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Papers by Claudio de Mello

Data compression and ciphering are essential features when digital data is stored or trasmitted o... more Data compression and ciphering are essential features when digital data is stored or trasmitted over insecure channels. Prefix codes are widely used to obtain high performance data compression algorithms. Given any prefix code for the symbols of a plaintext, we propose to add security using a multiple substitution function and a key. We show that breaking the code when we are given the ciphertext, dictionary, frequencies and code lengths is a NP-Complete problem.

Data Compression Conference (DCC'06), 2006

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, 2013

ABSTRACT The malicious code analysis allows malware behavior characteristics to be identified, in... more ABSTRACT The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how malware works, to create ways to identify new malicious softwares with similar behavior, and ways of defense. Manual scanning for signature generation becomes impractical, since it requires a lot of time compared to new malwares' dissemination and creation speed. Therefore, this paper proposes the use of sandbox techniques and machine learning techniques to automate software identification in this context. This paper, besides presenting a different and faster approach to malware detection, has achieved an accuracy rate of over 90% for the task of malware identifying.

SPIRE-String Processing …, 2001

... Ruy Luiz Milidiú * , Claudio Gomes de Mello * , José Rodrigues Fernandes ** * Computer Depart... more ... Ruy Luiz Milidiú * , Claudio Gomes de Mello * , José Rodrigues Fernandes ** * Computer Department, PUC-Rio, Brazil {milidiu, cgmello}@inf.puc-rio.br ** Computer Department, UCP, Brazil jose.rodrigues@ucp.br Abstract ... Günther [2] and Massey et al. ...

—The malicious code analysis allows malware behavior characteristics to be identified, in other w... more —The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how malware works, to create ways to identify new malicious softwares with similar behavior, and ways of defense. Manual scanning for signature generation becomes impractical, since it requires a lot of time compared to new malwares' dissemination and creation speed. Therefore, this paper proposes the use of sandbox techniques and machine learning techniques to automate software identification in this context. This paper, besides presenting a different and faster approach to malware detection, has achieved an accuracy rate of over 90% for the task of malware identifying. keywords— malware; sandbox; machine learning.

Data compression and ciphering are essential features when digital data is stored or trasmitted o... more Data compression and ciphering are essential features when digital data is stored or trasmitted over insecure channels. Prefix codes are widely used to obtain high performance data compression algorithms. Given any prefix code for the symbols of a plaintext, we propose to add security using a multiple substitution function and a key. We show that breaking the code when we are given the ciphertext, dictionary, frequencies and code lengths is a NP-Complete problem.

Data Compression Conference (DCC'06), 2006

2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence, 2013

ABSTRACT The malicious code analysis allows malware behavior characteristics to be identified, in... more ABSTRACT The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how malware works, to create ways to identify new malicious softwares with similar behavior, and ways of defense. Manual scanning for signature generation becomes impractical, since it requires a lot of time compared to new malwares' dissemination and creation speed. Therefore, this paper proposes the use of sandbox techniques and machine learning techniques to automate software identification in this context. This paper, besides presenting a different and faster approach to malware detection, has achieved an accuracy rate of over 90% for the task of malware identifying.

SPIRE-String Processing …, 2001

... Ruy Luiz Milidiú * , Claudio Gomes de Mello * , José Rodrigues Fernandes ** * Computer Depart... more ... Ruy Luiz Milidiú * , Claudio Gomes de Mello * , José Rodrigues Fernandes ** * Computer Department, PUC-Rio, Brazil {milidiu, cgmello}@inf.puc-rio.br ** Computer Department, UCP, Brazil jose.rodrigues@ucp.br Abstract ... Günther [2] and Massey et al. ...

—The malicious code analysis allows malware behavior characteristics to be identified, in other w... more —The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how malware works, to create ways to identify new malicious softwares with similar behavior, and ways of defense. Manual scanning for signature generation becomes impractical, since it requires a lot of time compared to new malwares' dissemination and creation speed. Therefore, this paper proposes the use of sandbox techniques and machine learning techniques to automate software identification in this context. This paper, besides presenting a different and faster approach to malware detection, has achieved an accuracy rate of over 90% for the task of malware identifying. keywords— malware; sandbox; machine learning.