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DATDroid: Dynamic Analysis Technique in Android Malware Detection

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@article{IJASEIT10238,
   author = {Rajan Thangaveloo and Wong Wang Jing and Chiew Kang Leng and Johari Abdullah},
   title = {DATDroid: Dynamic Analysis Technique in Android Malware Detection},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {10},
   number = {2},
   year = {2020},
   pages = {536--541},
   keywords = {android malware; dynamic analysis; static analysis; hybrid analysis; malware detection.},
   abstract = {

Android system has become a target for malware developers due to its huge market globally in recent years. The emergence of 5G in the market and limited protocols post a great challenge to the security in Android. Hence, various techniques have been taken by researchers to ensure high security in Android devices. There are three types of analysis namely static, dynamic and hybrid analysis used to detect and analyze the malicious application in Android. Due to evolving nature of the malware, it is very challenging for the existing techniques to detect and analyze it efficiently and accurately. This paper proposed a Dynamic Analysis Technique in Android Malware detection called DATDroid. The proposed technique consists of three phases, which includes feature extraction, feature selection and classification phases. A total of five features namely system call, errors and time of system call process, CPU usage, memory and network packets are extracted. During the classification 70% of the dataset was allocated for training phase and 30% for testing phase using machine learning algorithm. Our experimental results achieved an overall accuracy of 91.7% with lower false positive rates as compared to benchmarked method. DATDroid also achieved higher precision and recall rate of 93.1% and 90.0%, respectively. Hence our proposed technique has proven to be able to classify malware more accurately and reduce misclassification of malware application as benign significantly.

},    issn = {2088-5334},    publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},    url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10238},    doi = {10.18517/ijaseit.10.2.10238} }

EndNote

%A Thangaveloo, Rajan
%A Wang Jing, Wong
%A Kang Leng, Chiew
%A Abdullah, Johari
%D 2020
%T DATDroid: Dynamic Analysis Technique in Android Malware Detection
%B 2020
%9 android malware; dynamic analysis; static analysis; hybrid analysis; malware detection.
%! DATDroid: Dynamic Analysis Technique in Android Malware Detection
%K android malware; dynamic analysis; static analysis; hybrid analysis; malware detection.
%X 

Android system has become a target for malware developers due to its huge market globally in recent years. The emergence of 5G in the market and limited protocols post a great challenge to the security in Android. Hence, various techniques have been taken by researchers to ensure high security in Android devices. There are three types of analysis namely static, dynamic and hybrid analysis used to detect and analyze the malicious application in Android. Due to evolving nature of the malware, it is very challenging for the existing techniques to detect and analyze it efficiently and accurately. This paper proposed a Dynamic Analysis Technique in Android Malware detection called DATDroid. The proposed technique consists of three phases, which includes feature extraction, feature selection and classification phases. A total of five features namely system call, errors and time of system call process, CPU usage, memory and network packets are extracted. During the classification 70% of the dataset was allocated for training phase and 30% for testing phase using machine learning algorithm. Our experimental results achieved an overall accuracy of 91.7% with lower false positive rates as compared to benchmarked method. DATDroid also achieved higher precision and recall rate of 93.1% and 90.0%, respectively. Hence our proposed technique has proven to be able to classify malware more accurately and reduce misclassification of malware application as benign significantly.

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10238 %R doi:10.18517/ijaseit.10.2.10238 %J International Journal on Advanced Science, Engineering and Information Technology %V 10 %N 2 %@ 2088-5334

IEEE

Rajan Thangaveloo,Wong Wang Jing,Chiew Kang Leng and Johari Abdullah,"DATDroid: Dynamic Analysis Technique in Android Malware Detection," International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 2, pp. 536-541, 2020. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.10.2.10238.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Thangaveloo, Rajan
AU  - Wang Jing, Wong
AU  - Kang Leng, Chiew
AU  - Abdullah, Johari
PY  - 2020
TI  - DATDroid: Dynamic Analysis Technique in Android Malware Detection
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 10 (2020) No. 2
Y2  - 2020
SP  - 536
EP  - 541
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - android malware; dynamic analysis; static analysis; hybrid analysis; malware detection.
N2  - 

Android system has become a target for malware developers due to its huge market globally in recent years. The emergence of 5G in the market and limited protocols post a great challenge to the security in Android. Hence, various techniques have been taken by researchers to ensure high security in Android devices. There are three types of analysis namely static, dynamic and hybrid analysis used to detect and analyze the malicious application in Android. Due to evolving nature of the malware, it is very challenging for the existing techniques to detect and analyze it efficiently and accurately. This paper proposed a Dynamic Analysis Technique in Android Malware detection called DATDroid. The proposed technique consists of three phases, which includes feature extraction, feature selection and classification phases. A total of five features namely system call, errors and time of system call process, CPU usage, memory and network packets are extracted. During the classification 70% of the dataset was allocated for training phase and 30% for testing phase using machine learning algorithm. Our experimental results achieved an overall accuracy of 91.7% with lower false positive rates as compared to benchmarked method. DATDroid also achieved higher precision and recall rate of 93.1% and 90.0%, respectively. Hence our proposed technique has proven to be able to classify malware more accurately and reduce misclassification of malware application as benign significantly.

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10238 DO - 10.18517/ijaseit.10.2.10238

RefWorks

RT Journal Article
ID 10238
A1 Thangaveloo, Rajan
A1 Wang Jing, Wong
A1 Kang Leng, Chiew
A1 Abdullah, Johari
T1 DATDroid: Dynamic Analysis Technique in Android Malware Detection
JF International Journal on Advanced Science, Engineering and Information Technology
VO 10
IS 2
YR 2020
SP 536
OP 541
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 android malware; dynamic analysis; static analysis; hybrid analysis; malware detection.
AB 

Android system has become a target for malware developers due to its huge market globally in recent years. The emergence of 5G in the market and limited protocols post a great challenge to the security in Android. Hence, various techniques have been taken by researchers to ensure high security in Android devices. There are three types of analysis namely static, dynamic and hybrid analysis used to detect and analyze the malicious application in Android. Due to evolving nature of the malware, it is very challenging for the existing techniques to detect and analyze it efficiently and accurately. This paper proposed a Dynamic Analysis Technique in Android Malware detection called DATDroid. The proposed technique consists of three phases, which includes feature extraction, feature selection and classification phases. A total of five features namely system call, errors and time of system call process, CPU usage, memory and network packets are extracted. During the classification 70% of the dataset was allocated for training phase and 30% for testing phase using machine learning algorithm. Our experimental results achieved an overall accuracy of 91.7% with lower false positive rates as compared to benchmarked method. DATDroid also achieved higher precision and recall rate of 93.1% and 90.0%, respectively. Hence our proposed technique has proven to be able to classify malware more accurately and reduce misclassification of malware application as benign significantly.

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=10238 DO - 10.18517/ijaseit.10.2.10238