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Metamorphic Computer Virus Detection (2016)
DE PB NW RP
ISBN: 9783659936135 bzw. 3659936138, in Deutsch, LAP Lambert Academic Publishing Sep 2016, Taschenbuch, neu, Nachdruck.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Neuware - A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. 276 pp. Englisch.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Neuware - A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. 276 pp. Englisch.
2
Symbolbild
Metamorphic Computer Virus Detection (2016)
DE PB NW
ISBN: 9783659936135 bzw. 3659936138, in Deutsch, LAP Lambert Academic Publishing Sep 2016, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, Rheinberg-Buch [53870650], Bergisch Gladbach, Germany.
Neuware - A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. 276 pp. Englisch.
Von Händler/Antiquariat, Rheinberg-Buch [53870650], Bergisch Gladbach, Germany.
Neuware - A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. 276 pp. Englisch.
3
Metamorphic Computer Virus Detection
~EN NW AB
ISBN: 9783659936135 bzw. 3659936138, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Österreich, Lieferzeit: 5 Tage, zzgl. Versandkosten.
A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details.
A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details.
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Metamorphic Computer Virus Detection - Using Hidden Markov Model
~EN PB NW
ISBN: 9783659936135 bzw. 3659936138, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Metamorphic Computer Virus Detection: A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. Englisch, Taschenbuch.
Metamorphic Computer Virus Detection: A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. Englisch, Taschenbuch.
5
Symbolbild
Metamorphic Computer Virus Detection
DE PB NW
ISBN: 9783659936135 bzw. 3659936138, in Deutsch, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Von Händler/Antiquariat, English-Book-Service Mannheim [1048135], Mannheim, Germany.
Publisher/Verlag: LAP Lambert Academic Publishing | Using Hidden Markov Model | A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. | Format: Paperback | Language/Sprache: english | 276 pp.
Von Händler/Antiquariat, English-Book-Service Mannheim [1048135], Mannheim, Germany.
Publisher/Verlag: LAP Lambert Academic Publishing | Using Hidden Markov Model | A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. | Format: Paperback | Language/Sprache: english | 276 pp.
6
Metamorphic Computer Virus Detection
DE HC NW
ISBN: 9783659936135 bzw. 3659936138, in Deutsch, Lap Lambert Academic Publishing, gebundenes Buch, neu.
Lieferung aus: Deutschland, Versandkostenfrei innerhalb von Deutschland.
A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. Lieferzeit 1-2 Werktage.
A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based A metamorphic computer virus employs different obfuscation techniques to mutate its code, in its new instance in the new infected file. The mutated instance has the similar behavior and function of the original virus, but the binary pattern of the code is entirely different from its parent. Therefore, metamorphic variants of a virus family cannot be simply detected by most of commercial antivirus products, because their solutions depend on a signature database and employ string signature-based detection methods. Hence, the antivirus detection engine can be evaded by obfuscation techniques, easily. This book is a technical report of a PhD research on the metamorphic computer viruses, their characteristics and features, and detection methods. In this research, a machine learning based model (Hidden Markov Model) has been developed to classify and detect this type of malware. In the proposed method, a HMM is designed and trained on the statistical features of opcodes of executable viral files. This model can be employed to recognize the similar patterns and detect other variants of the same family of metamorphic viruses. The evaluation of the proposed method is discussed in details. Lieferzeit 1-2 Werktage.
7
Metamorphic Computer Virus Detection
~EN PB NW
ISBN: 3659936138 bzw. 9783659936135, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
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