Android Malware Detection Using Machine Learning on Image Patterns
원문 초록
Android platform has been targeted by cyber-criminals due to the increase number of Android users in 2017. More than 8,000 Android malware were identified everyday making it is difficult for the malware analyst to detect them. Traditional malware detection techniques are no longer reliable to detect newly created malware in short period of time. In this paper, we use a different approach to detect Android malware. The Android malware will be visualised into gray scale images and their image features will be extracted using GIST descriptor. The detection will be done and compare using three different classifiers namely k-nearest neighbor (KNN), Random Forest (RF), and Decision Tree (DT)
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems
MASKDROID: Robust Android Malware Detection with Masked Graph Representations
DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations
MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks
Efficient Concept Drift Handling for Batch Android Malware Detection Models
토론
이 논문과 연결된 포럼 글이 아직 없습니다.