The Experts below are selected from a list of 9168 Experts worldwide ranked by ideXlab platform
Guillaume Jourjon - One of the best experts on this subject based on the ideXlab platform.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
IEEE Transactions on Mobile Computing, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps to misguide users to install them for various reasons such as collecting information, spreading malware, or increasing advertisement revenue. Many counterfeits can be identified once installed, however even users may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. This paper leverages recent advances in deep learning to efficiently identify counterfeits. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings outperforms the baseline methods. We first evaluate the performance of the proposed method on two standard datasets and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively. Second, for the app counterfeit detection problem, we show that combined content and style embeddings achieve better performance. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
arXiv: Cryptography and Security, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted for publication. We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second, specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
Naveen Karunanayake - One of the best experts on this subject based on the ideXlab platform.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
IEEE Transactions on Mobile Computing, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps to misguide users to install them for various reasons such as collecting information, spreading malware, or increasing advertisement revenue. Many counterfeits can be identified once installed, however even users may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. This paper leverages recent advances in deep learning to efficiently identify counterfeits. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings outperforms the baseline methods. We first evaluate the performance of the proposed method on two standard datasets and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively. Second, for the app counterfeit detection problem, we show that combined content and style embeddings achieve better performance. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
arXiv: Cryptography and Security, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted for publication. We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second, specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
Suranga Seneviratne - One of the best experts on this subject based on the ideXlab platform.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
IEEE Transactions on Mobile Computing, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps to misguide users to install them for various reasons such as collecting information, spreading malware, or increasing advertisement revenue. Many counterfeits can be identified once installed, however even users may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. This paper leverages recent advances in deep learning to efficiently identify counterfeits. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings outperforms the baseline methods. We first evaluate the performance of the proposed method on two standard datasets and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively. Second, for the app counterfeit detection problem, we show that combined content and style embeddings achieve better performance. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
arXiv: Cryptography and Security, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted for publication. We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second, specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
Jathushan Rajasegaran - One of the best experts on this subject based on the ideXlab platform.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
IEEE Transactions on Mobile Computing, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps to misguide users to install them for various reasons such as collecting information, spreading malware, or increasing advertisement revenue. Many counterfeits can be identified once installed, however even users may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. This paper leverages recent advances in deep learning to efficiently identify counterfeits. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings outperforms the baseline methods. We first evaluate the performance of the proposed method on two standard datasets and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively. Second, for the app counterfeit detection problem, we show that combined content and style embeddings achieve better performance. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
arXiv: Cryptography and Security, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted for publication. We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second, specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.
Ashanie Gunathillake - One of the best experts on this subject based on the ideXlab platform.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
IEEE Transactions on Mobile Computing, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps to misguide users to install them for various reasons such as collecting information, spreading malware, or increasing advertisement revenue. Many counterfeits can be identified once installed, however even users may struggle to detect them before installation as app icons and descriptions can be quite similar to the original app. This paper leverages recent advances in deep learning to efficiently identify counterfeits. We show that for the problem of counterfeit detection, a novel approach of combining content embeddings and style embeddings outperforms the baseline methods. We first evaluate the performance of the proposed method on two standard datasets and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively. Second, for the app counterfeit detection problem, we show that combined content and style embeddings achieve better performance. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps.
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a multi modal neural embeddings approach for detecting mobile counterfeit apps a case study on google play store
arXiv: Cryptography and Security, 2020Co-Authors: Naveen Karunanayake, Jathushan Rajasegaran, Ashanie Gunathillake, Suranga Seneviratne, Guillaume JourjonAbstract:Counterfeit apps impersonate existing popular apps in attempts to misguide users to install them for various reasons such as collecting personal information or spreading malware. Many counterfeits can be identified once installed, however even a tech-savvy user may struggle to detect them before installation. To this end, this paper proposes to leverage the recent advances in deep learning methods to create image and text embeddings so that counterfeit apps can be efficiently identified when they are submitted for publication. We show that a novel approach of combining content embeddings and style embeddings outperforms the baseline methods for image similarity such as SIFT, SURF, and various image hashing methods. We first evaluate the performance of the proposed method on two well-known datasets for evaluating image similarity methods and show that content, style, and combined embeddings increase precision@k and recall@k by 10%-15% and 12%-25%, respectively when retrieving five nearest neighbours. Second, specifically for the app counterfeit detection problem, combined content and style embeddings achieve 12% and 14% increase in precision@k and recall@k, respectively compared to the baseline methods. Third, we present an analysis of approximately 1.2 million apps from Google Play Store and identify a set of potential counterfeits for top-10,000 popular apps. Under a Conservative Assumption, we were able to find 2,040 potential counterfeits that contain malware in a set of 49,608 apps that showed high similarity to one of the top-10,000 popular apps in Google Play Store. We also find 1,565 potential counterfeits asking for at least five additional dangerous permissions than the original app and 1,407 potential counterfeits having at least five extra third party advertisement libraries.