The Experts below are selected from a list of 4095 Experts worldwide ranked by ideXlab platform

Sam Malek - One of the best experts on this subject based on the ideXlab platform.

  • lightweight obfuscation resilient detection and family identification of android malware
    International Conference on Software Engineering, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a Security Analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

  • journal first lightweight obfuscation resilient detection and family identification of android malware
    International Conference on Software Engineering, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a Security Analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

  • lightweight obfuscation resilient detection and family identification of android malware
    ACM Transactions on Software Engineering and Methodology, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps is increasing rapidly. Android malware can damage or alter other files or settings, install additional applications, and so on. To determine such behaviors, a Security Analyst can significantly benefit from identifying the family to which an Android malware belongs rather than only detecting if an app is malicious. Techniques for detecting Android malware, and determining their families, lack the ability to handle certain obfuscations that aim to thwart detection. Moreover, some prior techniques face scalability issues, preventing them from detecting malware in a timely manner. To address these challenges, we present a novel machine-learning-based Android malware detection and family identification approach, RevealDroid, that operates without the need to perform complex program analyses or to extract large sets of features. Specifically, our selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid’s superiority against state-of-the-art approaches.

Toby Stock - One of the best experts on this subject based on the ideXlab platform.

  • Security Analyst and stock market efficiency in anticipating tax motivated income shifting
    The Accounting Review, 2006
    Co-Authors: Philip B Shane, Toby Stock
    Abstract:

    In the context of the statutory tax rate reductions enacted in the Tax Reform Act of 1986, this paper investigates the degree to which capital market participants anticipate and correctly interpret temporary income effects of tax‐motivated income shifting. We find evidence consistent with financial Analysts' earnings forecasts failing to anticipate earnings management that shifts income from fourth quarters in higher tax rate years to immediately following first quarters of lower tax rate years. The evidence suggests that this failure is not the result of a decision to ignore the income shifting, but rather an inability to recognize temporary components of reported earnings. We also find evidence that market prices do not fully reflect the temporary income effects of tax‐motivated income shifting, and that Analyst inefficiency explains about half of the market inefficiency. We interpret these inefficiencies as potentially important costs of tax planning that could limit the ability of public firm managers...

  • Security Analyst and stock market efficiency in anticipating tax motivated income shifting
    Social Science Research Network, 2005
    Co-Authors: Philip B Shane, Toby Stock
    Abstract:

    In the context of the statutory tax rate reductions enacted in the Tax Reform Act of 1986, this paper investigates the degree to which capital market participants anticipate and correctly interpret temporary income effects of tax-motivated income shifting. We find evidence consistent with financial Analysts' earnings forecasts failing to anticipate earnings management that shifts income from fourth quarters in higher tax rate years to immediately following first quarters of lower tax rate years. The evidence suggests that this failure is not the result of a decision to ignore the income shifting, but rather an inability to recognize temporary components of reported earnings. We also find evidence that market prices do not fully reflect the temporary income effects of tax-motivated income shifting, and that Analyst inefficiency explains about half of the market inefficiency. We interpret these inefficiencies as potentially important costs of tax planning that could limit the ability of public firm managers to implement otherwise optimal tax strategies.

Joshua Garcia - One of the best experts on this subject based on the ideXlab platform.

  • lightweight obfuscation resilient detection and family identification of android malware
    International Conference on Software Engineering, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a Security Analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

  • journal first lightweight obfuscation resilient detection and family identification of android malware
    International Conference on Software Engineering, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a Security Analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

  • lightweight obfuscation resilient detection and family identification of android malware
    ACM Transactions on Software Engineering and Methodology, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps is increasing rapidly. Android malware can damage or alter other files or settings, install additional applications, and so on. To determine such behaviors, a Security Analyst can significantly benefit from identifying the family to which an Android malware belongs rather than only detecting if an app is malicious. Techniques for detecting Android malware, and determining their families, lack the ability to handle certain obfuscations that aim to thwart detection. Moreover, some prior techniques face scalability issues, preventing them from detecting malware in a timely manner. To address these challenges, we present a novel machine-learning-based Android malware detection and family identification approach, RevealDroid, that operates without the need to perform complex program analyses or to extract large sets of features. Specifically, our selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid’s superiority against state-of-the-art approaches.

Philip B Shane - One of the best experts on this subject based on the ideXlab platform.

  • Security Analyst and stock market efficiency in anticipating tax motivated income shifting
    The Accounting Review, 2006
    Co-Authors: Philip B Shane, Toby Stock
    Abstract:

    In the context of the statutory tax rate reductions enacted in the Tax Reform Act of 1986, this paper investigates the degree to which capital market participants anticipate and correctly interpret temporary income effects of tax‐motivated income shifting. We find evidence consistent with financial Analysts' earnings forecasts failing to anticipate earnings management that shifts income from fourth quarters in higher tax rate years to immediately following first quarters of lower tax rate years. The evidence suggests that this failure is not the result of a decision to ignore the income shifting, but rather an inability to recognize temporary components of reported earnings. We also find evidence that market prices do not fully reflect the temporary income effects of tax‐motivated income shifting, and that Analyst inefficiency explains about half of the market inefficiency. We interpret these inefficiencies as potentially important costs of tax planning that could limit the ability of public firm managers...

  • Security Analyst and stock market efficiency in anticipating tax motivated income shifting
    Social Science Research Network, 2005
    Co-Authors: Philip B Shane, Toby Stock
    Abstract:

    In the context of the statutory tax rate reductions enacted in the Tax Reform Act of 1986, this paper investigates the degree to which capital market participants anticipate and correctly interpret temporary income effects of tax-motivated income shifting. We find evidence consistent with financial Analysts' earnings forecasts failing to anticipate earnings management that shifts income from fourth quarters in higher tax rate years to immediately following first quarters of lower tax rate years. The evidence suggests that this failure is not the result of a decision to ignore the income shifting, but rather an inability to recognize temporary components of reported earnings. We also find evidence that market prices do not fully reflect the temporary income effects of tax-motivated income shifting, and that Analyst inefficiency explains about half of the market inefficiency. We interpret these inefficiencies as potentially important costs of tax planning that could limit the ability of public firm managers to implement otherwise optimal tax strategies.

Mahmoud Hammad - One of the best experts on this subject based on the ideXlab platform.

  • lightweight obfuscation resilient detection and family identification of android malware
    International Conference on Software Engineering, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a Security Analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

  • journal first lightweight obfuscation resilient detection and family identification of android malware
    International Conference on Software Engineering, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps has been and continues to increase rapidly. These malware can damage or alter other files or settings, install additional applications, obfuscate their behaviors, propagate quickly, and so on. To identify and handle such malware, a Security Analyst can significantly benefit from identifying the family to which a malicious app belongs rather than only detecting if an app is malicious. To address these challenges, we present a novel machine learning-based Android malware detection and family-identification approach, RevealDroid, that operates without the need to perform complex program analyses or extract large sets of features. RevealDroid's selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid's superiority against state-of-the-art approaches. [URL of original paper: https://dl.acm.org/citation.cfm?id=3162625]

  • lightweight obfuscation resilient detection and family identification of android malware
    ACM Transactions on Software Engineering and Methodology, 2018
    Co-Authors: Joshua Garcia, Mahmoud Hammad, Sam Malek
    Abstract:

    The number of malicious Android apps is increasing rapidly. Android malware can damage or alter other files or settings, install additional applications, and so on. To determine such behaviors, a Security Analyst can significantly benefit from identifying the family to which an Android malware belongs rather than only detecting if an app is malicious. Techniques for detecting Android malware, and determining their families, lack the ability to handle certain obfuscations that aim to thwart detection. Moreover, some prior techniques face scalability issues, preventing them from detecting malware in a timely manner. To address these challenges, we present a novel machine-learning-based Android malware detection and family identification approach, RevealDroid, that operates without the need to perform complex program analyses or to extract large sets of features. Specifically, our selected features leverage categorized Android API usage, reflection-based features, and features from native binaries of apps. We assess RevealDroid for accuracy, efficiency, and obfuscation resilience using a large dataset consisting of more than 54,000 malicious and benign apps. Our experiments show that RevealDroid achieves an accuracy of 98% in detection of malware and an accuracy of 95% in determination of their families. We further demonstrate RevealDroid’s superiority against state-of-the-art approaches.