M. G. Schultz, E. Eskin, E. Zadok, and S. J. Stolfo, "Data mining methods for detection of new malicious executables," in Proceedings of the 2001. Home/Search Document Details and Download
Summary Related Articles Check |
This paper is cited in the following contexts:
MEF: Malicious Email Filter - Unix Mail Filter Self-citation (Schultz Eskin Zadok Stolfo) (Correct)
....that was trained over a given set of training data. The goal of this paper is to describe a data mining based filter which integrates with Procmail s pre existent security filter [3] to detect malicious executables. The MEF system is an application of more theoretical research into this problem [10]. The data mining based de tection system within MEF is a preliminary system that will become more accurate and efficient as our research progresses, and new data sets are analyzed. It uses a scoring system based on a data mining classifier to determine whether or not an attachment may be ....
....one classifier trained on all hex strings starting with an A , and another on all hex strings starting with a 0 . This was done 16 times and then a voting algorithm was then used to combine their outputs. A more thorough description along with an example, can be found in a companion paper [10]. 4.4 Signature Based Approach To compare our results with traditional methods we implemented a signature based method. First, we calculated the byte sequences that were only found in the malicious executable class. These byte sequences were then concatenated together to make a unique signature ....
Matthew G. Schultz, Eleazar Eskin, Erez Zadok, and Salvatore J. Stolfo. Data Mining Methods for Detection of New Malicious Executables. To appear in IEEE Symposium on Security and Privacy, May 2001.
Eleazar Eskin - Research Statement Part Self-citation (Eskin) (Correct)
No context found.
Matthew G. Schultz, Eleazar Eskin, Erez Zadok, and Salvatore J. Stolfo. "Data Mining Methods for Detection of New Malicious Executables." Long Version of Paper that Appeared in Proceedings of
Accepted to European Research Journal of Computer Virology.. - Md Enamul Karim (2005) (Correct)
No context found.
M. G. Schultz, E. Eskin, E. Zadok, and S. J. Stolfo, "Data mining methods for detection of new malicious executables," in Proceedings of the 2001.
A Packet Filter Placement Problem with - Application To Defense (Correct)
No context found.
M. Schultz, E. Eskin, E. Zadok, S. Stolfo, Data mining methods for detection of new malicious executables, in: Proc. IEEE Symposium on Security and Privacy, 2001, pp. 178--184.
An Approach for Detecting Self-Propagating Email Using Anomaly .. - Gupta, Sekar (2003) (6 citations) (Correct)
No context found.
M.G. Schultz, E.Eskin, E. Zadok, Data Mining Methods for Detection of New Malicious Executables, IEEE Symposium on Security and Privacy, May 2001.
An Approach for Detecting Self-Propagating Email Using Anomaly .. - Gupta, Sekar (2003) (6 citations) (Correct)
No context found.
M.G. Schultz, E.Eskin, E. Zadok, Data Mining Methods for Detection of New Malicious Executables, IEEE Symposium on Security and Privacy, May 2001.
Feedback Email Worm Defense System For Enterprise Networks - Zou, Gong, Towsley (2004) (Correct)
No context found.
M. G. Schultz, E. Zadok, and S. J. Stolfo. Data mining methods for detection of new malicious executables. In Proceedings of IEEE Symposium on Security and Privacy, May 2001.
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC