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Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
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Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
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Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
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Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
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URL |
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Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
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Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
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Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
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|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Intelligent Android Malware Detection Using Radial Basis Function Networks and Permission Features |
|
Author |
Ammar Abdulrahman, Khalid Hashem, Gaze Adnan, Waleed Ali |
| Citation |
Vol. 21 No. 7 pp. 286-293 |
|
Abstract |
Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures. |
|
Keywords |
Android applications, Android malware detection, Radial basis function network, Feature selection |
|
URL |
|
|
Title |
Classification of Apple Tree Leaves Diseases using Deep Learning Methods |
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Author |
Ashwaq Alsayed, Amani Alsabei, Muhammad Arif |
| Citation |
Vol. 21 No. 7 pp. 324-330 |
|
Abstract |
Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investi- gated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%. |
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Keywords |
Deep Learning, Classification, Apple Tree Leaves Diseases |
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URL |
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Title |
Classification of Apple Tree Leaves Diseases using Deep Learning Methods |
|
Author |
Ashwaq Alsayed, Amani Alsabei, Muhammad Arif |
| Citation |
Vol. 21 No. 7 pp. 324-330 |
|
Abstract |
Agriculture is one of the essential needs of human life on planet Earth. It is the source of food and earnings for many individuals around the world. The economy of many countries is associated with the agriculture sector. Lots of diseases exist that attack various fruits and crops. Apple Tree Leaves also suffer different types of pathological conditions that affect their production. These pathological conditions include apple scab, cedar apple rust, or multiple diseases, etc. In this paper, an automatic detection framework based on deep learning is investi- gated for apple leaves disease classification. Different pre-trained models, VGG16, ResNetV2, InceptionV3, and MobileNetV2, are considered for transfer learning. A combination of parameters like learning rate, batch size, and optimizer is analyzed, and the best combination of ResNetV2 with Adam optimizer provided the best classification accuracy of 94%. |
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Keywords |
Deep Learning, Classification, Apple Tree Leaves Diseases |
|
URL |
