The Experts below are selected from a list of 1524 Experts worldwide ranked by ideXlab platform
Pablo Garcia Bringas - One of the best experts on this subject based on the ideXlab platform.
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Opcode sequences as representation of executables for data-mining-based Unknown Malware detection
Information Sciences, 2013Co-Authors: Igor Santos, Xabier Ugarte-pedrero, Felix Brezo, Pablo Garcia BringasAbstract:Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of Malware is growing faster every year and poses a serious global security threat. Consequently, Malware detection has become a critical topic in computer security. Currently, signature-based detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect Malware only after the malicious executable has already caused damage and provided the Malware is adequately documented. Therefore, the signature-based method consistently fails to detect new Malware. In this paper, we propose a new method to detect Unknown Malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting Unknown Malware.
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collective classification for Unknown Malware detection
International Conference on Security and Cryptography, 2011Co-Authors: Igor Santos, Carlos Laorden, Pablo Garcia BringasAbstract:Malware is any type of computer software harmful to computers and networks. The amount of Malware is increasing every year and poses as a serious global security threat. Signature-based detection is the most broadly used commercial antivirus method, however, it fails to detect new and previously unseen Malware. Supervised machine-learning models have been proposed in order to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires a significant amount of malicious code and benign software to be identified and labelled in beforehand. In this paper, we propose a new method that adopts a collective learning approach to detect Unknown Malware. Collective classification is a type of semi-supervised learning that presents an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, collective classification algorithms to build different machine-learning classifiers using a set of labelled (as Malware and legitimate software) and unlabelled instances. We perform an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
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SECRYPT - Collective classification for Unknown Malware detection
2011Co-Authors: Igor Santos, Carlos Laorden, Pablo Garcia BringasAbstract:Malware is any type of computer software harmful to computers and networks. The amount of Malware is increasing every year and poses as a serious global security threat. Signature-based detection is the most broadly used commercial antivirus method, however, it fails to detect new and previously unseen Malware. Supervised machine-learning models have been proposed in order to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires a significant amount of malicious code and benign software to be identified and labelled in beforehand. In this paper, we propose a new method that adopts a collective learning approach to detect Unknown Malware. Collective classification is a type of semi-supervised learning that presents an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, collective classification algorithms to build different machine-learning classifiers using a set of labelled (as Malware and legitimate software) and unlabelled instances. We perform an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
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opcode sequence based semi supervised Unknown Malware detection
Computational Intelligence and Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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semi supervised learning for Unknown Malware detection
Distributed Computing and Artificial Intelligence, 2011Co-Authors: Igor Santos, Javier Nieves, Pablo Garcia BringasAbstract:Malware is any kind of computer software potentially harmful to both computers and networks. The amount of Malware is increasing every year and poses a serious global security threat. Signature-based detection is the most widely used commercial antivirus method, however, it consistently fails to detect new Malware. Supervised machine-learning models have been used to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires that a significant amount of malicious code and benign software to be identified and labelled beforehand. In this paper, we propose a new method of Malware protection that adopts a semi-supervised learning approach to detect Unknown Malware. This method is designed to build a machine-learning classifier using a set of labelled (Malware and legitimate software) and unlabelled instances.We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
Igor Santos - One of the best experts on this subject based on the ideXlab platform.
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Opcode sequences as representation of executables for data-mining-based Unknown Malware detection
Information Sciences, 2013Co-Authors: Igor Santos, Xabier Ugarte-pedrero, Felix Brezo, Pablo Garcia BringasAbstract:Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of Malware is growing faster every year and poses a serious global security threat. Consequently, Malware detection has become a critical topic in computer security. Currently, signature-based detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect Malware only after the malicious executable has already caused damage and provided the Malware is adequately documented. Therefore, the signature-based method consistently fails to detect new Malware. In this paper, we propose a new method to detect Unknown Malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting Unknown Malware.
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collective classification for Unknown Malware detection
International Conference on Security and Cryptography, 2011Co-Authors: Igor Santos, Carlos Laorden, Pablo Garcia BringasAbstract:Malware is any type of computer software harmful to computers and networks. The amount of Malware is increasing every year and poses as a serious global security threat. Signature-based detection is the most broadly used commercial antivirus method, however, it fails to detect new and previously unseen Malware. Supervised machine-learning models have been proposed in order to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires a significant amount of malicious code and benign software to be identified and labelled in beforehand. In this paper, we propose a new method that adopts a collective learning approach to detect Unknown Malware. Collective classification is a type of semi-supervised learning that presents an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, collective classification algorithms to build different machine-learning classifiers using a set of labelled (as Malware and legitimate software) and unlabelled instances. We perform an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
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SECRYPT - Collective classification for Unknown Malware detection
2011Co-Authors: Igor Santos, Carlos Laorden, Pablo Garcia BringasAbstract:Malware is any type of computer software harmful to computers and networks. The amount of Malware is increasing every year and poses as a serious global security threat. Signature-based detection is the most broadly used commercial antivirus method, however, it fails to detect new and previously unseen Malware. Supervised machine-learning models have been proposed in order to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires a significant amount of malicious code and benign software to be identified and labelled in beforehand. In this paper, we propose a new method that adopts a collective learning approach to detect Unknown Malware. Collective classification is a type of semi-supervised learning that presents an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, collective classification algorithms to build different machine-learning classifiers using a set of labelled (as Malware and legitimate software) and unlabelled instances. We perform an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
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opcode sequence based semi supervised Unknown Malware detection
Computational Intelligence and Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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semi supervised learning for Unknown Malware detection
Distributed Computing and Artificial Intelligence, 2011Co-Authors: Igor Santos, Javier Nieves, Pablo Garcia BringasAbstract:Malware is any kind of computer software potentially harmful to both computers and networks. The amount of Malware is increasing every year and poses a serious global security threat. Signature-based detection is the most widely used commercial antivirus method, however, it consistently fails to detect new Malware. Supervised machine-learning models have been used to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires that a significant amount of malicious code and benign software to be identified and labelled beforehand. In this paper, we propose a new method of Malware protection that adopts a semi-supervised learning approach to detect Unknown Malware. This method is designed to build a machine-learning classifier using a set of labelled (Malware and legitimate software) and unlabelled instances.We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
Carlos Laorden - One of the best experts on this subject based on the ideXlab platform.
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collective classification for Unknown Malware detection
International Conference on Security and Cryptography, 2011Co-Authors: Igor Santos, Carlos Laorden, Pablo Garcia BringasAbstract:Malware is any type of computer software harmful to computers and networks. The amount of Malware is increasing every year and poses as a serious global security threat. Signature-based detection is the most broadly used commercial antivirus method, however, it fails to detect new and previously unseen Malware. Supervised machine-learning models have been proposed in order to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires a significant amount of malicious code and benign software to be identified and labelled in beforehand. In this paper, we propose a new method that adopts a collective learning approach to detect Unknown Malware. Collective classification is a type of semi-supervised learning that presents an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, collective classification algorithms to build different machine-learning classifiers using a set of labelled (as Malware and legitimate software) and unlabelled instances. We perform an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
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SECRYPT - Collective classification for Unknown Malware detection
2011Co-Authors: Igor Santos, Carlos Laorden, Pablo Garcia BringasAbstract:Malware is any type of computer software harmful to computers and networks. The amount of Malware is increasing every year and poses as a serious global security threat. Signature-based detection is the most broadly used commercial antivirus method, however, it fails to detect new and previously unseen Malware. Supervised machine-learning models have been proposed in order to solve this issue, but the usefulness of supervised learning is far to be perfect because it requires a significant amount of malicious code and benign software to be identified and labelled in beforehand. In this paper, we propose a new method that adopts a collective learning approach to detect Unknown Malware. Collective classification is a type of semi-supervised learning that presents an interesting method for optimising the classification of partially-labelled data. In this way, we propose here, for the first time, collective classification algorithms to build different machine-learning classifiers using a set of labelled (as Malware and legitimate software) and unlabelled instances. We perform an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used, while maintaining high accuracy rates.
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opcode sequence based semi supervised Unknown Malware detection
Computational Intelligence and Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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CISIS - Opcode-sequence-based semi-supervised Unknown Malware detection
Computational Intelligence in Security for Information Systems, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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Using opcode sequences in single-class learning to detect Unknown Malware
IET Information Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any type of malicious code that has the potential to harm a computer or network. The volume of Malware is growing at a faster rate every year and poses a serious global security threat. Although signature-based detection is the most widespread method used in commercial antivirus programs, it consistently fails to detect new Malware. Supervised machine-learning models have been used to address this issue. However, the use of supervised learning is limited because it needs a large amount of malicious code and benign software to be labelled first. In this study, the authors propose a new method that uses single-class learning to detect Unknown Malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either Malware or legitimate software. The authors performed an empirical study that shows that this method can reduce the effort of labelling software while maintaining high accuracy.
Felix Brezo - One of the best experts on this subject based on the ideXlab platform.
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Opcode sequences as representation of executables for data-mining-based Unknown Malware detection
Information Sciences, 2013Co-Authors: Igor Santos, Xabier Ugarte-pedrero, Felix Brezo, Pablo Garcia BringasAbstract:Malware can be defined as any type of malicious code that has the potential to harm a computer or network. The volume of Malware is growing faster every year and poses a serious global security threat. Consequently, Malware detection has become a critical topic in computer security. Currently, signature-based detection is the most widespread method used in commercial antivirus. In spite of the broad use of this method, it can detect Malware only after the malicious executable has already caused damage and provided the Malware is adequately documented. Therefore, the signature-based method consistently fails to detect new Malware. In this paper, we propose a new method to detect Unknown Malware families. This model is based on the frequency of the appearance of opcode sequences. Furthermore, we describe a technique to mine the relevance of each opcode and assess the frequency of each opcode sequence. In addition, we provide empirical validation that this new method is capable of detecting Unknown Malware.
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opcode sequence based semi supervised Unknown Malware detection
Computational Intelligence and Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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CISIS - Opcode-sequence-based semi-supervised Unknown Malware detection
Computational Intelligence in Security for Information Systems, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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Using opcode sequences in single-class learning to detect Unknown Malware
IET Information Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any type of malicious code that has the potential to harm a computer or network. The volume of Malware is growing at a faster rate every year and poses a serious global security threat. Although signature-based detection is the most widespread method used in commercial antivirus programs, it consistently fails to detect new Malware. Supervised machine-learning models have been used to address this issue. However, the use of supervised learning is limited because it needs a large amount of malicious code and benign software to be labelled first. In this study, the authors propose a new method that uses single-class learning to detect Unknown Malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either Malware or legitimate software. The authors performed an empirical study that shows that this method can reduce the effort of labelling software while maintaining high accuracy.
Borja Sanz - One of the best experts on this subject based on the ideXlab platform.
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opcode sequence based semi supervised Unknown Malware detection
Computational Intelligence and Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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CISIS - Opcode-sequence-based semi-supervised Unknown Malware detection
Computational Intelligence in Security for Information Systems, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any computer software potentially harmful to both computers and networks. The amount of Malware is growing every year and poses a serious global security threat. Signature-based detection is the most extended method in commercial antivirus software, however, it consistently fails to detect new Malware. Supervised machine learning has been adopted to solve this issue, but the usefulness of supervised learning is far to be complete because it requires a high amount of malicious executables and benign software to be identified and labelled previously. In this paper, we propose a new method of Malware detection that adopts a well-known semi-supervised learning approach to detect Unknown Malware. This method is based on examining the frequencies of the appearance of opcode sequences to build a semi-supervised machine-learning classifier using a set of labelled (either Malware or legitimate software) and unlabelled instances. We performed an empirical validation demonstrating that the labelling efforts are lower than when supervised learning is used while the system maintains high accuracy rate.
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Using opcode sequences in single-class learning to detect Unknown Malware
IET Information Security, 2011Co-Authors: Igor Santos, Felix Brezo, Carlos Laorden, Borja Sanz, Pablo Garcia BringasAbstract:Malware is any type of malicious code that has the potential to harm a computer or network. The volume of Malware is growing at a faster rate every year and poses a serious global security threat. Although signature-based detection is the most widespread method used in commercial antivirus programs, it consistently fails to detect new Malware. Supervised machine-learning models have been used to address this issue. However, the use of supervised learning is limited because it needs a large amount of malicious code and benign software to be labelled first. In this study, the authors propose a new method that uses single-class learning to detect Unknown Malware families. This method is based on examining the frequencies of the appearance of opcode sequences to build a machine-learning classifier using only one set of labelled instances within a specific class of either Malware or legitimate software. The authors performed an empirical study that shows that this method can reduce the effort of labelling software while maintaining high accuracy.
