Dhiego Ramos Pinto - Academia.edu (original) (raw)

Uploads

Papers by Dhiego Ramos Pinto

Research paper thumbnail of A Deep Learning Approach to the Malware Classification Problem using Autoencoders

Detecting malicious code or categorizing it among families has become an increasingly difficult t... more Detecting malicious code or categorizing it among families has become an increasingly difficult task. Malware1 exploits vulnerabilities and employ sophisticated techniques to avoid their detection and further classification, challenging cybersecurity teams, governments, enterprises, and the ordinary user, causing uncountable losses annually. Traditional machine learning algorithms have been used to attack the problem, although, these methods are heavily relying on domain expertise to be successful. Deep Learning methods requires less dependency on feature engineering, discovering the important features straightly from the raw data, recognizing patterns that humans usually can't. This work presents a deep learning approach for malware multi-class classification based on an unsupervised pre-trained classifier, using opcodes and its operands frequencies as raw data, ignoring knowledge that could be acquired from any known features from the malware families. The results confirmed that the approach is well succeeded and our best model achieved a MacroF1 of 93.14% a competitive result comparing to best-known classifier, since it uses less information about the malware.

Research paper thumbnail of Static Analysis on Disassembled Files: A Deep Learning Approach to Malware Classification

The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and ... more The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and the massive use of social networks considerably raised the number of vectors of malware propagation. Deep Learning models achieved great results in many different areas, including security-related tasks, such as static and dynamic malware analysis. This paper details a deep learning approach to the problem of malware classification using only the disassembled artifact's code as input. We show competitive performance when comparing to other solutions that use a higher degree of knowledge.

Research paper thumbnail of Static Analysis on Disassembled Files: A Deep Learning Approach to Malware Classification

Anais do XVII Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg 2017)

The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and ... more The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and the massive use of social networks considerably raised the number of vectors of malware propagation. Deep Learning models achieved great results in many different areas, including security-related tasks, such as static and dynamic malware analysis. This paper details a deep learning approach to the problem of malware classification using only the disassembled artifact's code as input. We show competitive performance when comparing to other solutions that use a higher degree of knowledge.

Research paper thumbnail of A Deep Learning Approach to the Malware Classification Problem using Autoencoders

Proceedings of the XV Brazilian Symposium on Information Systems, 2019

Detecting malicious code or categorizing it among families has become an increasingly difficult t... more Detecting malicious code or categorizing it among families has become an increasingly difficult task. Malware1 exploits vulnerabilities and employ sophisticated techniques to avoid their detection and further classification, challenging cybersecurity teams, governments, enterprises, and the ordinary user, causing uncountable losses annually. Traditional machine learning algorithms have been used to attack the problem, although, these methods are heavily relying on domain expertise to be successful. Deep Learning methods requires less dependency on feature engineering, discovering the important features straightly from the raw data, recognizing patterns that humans usually can't. This work presents a deep learning approach for malware multi-class classification based on an unsupervised pre-trained classifier, using opcodes and its operands frequencies as raw data, ignoring knowledge that could be acquired from any known features from the malware families. The results confirmed that the approach is well succeeded and our best model achieved a MacroF1 of 93.14% a competitive result comparing to best-known classifier, since it uses less information about the malware.

Research paper thumbnail of A Deep Learning Approach to the Malware Classification Problem using Autoencoders

Detecting malicious code or categorizing it among families has become an increasingly difficult t... more Detecting malicious code or categorizing it among families has become an increasingly difficult task. Malware1 exploits vulnerabilities and employ sophisticated techniques to avoid their detection and further classification, challenging cybersecurity teams, governments, enterprises, and the ordinary user, causing uncountable losses annually. Traditional machine learning algorithms have been used to attack the problem, although, these methods are heavily relying on domain expertise to be successful. Deep Learning methods requires less dependency on feature engineering, discovering the important features straightly from the raw data, recognizing patterns that humans usually can't. This work presents a deep learning approach for malware multi-class classification based on an unsupervised pre-trained classifier, using opcodes and its operands frequencies as raw data, ignoring knowledge that could be acquired from any known features from the malware families. The results confirmed that the approach is well succeeded and our best model achieved a MacroF1 of 93.14% a competitive result comparing to best-known classifier, since it uses less information about the malware.

Research paper thumbnail of Static Analysis on Disassembled Files: A Deep Learning Approach to Malware Classification

The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and ... more The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and the massive use of social networks considerably raised the number of vectors of malware propagation. Deep Learning models achieved great results in many different areas, including security-related tasks, such as static and dynamic malware analysis. This paper details a deep learning approach to the problem of malware classification using only the disassembled artifact's code as input. We show competitive performance when comparing to other solutions that use a higher degree of knowledge.

Research paper thumbnail of Static Analysis on Disassembled Files: A Deep Learning Approach to Malware Classification

Anais do XVII Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais (SBSeg 2017)

The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and ... more The cybernetic environment is hostile. An infinitude of gadgets with access to fast networks and the massive use of social networks considerably raised the number of vectors of malware propagation. Deep Learning models achieved great results in many different areas, including security-related tasks, such as static and dynamic malware analysis. This paper details a deep learning approach to the problem of malware classification using only the disassembled artifact's code as input. We show competitive performance when comparing to other solutions that use a higher degree of knowledge.

Research paper thumbnail of A Deep Learning Approach to the Malware Classification Problem using Autoencoders

Proceedings of the XV Brazilian Symposium on Information Systems, 2019

Detecting malicious code or categorizing it among families has become an increasingly difficult t... more Detecting malicious code or categorizing it among families has become an increasingly difficult task. Malware1 exploits vulnerabilities and employ sophisticated techniques to avoid their detection and further classification, challenging cybersecurity teams, governments, enterprises, and the ordinary user, causing uncountable losses annually. Traditional machine learning algorithms have been used to attack the problem, although, these methods are heavily relying on domain expertise to be successful. Deep Learning methods requires less dependency on feature engineering, discovering the important features straightly from the raw data, recognizing patterns that humans usually can't. This work presents a deep learning approach for malware multi-class classification based on an unsupervised pre-trained classifier, using opcodes and its operands frequencies as raw data, ignoring knowledge that could be acquired from any known features from the malware families. The results confirmed that the approach is well succeeded and our best model achieved a MacroF1 of 93.14% a competitive result comparing to best-known classifier, since it uses less information about the malware.