious threat to confidentiality. The traditional approaches using signature-based to detect spyware programs fails in detecting new and unknown spyware. Many of the malware detection techniques which work well in detecting malware are not investigated in terms of spyware detection. In this research, we investigate the spyware detection by using data mining techniques based on mining Application Programming Interface (API) calls. 2084 spyware and 1065 benign windows Portable Executable (PE) file samples were collected in order to create binary data set. API call statically extracted from binary file, then generate a set of features and features selection was performed, these features are then used to train a classifier. We evaluated a variety of classification algorithms. The accuracy and the area under ROC curve are used for the evaluation of classifier performance. The results show that we achieved an overall accuracy of 98.09% with an area under the ROC curve of 0.995. 92 pp. Englisch. Bookseller Inventory # 9783659488832

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Book Description LAP Lambert Academic Publishing Nov 2013, 2013. Taschenbuch. Book Condition: Neu. Neuware - Malware represents a significant problem that threatens the security of computer systems. Spyware is one of the recent types of malware that represents a serious threat to confidentiality. The traditional approaches using signature-based to detect spyware programs fails in detecting new and unknown spyware. Many of the malware detection techniques which work well in detecting malware are not investigated in terms of spyware detection. In this research, we investigate the spyware detection by using data mining techniques based on mining Application Programming Interface (API) calls. 2084 spyware and 1065 benign windows Portable Executable (PE) file samples were collected in order to create binary data set. API call statically extracted from binary file, then generate a set of features and features selection was performed, these features are then used to train a classifier. We evaluated a variety of classification algorithms. The accuracy and the area under ROC curve are used for the evaluation of classifier performance. The results show that we achieved an overall accuracy of 98.09% with an area under the ROC curve of 0.995. 92 pp. Englisch. Bookseller Inventory # 9783659488832

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Book Description LAP Lambert Academic Publishing, 2013. Paperback. Book Condition: New. Language: English . Brand New Book. Malware represents a significant problem that threatens the security of computer systems. Spyware is one of the recent types of malware that represents a serious threat to confidentiality. The traditional approaches using signature-based to detect spyware programs fails in detecting new and unknown spyware. Many of the malware detection techniques which work well in detecting malware are not investigated in terms of spyware detection. In this research, we investigate the spyware detection by using data mining techniques based on mining Application Programming Interface (API) calls. 2084 spyware and 1065 benign windows Portable Executable (PE) file samples were collected in order to create binary data set. API call statically extracted from binary file, then generate a set of features and features selection was performed, these features are then used to train a classifier. We evaluated a variety of classification algorithms. The accuracy and the area under ROC curve are used for the evaluation of classifier performance. The results show that we achieved an overall accuracy of 98.09 with an area under the ROC curve of 0.995. Bookseller Inventory # KNV9783659488832

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Book Description LAP Lambert Academic Publishing Nov 2013, 2013. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - Malware represents a significant problem that threatens the security of computer systems. Spyware is one of the recent types of malware that represents a serious threat to confidentiality. The traditional approaches using signature-based to detect spyware programs fails in detecting new and unknown spyware. Many of the malware detection techniques which work well in detecting malware are not investigated in terms of spyware detection. In this research, we investigate the spyware detection by using data mining techniques based on mining Application Programming Interface (API) calls. 2084 spyware and 1065 benign windows Portable Executable (PE) file samples were collected in order to create binary data set. API call statically extracted from binary file, then generate a set of features and features selection was performed, these features are then used to train a classifier. We evaluated a variety of classification algorithms. The accuracy and the area under ROC curve are used for the evaluation of classifier performance. The results show that we achieved an overall accuracy of 98.09% with an area under the ROC curve of 0.995. 92 pp. Englisch. Bookseller Inventory # 9783659488832

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Book Description LAP Lambert Academic Publishing. Paperback. Book Condition: New. Paperback. 92 pages. Dimensions: 8.7in. x 5.9in. x 0.2in.Malware represents a significant problem that threatens the security of computer systems. Spyware is one of the recent types of malware that represents a serious threat to confidentiality. The traditional approaches using signature-based to detect spyware programs fails in detecting new and unknown spyware. Many of the malware detection techniques which work well in detecting malware are not investigated in terms of spyware detection. In this research, we investigate the spyware detection by using data mining techniques based on mining Application Programming Interface (API) calls. 2084 spyware and 1065 benign windows Portable Executable (PE) file samples were collected in order to create binary data set. API call statically extracted from binary file, then generate a set of features and features selection was performed, these features are then used to train a classifier. We evaluated a variety of classification algorithms. The accuracy and the area under ROC curve are used for the evaluation of classifier performance. The results show that we achieved an overall accuracy of 98. 09 with an area under the ROC curve of 0. 995. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9783659488832

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