The Experts below are selected from a list of 447 Experts worldwide ranked by ideXlab platform

Sanjay K. Sahay - One of the best experts on this subject based on the ideXlab platform.

  • Group-wise classification approach to improve Android malicious apps detection accuracy
    arXiv: Cryptography and Security, 2019
    Co-Authors: Ashu Sharma, Sanjay K. Sahay
    Abstract:

    In the fast-growing smart devices, Android is the most popular OS, and due to its attractive features, mobility, ease of use, these devices hold sensitive information such as personal data, browsing history, shopping history, financial details, etc. Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the Malware. These Malware are continuously growing and are also used for military espionage, disrupting the industry, power grids, etc. To detect these Malware, traditional signature matching techniques are widely used. However, such strategies are not capable to detect the advanced Android malicious apps because Malware Developer uses several obfuscation techniques. Hence, researchers are continuously addressing the security issues in the Android based smart devices. Therefore, in this paper using Drebin benchmark Malware dataset we experimentally demonstrate how to improve the detection accuracy by analyzing the apps after grouping the collected data based on the permissions and achieved 97.15% overall average accuracy. Our results outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%, 2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that among the groups, Microphone group detection accuracy is least while Calendar group apps are detected with the highest accuracy, and with the highest accuracy, and for the best performance, one shall take 80-100 features.

  • An Investigation of the Classifiers to Detect Android Malicious Apps
    Information and Communication Technology, 2017
    Co-Authors: Ashu Sharma, Sanjay K. Sahay
    Abstract:

    Android devices are growing exponentially and are connected through the Internet accessing billion of online Websites. The popularity of these devices encourages Malware Developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although for the detection of malicious apps different approaches are discussed. However, proposed approaches are not sufficed to detect the advanced Malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcode occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy (~79.27%) is best among the investigated classifiers. However, true positives rate, i.e. Malware detection rate is highest (~99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.

Sahay, Sanjay K. - One of the best experts on this subject based on the ideXlab platform.

  • Group-wise classification approach to improve Android malicious apps detection accuracy
    2019
    Co-Authors: Sharma Ashu, Sahay, Sanjay K.
    Abstract:

    In the fast-growing smart devices, Android is the most popular OS, and due to its attractive features, mobility, ease of use, these devices hold sensitive information such as personal data, browsing history, shopping history, financial details, etc. Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the Malware. These Malware are continuously growing and are also used for military espionage, disrupting the industry, power grids, etc. To detect these Malware, traditional signature matching techniques are widely used. However, such strategies are not capable to detect the advanced Android malicious apps because Malware Developer uses several obfuscation techniques. Hence, researchers are continuously addressing the security issues in the Android based smart devices. Therefore, in this paper using Drebin benchmark Malware dataset we experimentally demonstrate how to improve the detection accuracy by analyzing the apps after grouping the collected data based on the permissions and achieved 97.15% overall average accuracy. Our results outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%, 2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that among the groups, Microphone group detection accuracy is least while Calendar group apps are detected with the highest accuracy, and with the highest accuracy, and for the best performance, one shall take 80-100 features.Comment: 9 pages, 20 Figure

  • An investigation of the classifiers to detect android malicious apps
    2018
    Co-Authors: Sharma Ashu, Sahay, Sanjay K.
    Abstract:

    Android devices are growing exponentially and are connected through the internet accessing billion of online websites. The popularity of these devices encourages Malware Developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although, for the detection of malicious apps different approaches are discussed. However, proposed approaches are not suffice to detect the advanced Malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcodes occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy (79.27%) is best among the investigated classifiers. However, true positives rate i.e. Malware detection rate is highest (99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.Comment: 8 Pages, 8 Figure

Ashu Sharma - One of the best experts on this subject based on the ideXlab platform.

  • Group-wise classification approach to improve Android malicious apps detection accuracy
    arXiv: Cryptography and Security, 2019
    Co-Authors: Ashu Sharma, Sanjay K. Sahay
    Abstract:

    In the fast-growing smart devices, Android is the most popular OS, and due to its attractive features, mobility, ease of use, these devices hold sensitive information such as personal data, browsing history, shopping history, financial details, etc. Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the Malware. These Malware are continuously growing and are also used for military espionage, disrupting the industry, power grids, etc. To detect these Malware, traditional signature matching techniques are widely used. However, such strategies are not capable to detect the advanced Android malicious apps because Malware Developer uses several obfuscation techniques. Hence, researchers are continuously addressing the security issues in the Android based smart devices. Therefore, in this paper using Drebin benchmark Malware dataset we experimentally demonstrate how to improve the detection accuracy by analyzing the apps after grouping the collected data based on the permissions and achieved 97.15% overall average accuracy. Our results outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%, 2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that among the groups, Microphone group detection accuracy is least while Calendar group apps are detected with the highest accuracy, and with the highest accuracy, and for the best performance, one shall take 80-100 features.

  • An Investigation of the Classifiers to Detect Android Malicious Apps
    Information and Communication Technology, 2017
    Co-Authors: Ashu Sharma, Sanjay K. Sahay
    Abstract:

    Android devices are growing exponentially and are connected through the Internet accessing billion of online Websites. The popularity of these devices encourages Malware Developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although for the detection of malicious apps different approaches are discussed. However, proposed approaches are not sufficed to detect the advanced Malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcode occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy (~79.27%) is best among the investigated classifiers. However, true positives rate, i.e. Malware detection rate is highest (~99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.

Sharma Ashu - One of the best experts on this subject based on the ideXlab platform.

  • Group-wise classification approach to improve Android malicious apps detection accuracy
    2019
    Co-Authors: Sharma Ashu, Sahay, Sanjay K.
    Abstract:

    In the fast-growing smart devices, Android is the most popular OS, and due to its attractive features, mobility, ease of use, these devices hold sensitive information such as personal data, browsing history, shopping history, financial details, etc. Therefore, any security gap in these devices means that the information stored or accessing the smart devices are at high risk of being breached by the Malware. These Malware are continuously growing and are also used for military espionage, disrupting the industry, power grids, etc. To detect these Malware, traditional signature matching techniques are widely used. However, such strategies are not capable to detect the advanced Android malicious apps because Malware Developer uses several obfuscation techniques. Hence, researchers are continuously addressing the security issues in the Android based smart devices. Therefore, in this paper using Drebin benchmark Malware dataset we experimentally demonstrate how to improve the detection accuracy by analyzing the apps after grouping the collected data based on the permissions and achieved 97.15% overall average accuracy. Our results outperform the accuracy obtained without grouping data (79.27%, 2017), Arp, et al. (94%, 2014), Annamalai et al. (84.29%, 2016), Bahman Rashidi et al. (82%, 2017)) and Ali Feizollah, et al. (95.5%, 2017). The analysis also shows that among the groups, Microphone group detection accuracy is least while Calendar group apps are detected with the highest accuracy, and with the highest accuracy, and for the best performance, one shall take 80-100 features.Comment: 9 pages, 20 Figure

  • An investigation of the classifiers to detect android malicious apps
    2018
    Co-Authors: Sharma Ashu, Sahay, Sanjay K.
    Abstract:

    Android devices are growing exponentially and are connected through the internet accessing billion of online websites. The popularity of these devices encourages Malware Developer to penetrate the market with malicious apps to annoy and disrupt the victim. Although, for the detection of malicious apps different approaches are discussed. However, proposed approaches are not suffice to detect the advanced Malware to limit/prevent the damages. In this, very few approaches are based on opcode occurrence to classify the malicious apps. Therefore, this paper investigates the five classifiers using opcodes occurrence as the prominent features for the detection of malicious apps. For the analysis, we use WEKA tool and found that FT detection accuracy (79.27%) is best among the investigated classifiers. However, true positives rate i.e. Malware detection rate is highest (99.91%) by RF and fluctuate least with the different number of prominent features compared to other studied classifiers. The analysis shows that overall accuracy is majorly affected by the false positives of the classifier.Comment: 8 Pages, 8 Figure

Kiran Khatter - One of the best experts on this subject based on the ideXlab platform.

  • Malicious Application Detection and Classification System for Android Mobiles
    Cognitive Analytics, 2020
    Co-Authors: Sapna Malik, Kiran Khatter
    Abstract:

    The Android Mobiles constitute a large portion of mobile market which also attracts the Malware Developer for malicious gains. Every year hundreds of Malwares are detected in the Android market. Unofficial and Official Android market such as Google Play Store are infested with fake and malicious apps which is a warning alarm for naive user. Guided by this insight, this paper presents the malicious application detection and classification system using machine learning techniques by extracting and analyzing the Android Permission Feature of the Android applications. For the feature extraction, the authors of this work have developed the AndroData tool written in shell script and analyzed the extracted features of 1060 Android applications with machine learning algorithms. They have achieved the malicious application detection and classification accuracy of 98.2% and 87.3%, respectively with machine learning techniques.