The Experts below are selected from a list of 1920 Experts worldwide ranked by ideXlab platform

Alison Anderson - One of the best experts on this subject based on the ideXlab platform.

  • Machine-independent audit trail analysis - a tool for continuous audit assurance
    Intelligent Systems in Accounting Finance & Management, 2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    [Summary]: This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorised user activity in multi-usercomputer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for 2 main types of events - user activity rejected by the system's Security settings (failed actions) and user's behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge based system is suited to applications that require expertise to perform well-defined, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profile database (knowledge base) that allows identification of users with rejected behaviour as well as abnormal behaviour. The knowledge based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table 1). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profiles and forecasts of behaviour on a daily basis. As such, it also 'learns' from changes in user behaviour. The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity.

  • Machine-independent audit trail analysis—a tool for continuous audit assurance: Research Articles
    International Journal of Intelligent Systems in Accounting Finance & Management, 2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge-based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorized user activity in multi-user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors.Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for two main types of events—user activity rejected by the system's Security settings (failed actions) and users behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge-based system is suited to applications that require expertise to perform well-defned, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems).To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profle database (knowledge base) that allows identifcation of users with rejected behaviour as well as abnormal behaviour. The knowledge-based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table I).The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profles and forecasts of behaviour on a daily basis. As such, it also ‘learns’ from changes in user behaviour.The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods, are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity. Copyright © 2004 John Wiley & Sons, Ltd.

  • Machine-independent audit trail analysis – a decision support tool for continuous audit assurance
    2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorised user activity in multi-user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for 2 main types of events - user activity rejected by the system's Security settings (failed actions) and user's behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge based system is suited to applications that require expertise to perform well-defined, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profile database (knowledge base) that allows identification of users with rejected behaviour as well as abnormal behaviour. The knowledge based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table 1). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profiles and forecasts of behaviour on a daily basis. As such, it also 'learns' from changes in user behaviour. The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity.

Peter Best - One of the best experts on this subject based on the ideXlab platform.

  • Machine-independent audit trail analysis - a tool for continuous audit assurance
    Intelligent Systems in Accounting Finance & Management, 2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    [Summary]: This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorised user activity in multi-usercomputer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for 2 main types of events - user activity rejected by the system's Security settings (failed actions) and user's behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge based system is suited to applications that require expertise to perform well-defined, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profile database (knowledge base) that allows identification of users with rejected behaviour as well as abnormal behaviour. The knowledge based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table 1). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profiles and forecasts of behaviour on a daily basis. As such, it also 'learns' from changes in user behaviour. The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity.

  • Machine-independent audit trail analysis—a tool for continuous audit assurance: Research Articles
    International Journal of Intelligent Systems in Accounting Finance & Management, 2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge-based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorized user activity in multi-user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors.Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for two main types of events—user activity rejected by the system's Security settings (failed actions) and users behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge-based system is suited to applications that require expertise to perform well-defned, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems).To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profle database (knowledge base) that allows identifcation of users with rejected behaviour as well as abnormal behaviour. The knowledge-based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table I).The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profles and forecasts of behaviour on a daily basis. As such, it also ‘learns’ from changes in user behaviour.The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods, are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity. Copyright © 2004 John Wiley & Sons, Ltd.

  • Machine-independent audit trail analysis – a decision support tool for continuous audit assurance
    2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorised user activity in multi-user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for 2 main types of events - user activity rejected by the system's Security settings (failed actions) and user's behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge based system is suited to applications that require expertise to perform well-defined, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profile database (knowledge base) that allows identification of users with rejected behaviour as well as abnormal behaviour. The knowledge based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table 1). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profiles and forecasts of behaviour on a daily basis. As such, it also 'learns' from changes in user behaviour. The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity.

Hao Chen - One of the best experts on this subject based on the ideXlab platform.

  • androidleaks automatically detecting potential privacy leaks in android applications on a large scale
    Trust and Trustworthy Computing, 2012
    Co-Authors: Clint Gibler, Jonathan Crussell, Jeremy D Erickson, Hao Chen
    Abstract:

    As mobile devices become more widespread and powerful, they store more sensitive data, which includes not only users' personal information but also the data collected via sensors throughout the day. When mobile applications have access to this growing amount of sensitive information, they may leak it carelessly or maliciously. Google's Android operating system provides a permissions-based Security model that restricts an application's access to the user's private data. Each application statically declares the sensitive data and functionality that it requires in a manifest, which is presented to the user upon installation. However, it is not clear to the user how sensitive data is used once the application is installed. To combat this problem, we present AndroidLeaks, a static analysis framework for automatically finding potential leaks of sensitive information in Android applications on a massive scale. AndroidLeaks drastically reduces the number of applications and the number of traces that a Security Auditor has to verify manually. We evaluate the efficacy of AndroidLeaks on 24,350 Android applications from several Android markets. AndroidLeaks found 57,299 potential privacy leaks in 7,414 Android applications, out of which we have manually verified that 2,342 applications leak private data including phone information, GPS location, WiFi data, and audio recorded with the microphone. AndroidLeaks examined these applications in 30 hours, which indicates that it is capable of scaling to the increasingly large set of available applications.

  • TRUST - AndroidLeaks: automatically detecting potential privacy leaks in android applications on a large scale
    Trust and Trustworthy Computing, 2012
    Co-Authors: Clint Gibler, Jonathan Crussell, Jeremy D Erickson, Hao Chen
    Abstract:

    As mobile devices become more widespread and powerful, they store more sensitive data, which includes not only users' personal information but also the data collected via sensors throughout the day. When mobile applications have access to this growing amount of sensitive information, they may leak it carelessly or maliciously. Google's Android operating system provides a permissions-based Security model that restricts an application's access to the user's private data. Each application statically declares the sensitive data and functionality that it requires in a manifest, which is presented to the user upon installation. However, it is not clear to the user how sensitive data is used once the application is installed. To combat this problem, we present AndroidLeaks, a static analysis framework for automatically finding potential leaks of sensitive information in Android applications on a massive scale. AndroidLeaks drastically reduces the number of applications and the number of traces that a Security Auditor has to verify manually. We evaluate the efficacy of AndroidLeaks on 24,350 Android applications from several Android markets. AndroidLeaks found 57,299 potential privacy leaks in 7,414 Android applications, out of which we have manually verified that 2,342 applications leak private data including phone information, GPS location, WiFi data, and audio recorded with the microphone. AndroidLeaks examined these applications in 30 hours, which indicates that it is capable of scaling to the increasingly large set of available applications.

George M. Mohay - One of the best experts on this subject based on the ideXlab platform.

  • Machine-independent audit trail analysis - a tool for continuous audit assurance
    Intelligent Systems in Accounting Finance & Management, 2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    [Summary]: This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorised user activity in multi-usercomputer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for 2 main types of events - user activity rejected by the system's Security settings (failed actions) and user's behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge based system is suited to applications that require expertise to perform well-defined, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profile database (knowledge base) that allows identification of users with rejected behaviour as well as abnormal behaviour. The knowledge based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table 1). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profiles and forecasts of behaviour on a daily basis. As such, it also 'learns' from changes in user behaviour. The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity.

  • Machine-independent audit trail analysis—a tool for continuous audit assurance: Research Articles
    International Journal of Intelligent Systems in Accounting Finance & Management, 2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge-based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorized user activity in multi-user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors.Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for two main types of events—user activity rejected by the system's Security settings (failed actions) and users behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge-based system is suited to applications that require expertise to perform well-defned, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems).To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profle database (knowledge base) that allows identifcation of users with rejected behaviour as well as abnormal behaviour. The knowledge-based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table I).The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profles and forecasts of behaviour on a daily basis. As such, it also ‘learns’ from changes in user behaviour.The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods, are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity. Copyright © 2004 John Wiley & Sons, Ltd.

  • Machine-independent audit trail analysis – a decision support tool for continuous audit assurance
    2004
    Co-Authors: Peter Best, George M. Mohay, Alison Anderson
    Abstract:

    This paper reports the results of a research project which examines the feasibility of developing a machine-independent audit trail analyser (MIATA). MIATA is a knowledge based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for Auditors when assessing the risk of unauthorised user activity in multi-user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external Auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert Security Auditor would be needed to look for 2 main types of events - user activity rejected by the system's Security settings (failed actions) and user's behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge based system is suited to applications that require expertise to perform well-defined, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine-independent intelligent audit trail analysis, an anomaly-detection approach is adopted. Time series forecasting methods are used to develop and maintain the user profile database (knowledge base) that allows identification of users with rejected behaviour as well as abnormal behaviour. The knowledge based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table 1). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user profiles and forecasts of behaviour on a daily basis. As such, it also 'learns' from changes in user behaviour. The feasibility of generating machine-independent audit trail records, and the applicability of the anomaly-detection approach and time series forecasting methods are demonstrated using three case studies. These results support the proposal that developing a machine-independent audit trail analyser is feasible. Such a system will be an invaluable aid to an Auditor in detecting potential computer intrusions and monitoring user activity.

Clint Gibler - One of the best experts on this subject based on the ideXlab platform.

  • androidleaks automatically detecting potential privacy leaks in android applications on a large scale
    Trust and Trustworthy Computing, 2012
    Co-Authors: Clint Gibler, Jonathan Crussell, Jeremy D Erickson, Hao Chen
    Abstract:

    As mobile devices become more widespread and powerful, they store more sensitive data, which includes not only users' personal information but also the data collected via sensors throughout the day. When mobile applications have access to this growing amount of sensitive information, they may leak it carelessly or maliciously. Google's Android operating system provides a permissions-based Security model that restricts an application's access to the user's private data. Each application statically declares the sensitive data and functionality that it requires in a manifest, which is presented to the user upon installation. However, it is not clear to the user how sensitive data is used once the application is installed. To combat this problem, we present AndroidLeaks, a static analysis framework for automatically finding potential leaks of sensitive information in Android applications on a massive scale. AndroidLeaks drastically reduces the number of applications and the number of traces that a Security Auditor has to verify manually. We evaluate the efficacy of AndroidLeaks on 24,350 Android applications from several Android markets. AndroidLeaks found 57,299 potential privacy leaks in 7,414 Android applications, out of which we have manually verified that 2,342 applications leak private data including phone information, GPS location, WiFi data, and audio recorded with the microphone. AndroidLeaks examined these applications in 30 hours, which indicates that it is capable of scaling to the increasingly large set of available applications.

  • TRUST - AndroidLeaks: automatically detecting potential privacy leaks in android applications on a large scale
    Trust and Trustworthy Computing, 2012
    Co-Authors: Clint Gibler, Jonathan Crussell, Jeremy D Erickson, Hao Chen
    Abstract:

    As mobile devices become more widespread and powerful, they store more sensitive data, which includes not only users' personal information but also the data collected via sensors throughout the day. When mobile applications have access to this growing amount of sensitive information, they may leak it carelessly or maliciously. Google's Android operating system provides a permissions-based Security model that restricts an application's access to the user's private data. Each application statically declares the sensitive data and functionality that it requires in a manifest, which is presented to the user upon installation. However, it is not clear to the user how sensitive data is used once the application is installed. To combat this problem, we present AndroidLeaks, a static analysis framework for automatically finding potential leaks of sensitive information in Android applications on a massive scale. AndroidLeaks drastically reduces the number of applications and the number of traces that a Security Auditor has to verify manually. We evaluate the efficacy of AndroidLeaks on 24,350 Android applications from several Android markets. AndroidLeaks found 57,299 potential privacy leaks in 7,414 Android applications, out of which we have manually verified that 2,342 applications leak private data including phone information, GPS location, WiFi data, and audio recorded with the microphone. AndroidLeaks examined these applications in 30 hours, which indicates that it is capable of scaling to the increasingly large set of available applications.