Pub Date : 2020-06-01DOI: 10.1109/ICCCI49374.2020.9145994
Yung-Ching Shyong, Tzung-Han Jeng, Yi-Ming Chen
Nowadays Android smart mobile devices have become the main target of malware developers, so detecting and preventing Android malware has become an important issue of information security. Therefore, this paper proposes an Android application classification system that combines static permissions and dynamic packet analysis. This system first obtains the static information of Android applications through static analysis, classifies the applications as benign or malicious through machine learning, and avoids excessive dynamic data collection time by filtering out benign applications. Then in the dynamic analysis stage, the malware's network traffic is used to extract multiple types of features, and then machine learning is used to achieve the malware family classification. The experimental results showed that the accuracy rate of the static model for malicious and benign classification was 98.86%. On the other hand, the accuracy of the dynamic model proposed in this paper for family classification of applications is 96%, which is better than 94.33% of DroidClassifier [1]. The final experiment confirmed that the system proposed in this paper can not only save 52.5% of dynamic data collection time but also improve the accuracy of Android application family classification.
{"title":"Combining Static Permissions and Dynamic Packet Analysis to Improve Android Malware Detection","authors":"Yung-Ching Shyong, Tzung-Han Jeng, Yi-Ming Chen","doi":"10.1109/ICCCI49374.2020.9145994","DOIUrl":"https://doi.org/10.1109/ICCCI49374.2020.9145994","url":null,"abstract":"Nowadays Android smart mobile devices have become the main target of malware developers, so detecting and preventing Android malware has become an important issue of information security. Therefore, this paper proposes an Android application classification system that combines static permissions and dynamic packet analysis. This system first obtains the static information of Android applications through static analysis, classifies the applications as benign or malicious through machine learning, and avoids excessive dynamic data collection time by filtering out benign applications. Then in the dynamic analysis stage, the malware's network traffic is used to extract multiple types of features, and then machine learning is used to achieve the malware family classification. The experimental results showed that the accuracy rate of the static model for malicious and benign classification was 98.86%. On the other hand, the accuracy of the dynamic model proposed in this paper for family classification of applications is 96%, which is better than 94.33% of DroidClassifier [1]. The final experiment confirmed that the system proposed in this paper can not only save 52.5% of dynamic data collection time but also improve the accuracy of Android application family classification.","PeriodicalId":153290,"journal":{"name":"2020 2nd International Conference on Computer Communication and the Internet (ICCCI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130318775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-01DOI: 10.1109/ICCCI49374.2020.9145981
A. K. Ibrahim, E. Hagras, Adel Alfhar, H. A. El-Kamchochi
In this paper, a novel Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) has been introduced for Image Encryption. The biometric digital identity is extracted from the user fingerprint image as fingerprint minutia data incorporated with the chaotic logistic map and hence, a new DCBDI-IEC has been suggested. DCBI-IEC is used to control the key schedule for all encryption and decryption processing. Statistical analysis, differential analysis and key sensitivity test are performed to estimate the security strengths of the proposed DCBI-IEC system. The experimental results show that the proposed algorithm is robust against common signal processing attacks and provides a high security level for image encryption application.
{"title":"Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) for Crypto Images","authors":"A. K. Ibrahim, E. Hagras, Adel Alfhar, H. A. El-Kamchochi","doi":"10.1109/ICCCI49374.2020.9145981","DOIUrl":"https://doi.org/10.1109/ICCCI49374.2020.9145981","url":null,"abstract":"In this paper, a novel Dynamic Chaotic Biometric Identity Isomorphic Elliptic Curve (DCBI-IEC) has been introduced for Image Encryption. The biometric digital identity is extracted from the user fingerprint image as fingerprint minutia data incorporated with the chaotic logistic map and hence, a new DCBDI-IEC has been suggested. DCBI-IEC is used to control the key schedule for all encryption and decryption processing. Statistical analysis, differential analysis and key sensitivity test are performed to estimate the security strengths of the proposed DCBI-IEC system. The experimental results show that the proposed algorithm is robust against common signal processing attacks and provides a high security level for image encryption application.","PeriodicalId":153290,"journal":{"name":"2020 2nd International Conference on Computer Communication and the Internet (ICCCI)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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