2023 |
Pimenta, Thalita Scharr Rodrigues; Ceschin, Fabricio; Gregio, Andre ANDROIDGYNY: Reviewing Clustering Techniques for Android Malware Family Classification Journal Article Digital Threats, 2023, ISSN: 2692-1626, (Just Accepted). Abstract | Links | BibTeX | Tags: Classification, Mobile Malware, Phylogeny @article{10.1145/3587471, title = {ANDROIDGYNY: Reviewing Clustering Techniques for Android Malware Family Classification}, author = {Thalita Scharr Rodrigues Pimenta and Fabricio Ceschin and Andre Gregio}, url = {https://doi.org/10.1145/3587471 https://secret.inf.ufpr.br/papers/androidgyny_thalita.pdf}, doi = {10.1145/3587471}, issn = {2692-1626}, year = {2023}, date = {2023-03-01}, journal = {Digital Threats}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {Thousands of malicious applications (apps) are daily created, modified with the aid of automation tools, and released on the World Wide Web. Several techniques have been applied over the years to identify whether an APK is malicious or not. The use of these techniques intends to identify unknown malware mainly by calculating the similarity of a sample with previously grouped, already known families of malicious apps. Thus, high rates of accuracy would enable several countermeasures: from further quick detection to the development of vaccines and aid for reverse engineering new variants. However, most of the literature consists of limited experiments—either short-term and offline or based exclusively on well-known malicious apps’ families. In this paper, we explore the use of malware phylogeny, a term borrowed from biology, consisting of the genealogical study of the relationship between elements and families. Also, we investigate the literature on clustering techniques applied to mobile malware classification and discuss how researchers have been setting up their experiments.}, note = {Just Accepted}, keywords = {Classification, Mobile Malware, Phylogeny}, pubstate = {published}, tppubtype = {article} } Thousands of malicious applications (apps) are daily created, modified with the aid of automation tools, and released on the World Wide Web. Several techniques have been applied over the years to identify whether an APK is malicious or not. The use of these techniques intends to identify unknown malware mainly by calculating the similarity of a sample with previously grouped, already known families of malicious apps. Thus, high rates of accuracy would enable several countermeasures: from further quick detection to the development of vaccines and aid for reverse engineering new variants. However, most of the literature consists of limited experiments—either short-term and offline or based exclusively on well-known malicious apps’ families. In this paper, we explore the use of malware phylogeny, a term borrowed from biology, consisting of the genealogical study of the relationship between elements and families. Also, we investigate the literature on clustering techniques applied to mobile malware classification and discuss how researchers have been setting up their experiments. |
2023 |
ANDROIDGYNY: Reviewing Clustering Techniques for Android Malware Family Classification Journal Article Digital Threats, 2023, ISSN: 2692-1626, (Just Accepted). |