| Title | Effective of Obfuscated Android Malware Detection using Static Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Mantoro, Teddy, Fahriza, Muhammad Elky, Agni Catur Bhakti, Muhammad |
| Conference Name | 2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED) |
| Keywords | Android operating system, Behavioral sciences, data privacy, Data security, hash, Human Behavior, Malware, malware analysis, malware detection, Metrics, Operating systems, privacy, pubcrawl, resilience, Resiliency, risk value, static analysis, system protection |
| Abstract | The effective security system improvement from malware attacks on the Android operating system should be updated and improved. Effective malware detection increases the level of data security and high protection for the users. Malicious software or malware typically finds a means to circumvent the security procedure, even when the user is unaware whether the application can act as malware. The effectiveness of obfuscated android malware detection is evaluated by collecting static analysis data from a data set. The experiment assesses the risk level of which malware dataset using the hash value of the malware and records malware behavior. A set of hash SHA256 malware samples has been obtained from an internet dataset and will be analyzed using static analysis to record malware behavior and evaluate which risk level of the malware. According to the results, most of the algorithms provide the same total score because of the multiple crime inside the malware application. |
| DOI | 10.1109/ICCED56140.2022.10010587 |
| Citation Key | mantoro_effective_2022 |
Effective of Obfuscated Android Malware Detection using Static Analysis