University of Detroit Mercy

Displaying 1-14 of 14 results

  • White Papers // May 2013

    Instance-Based Anomaly Method for Android Malware Detection

    The usage of mobile phones has increased in the authors' lives because they offer nearly the same functionality as a personal computer. Besides, the number of applications available for Android-based mobile devices has increased. Android application distribution is based on a centralized market where the developers can upload and sell...

    Provided By University of Detroit Mercy

  • White Papers // Apr 2013

    MAMA: Manifest Analysis for Malware Detection in Android

    The use of mobile phones has increased in the authors' lives because they offer nearly the same functionality as a personal computer. Besides, the number of applications available for Android-based mobile devices has increased. Google offers programmers the opportunity to upload and sell applications in the Android Market, but malware...

    Provided By University of Detroit Mercy

  • White Papers // Mar 2013

    MADS: Malicious Android Applications Detection through String Analysis

    The use of mobile phones has increased in the authors' lives because they offer nearly the same functionality as a personal computer. Besides, the number of applications available for Android-based mobile devices has increased. Google offers to programmers the opportunity to upload and sell applications in the Android Market, but...

    Provided By University of Detroit Mercy

  • White Papers // Nov 2012

    NOA: An Information Retrieval Based Malware Detection System

    Malware refers to any type of code written with the intention of harming a computer or network. The quantity of malware being produced is increasing every year and poses a serious global security threat. Hence, malware detection is a critical topic in computer security. Signature-based detection is the most widespread...

    Provided By University of Detroit Mercy

  • White Papers // Oct 2012

    PUMA: Permission Usage to Detect Malware in Android

    The presence of mobile devices has increased in the authors' lives offering almost the same functionality as a personal computer. Android devices have appeared lately and, since then, the number of applications available for this operating system has increased exponentially. Google already has its Android Market where applications are offered...

    Provided By University of Detroit Mercy

  • White Papers // Jul 2012

    OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection

    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. In this paper, the...

    Provided By University of Detroit Mercy

  • White Papers // Oct 2011

    Using Opcode Sequences in Single-Class Learning to Detect Unknown Malware

    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,...

    Provided By University of Detroit Mercy

  • White Papers // Aug 2011

    Opcode Sequences as Representation of Executables for Data-Mining-Based Unknown Malware Detection

    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...

    Provided By University of Detroit Mercy

  • White Papers // May 2011

    Collective Classification for Unknown Malware Detection

    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. In this paper, the authors propose a new method that adopts a collective learning approach to detect unknown malware. Collective classification is...

    Provided By University of Detroit Mercy

  • White Papers // Apr 2011

    Semi-Supervised Learning for Unknown Malware Detection

    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. In this paper,...

    Provided By University of Detroit Mercy

  • White Papers // Mar 2011

    Structural Feature Based Anomaly Detection for Packed Executable Identification

    Malware is any software with malicious intentions. Commercial anti-malware software relies on signature databases. This approach has proven to be effective when the threats are already known. However, malware writers employ software encryption tools and code obfuscation techniques to hide the actual behavior of their malicious programs. One of these...

    Provided By University of Detroit Mercy

  • White Papers // Jun 2010

    China's Economic Restructuring Through Induced Capital Inflows

    Mao Zedong was China's "Great Helmsman" between 1949 and 1976. Corollary to his vision for China, all major social, economic and political decisions bore his personal imprimatur. Mao advocated self sufficiency and isolated China's economy from the rest of the world. By the time of his demise in 1976, the...

    Provided By University of Detroit Mercy

  • White Papers // Apr 2010

    Automatic Behaviour-Based Analysis and Classification System for Malware Detection

    Malware is any kind of program explicitly designed to harm, such as viruses, Trojan horses or worms. Since the amount of malware is growing exponentially, it already poses a serious security threat. Therefore, every incoming code must be analyzed in order to classify it as malware or benign software. These...

    Provided By University of Detroit Mercy

  • White Papers // Dec 2009

    Idea: Opcode-sequence-based Malware Detection

    Malware is every malicious code that has the potential to harm any computer or network. The amount of malware is increasing faster every year and poses a serious security threat. Hence, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most extended method within...

    Provided By University of Detroit Mercy

  • White Papers // Apr 2010

    Automatic Behaviour-Based Analysis and Classification System for Malware Detection

    Malware is any kind of program explicitly designed to harm, such as viruses, Trojan horses or worms. Since the amount of malware is growing exponentially, it already poses a serious security threat. Therefore, every incoming code must be analyzed in order to classify it as malware or benign software. These...

    Provided By University of Detroit Mercy

  • White Papers // Jul 2012

    OPEM: A Static-Dynamic Approach for Machine-Learning-Based Malware Detection

    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. In this paper, the...

    Provided By University of Detroit Mercy

  • White Papers // May 2011

    Collective Classification for Unknown Malware Detection

    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. In this paper, the authors propose a new method that adopts a collective learning approach to detect unknown malware. Collective classification is...

    Provided By University of Detroit Mercy

  • White Papers // Apr 2011

    Semi-Supervised Learning for Unknown Malware Detection

    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. In this paper,...

    Provided By University of Detroit Mercy

  • White Papers // Jun 2010

    China's Economic Restructuring Through Induced Capital Inflows

    Mao Zedong was China's "Great Helmsman" between 1949 and 1976. Corollary to his vision for China, all major social, economic and political decisions bore his personal imprimatur. Mao advocated self sufficiency and isolated China's economy from the rest of the world. By the time of his demise in 1976, the...

    Provided By University of Detroit Mercy

  • White Papers // Apr 2013

    MAMA: Manifest Analysis for Malware Detection in Android

    The use of mobile phones has increased in the authors' lives because they offer nearly the same functionality as a personal computer. Besides, the number of applications available for Android-based mobile devices has increased. Google offers programmers the opportunity to upload and sell applications in the Android Market, but malware...

    Provided By University of Detroit Mercy

  • White Papers // Dec 2009

    Idea: Opcode-sequence-based Malware Detection

    Malware is every malicious code that has the potential to harm any computer or network. The amount of malware is increasing faster every year and poses a serious security threat. Hence, malware detection has become a critical topic in computer security. Currently, signature-based detection is the most extended method within...

    Provided By University of Detroit Mercy

  • White Papers // Aug 2011

    Opcode Sequences as Representation of Executables for Data-Mining-Based Unknown Malware Detection

    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...

    Provided By University of Detroit Mercy

  • White Papers // Mar 2011

    Structural Feature Based Anomaly Detection for Packed Executable Identification

    Malware is any software with malicious intentions. Commercial anti-malware software relies on signature databases. This approach has proven to be effective when the threats are already known. However, malware writers employ software encryption tools and code obfuscation techniques to hide the actual behavior of their malicious programs. One of these...

    Provided By University of Detroit Mercy

  • White Papers // Oct 2011

    Using Opcode Sequences in Single-Class Learning to Detect Unknown Malware

    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,...

    Provided By University of Detroit Mercy

  • White Papers // May 2013

    Instance-Based Anomaly Method for Android Malware Detection

    The usage of mobile phones has increased in the authors' lives because they offer nearly the same functionality as a personal computer. Besides, the number of applications available for Android-based mobile devices has increased. Android application distribution is based on a centralized market where the developers can upload and sell...

    Provided By University of Detroit Mercy

  • White Papers // Nov 2012

    NOA: An Information Retrieval Based Malware Detection System

    Malware refers to any type of code written with the intention of harming a computer or network. The quantity of malware being produced is increasing every year and poses a serious global security threat. Hence, malware detection is a critical topic in computer security. Signature-based detection is the most widespread...

    Provided By University of Detroit Mercy

  • White Papers // Oct 2012

    PUMA: Permission Usage to Detect Malware in Android

    The presence of mobile devices has increased in the authors' lives offering almost the same functionality as a personal computer. Android devices have appeared lately and, since then, the number of applications available for this operating system has increased exponentially. Google already has its Android Market where applications are offered...

    Provided By University of Detroit Mercy

  • White Papers // Mar 2013

    MADS: Malicious Android Applications Detection through String Analysis

    The use of mobile phones has increased in the authors' lives because they offer nearly the same functionality as a personal computer. Besides, the number of applications available for Android-based mobile devices has increased. Google offers to programmers the opportunity to upload and sell applications in the Android Market, but...

    Provided By University of Detroit Mercy