The Experts below are selected from a list of 48 Experts worldwide ranked by ideXlab platform

Arun Kumar Sangaiah - One of the best experts on this subject based on the ideXlab platform.

  • Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics
    Multimedia Tools and Applications, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Norfazlina B Anuar, Arun Kumar Sangaiah
    Abstract:

    Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, Directory Path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.

  • Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management
    Journal of Medical Systems, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Nor Badrul Anuar, Ibrahim Abaker Targio Hashem, Syafiq Bachok, Arun Kumar Sangaiah
    Abstract:

    The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims’ private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) Directory Path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).

Ahmad Firdaus - One of the best experts on this subject based on the ideXlab platform.

  • Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics
    Multimedia Tools and Applications, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Norfazlina B Anuar, Arun Kumar Sangaiah
    Abstract:

    Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, Directory Path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.

  • Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management
    Journal of Medical Systems, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Nor Badrul Anuar, Ibrahim Abaker Targio Hashem, Syafiq Bachok, Arun Kumar Sangaiah
    Abstract:

    The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims’ private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) Directory Path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).

  • Mobile malware anomaly-based detection systems using static analysis features / Ahmad Firdaus Zainal Abidin
    2017
    Co-Authors: Ahmad Firdaus, Zainal Abidin
    Abstract:

    Presently, the rise of demand for Android gadget motivates the unscrupulous author to develop malware to compromise mobile devices for malicious and private purposes. The categories of mobile malware types are root exploit, botnet, and Trojan. Consequently, in order to classify an application either malware or benign, security practitioners conduct two types of analysis, namely dynamic and static. Dynamic analysis classifies an application as malware by executing it and monitors the behavior. However, it demands high computing requirements and monitors in a limited range of time. On the other hand, static analysis reverses engineer an application and examine overall code thoroughly, therefore further capable of examining the whole structure of the application. Furthermore, static analysis consumes low resources (for instance, CPU, memory, storage) and less time processing. As static analysis concentrates on the code, security practitioners face challenges to select the best features among thousand lines of it. Although they suggest several features, however, there are still provides many features available to be explored. Furthermore, less attention has been given to root exploit features specifically. It is one of the critical malware which compromises operating system kernel to obtain root privileges. When the attackers obtain the privileges, they are able to bypass security mechanisms and install other possible types of malware to the devices. Moreover, in order to achieve an efficient malware prediction in machine learning, it needs features in a minimal amount to enhance accuracy with fewer data, less time processing and reduces model complexity. Therefore, to achieve the aim of finding the best and minimal features to detect malware with root exploit, this study adopts bio-inspired Genetic Search (GS), conveys the range iv of repeated features in similar application, and investigates root exploit to gain the best features to predict unknown malware using machine learning. The features categories involved in all these experiments are the permission, Directory Path, code-based, system command, and telephony. In detecting root exploit, the category involved is the novel features called Android Debug Bridge (ADB). By obtaining the best features derived from these experiments, this study applies it in machine learning to predict unknown malware. To demonstrate the results, this experiment evaluated six benchmarks (for instance, accuracy, True Positive Rate (TPR), False Positive Rate (FPR), recall, precision, and f-measure) to test the prediction and performance. From the outstanding results being collected, a website was established to validate the unique static features with machine learning mechanism to investigate its efficiency and practicality. Through the outcomes assembled, this research has verified that the unique static features capable of predicting unknown malware together with root exploit. The contributions of this study were investigated, selected, proposed, designed and evaluated the best features in detecting malware by using static analysis.

Mohd Faizal Ab Razak - One of the best experts on this subject based on the ideXlab platform.

  • Bio-inspired computational paradigm for feature investigation and malware detection: interactive analytics
    Multimedia Tools and Applications, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Norfazlina B Anuar, Arun Kumar Sangaiah
    Abstract:

    Recently, people rely on mobile devices to conduct their daily fundamental activities. Simultaneously, most of the people prefer devices with Android operating system. As the demand expands, deceitful authors develop malware to compromise Android for private and money purposes. Consequently, security analysts have to conduct static and dynamic analyses to counter malware violation. In this paper, we adopt static analysis which only requests minimal resource consumption and rapid processing. However, finding a minimum set of features in the static analysis are vital because it removes irrelevant data, reduces the runtime of machine learning detection and reduces the dimensionality of datasets. Therefore, in this paper, we investigate three categories of features, which are permissions, Directory Path, and telephony. This investigation considers the features frequency as well as repeatedly used in each application. Subsequently, this study evaluates the proposed features in three bio-inspired machine learning classifiers in artificial neural network (ANN) category to signify the usefulness of ANN type in uncovering unknown malware. The classifiers are multilayer perceptron (MLP), voted perceptron (VP) and radial basis function network (RBFN). Among all these three classifiers, the outstanding outcomes acquire is the MLP, which achieves 90% in accuracy and 87% in true positive rate (TPR), as well as 97% accuracy in our Bio Analyzer prediction system.

  • Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management
    Journal of Medical Systems, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Nor Badrul Anuar, Ibrahim Abaker Targio Hashem, Syafiq Bachok, Arun Kumar Sangaiah
    Abstract:

    The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims’ private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) Directory Path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).

Ibrahim Abaker Targio Hashem - One of the best experts on this subject based on the ideXlab platform.

  • Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management
    Journal of Medical Systems, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Nor Badrul Anuar, Ibrahim Abaker Targio Hashem, Syafiq Bachok, Arun Kumar Sangaiah
    Abstract:

    The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims’ private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) Directory Path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).

Nor Badrul Anuar - One of the best experts on this subject based on the ideXlab platform.

  • Root Exploit Detection and Features Optimization: Mobile Device and Blockchain Based Medical Data Management
    Journal of Medical Systems, 2018
    Co-Authors: Ahmad Firdaus, Mohd Faizal Ab Razak, Nor Badrul Anuar, Ibrahim Abaker Targio Hashem, Syafiq Bachok, Arun Kumar Sangaiah
    Abstract:

    The increasing demand for Android mobile devices and blockchain has motivated malware creators to develop mobile malware to compromise the blockchain. Although the blockchain is secure, attackers have managed to gain access into the blockchain as legal users, thereby comprising important and crucial information. Examples of mobile malware include root exploit, botnets, and Trojans and root exploit is one of the most dangerous malware. It compromises the operating system kernel in order to gain root privileges which are then used by attackers to bypass the security mechanisms, to gain complete control of the operating system, to install other possible types of malware to the devices, and finally, to steal victims’ private keys linked to the blockchain. For the purpose of maximizing the security of the blockchain-based medical data management (BMDM), it is crucial to investigate the novel features and approaches contained in root exploit malware. This study proposes to use the bio-inspired method of practical swarm optimization (PSO) which automatically select the exclusive features that contain the novel android debug bridge (ADB). This study also adopts boosting (adaboost, realadaboost, logitboost, and multiboost) to enhance the machine learning prediction that detects unknown root exploit, and scrutinized three categories of features including (1) system command, (2) Directory Path and (3) code-based. The evaluation gathered from this study suggests a marked accuracy value of 93% with Logitboost in the simulation. Logitboost also helped to predicted all the root exploit samples in our developed system, the root exploit detection system (RODS).