The number of malware attacks on the Android platform has escalated over the past few years. The ... more The number of malware attacks on the Android platform has escalated over the past few years. The seriousness of these attacks can be depicted from the fact that around 25 million Android smartphones were infected with malware within the first half of 2019. Keeping these threats in mind, we aim to develop a hybrid Android malware detector based on ranked permissions and network traffic features. In this work, first, we find the permissions that are frequently present in normal and malicious apps and rank the permissions based upon their frequency in normal and malware dataset. Additionally, we applied different support thresholds to remove the unnecessary and redundant permissions from the rankings. Further, we merge the ranked permissions with the network traffic features to form a hybrid vector for all the apps in the dataset. Finally, we propose a novel algorithm that applies machine learning algorithms on the hybrid vectors consisting of permissions and traffic features to detect Android malware. The experimental results demonstrate that by using the hybrid approach, we could achieve 95.96% detection accuracy with the proposed algorithm.
International Journal of Recent Technology and Engineering (IJRTE), 2021
Over the past few years, malware attacks have risen in huge numbers on the Android platform. Sign... more Over the past few years, malware attacks have risen in huge numbers on the Android platform. Significant threats are posed by these attacks which may cause financial loss, information leakage, and damage to the system. Around 25 million smartphones were infected with malware within the first half of 2019 that depicts the seriousness of these attacks. Taking into account the danger posed by the Android malware to the users’ community, we aim to develop a static Android malware detector named SFDroid that analyzes manifest file components for malware detection. In this work, first, the proposed model ranks the manifest features according to their frequency in normal and malicious apps. This helps us to identify the significant features present in normal and malware datasets. Additionally, we apply support thresholds to remove the unnecessary and redundant features from the rankings. Further, we propose a novel algorithm that uses the ranked features, and several machine learning class...
2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), 2021
The number of malware attacks on the Android platform has escalated over the past few years. The ... more The number of malware attacks on the Android platform has escalated over the past few years. The seriousness of these attacks can be depicted from the fact that around 25 million Android smartphones were infected with malware within the first half of 2019. Keeping these threats in mind, we aim to develop a hybrid Android malware detector based on ranked permissions and network traffic features. In this work, first, we find the permissions that are frequently present in normal and malicious apps and rank the permissions based upon their frequency in normal and malware dataset. Additionally, we applied different support thresholds to remove the unnecessary and redundant permissions from the rankings. Further, we merge the ranked permissions with the network traffic features to form a hybrid vector for all the apps in the dataset. Finally, we propose a novel algorithm that applies machine learning algorithms on the hybrid vectors consisting of permissions and traffic features to detect Android malware. The experimental results demonstrate that by using the hybrid approach, we could achieve 95.96% detection accuracy with the proposed algorithm.
The number of malware attacks on the Android platform has escalated over the past few years. The ... more The number of malware attacks on the Android platform has escalated over the past few years. The seriousness of these attacks can be depicted from the fact that around 25 million Android smartphones were infected with malware within the first half of 2019. Keeping these threats in mind, we aim to develop a hybrid Android malware detector based on ranked permissions and network traffic features. In this work, first, we find the permissions that are frequently present in normal and malicious apps and rank the permissions based upon their frequency in normal and malware dataset. Additionally, we applied different support thresholds to remove the unnecessary and redundant permissions from the rankings. Further, we merge the ranked permissions with the network traffic features to form a hybrid vector for all the apps in the dataset. Finally, we propose a novel algorithm that applies machine learning algorithms on the hybrid vectors consisting of permissions and traffic features to detect Android malware. The experimental results demonstrate that by using the hybrid approach, we could achieve 95.96% detection accuracy with the proposed algorithm.
International Journal of Recent Technology and Engineering (IJRTE), 2021
Over the past few years, malware attacks have risen in huge numbers on the Android platform. Sign... more Over the past few years, malware attacks have risen in huge numbers on the Android platform. Significant threats are posed by these attacks which may cause financial loss, information leakage, and damage to the system. Around 25 million smartphones were infected with malware within the first half of 2019 that depicts the seriousness of these attacks. Taking into account the danger posed by the Android malware to the users’ community, we aim to develop a static Android malware detector named SFDroid that analyzes manifest file components for malware detection. In this work, first, the proposed model ranks the manifest features according to their frequency in normal and malicious apps. This helps us to identify the significant features present in normal and malware datasets. Additionally, we apply support thresholds to remove the unnecessary and redundant features from the rankings. Further, we propose a novel algorithm that uses the ranked features, and several machine learning class...
2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), 2021
The number of malware attacks on the Android platform has escalated over the past few years. The ... more The number of malware attacks on the Android platform has escalated over the past few years. The seriousness of these attacks can be depicted from the fact that around 25 million Android smartphones were infected with malware within the first half of 2019. Keeping these threats in mind, we aim to develop a hybrid Android malware detector based on ranked permissions and network traffic features. In this work, first, we find the permissions that are frequently present in normal and malicious apps and rank the permissions based upon their frequency in normal and malware dataset. Additionally, we applied different support thresholds to remove the unnecessary and redundant permissions from the rankings. Further, we merge the ranked permissions with the network traffic features to form a hybrid vector for all the apps in the dataset. Finally, we propose a novel algorithm that applies machine learning algorithms on the hybrid vectors consisting of permissions and traffic features to detect Android malware. The experimental results demonstrate that by using the hybrid approach, we could achieve 95.96% detection accuracy with the proposed algorithm.
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Papers by Gourav Garg