DroidChameleon: evaluating Android anti-malware against transformation attacks

By Vaibhav Rastogi, Yan Chen, Xuxian Jiang. ASIA CCS '13, 2013

407 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Software, Signal Processing

View Paper: https://www.semanticscholar.org/paper/eb4d3d9357927e6cb617eaba5cedeadc949d0fd7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Lorenzo Cavallaro
2. Sakir Sezer
3. Tao Xie
4. Wei Yang
5. Yue Duan
6. A. Aiken
7. Konrad Rieck
8. Malte Hubner

In Software

1. Lorenzo Cavallaro
2. Sakir Sezer
3. Wei Yang
4. Yue Duan
5. A. Aiken
6. Konrad Rieck
7. Malte Hubner
8. Heng Yin

In Signal Processing

1. Lorenzo Cavallaro
2. Sakir Sezer
3. Wei Yang
4. Tao Xie
5. Yue Duan
6. A. Aiken
7. Konrad Rieck
8. Malte Hubner

Citations in the corpus

• The Evolution of Android Malware and Android Analysis Techniques (2017, methodology): “In 2013, DroidChameleon [Rastogi et al. 2013] submitted automatically obfuscated Android apps to ten popular AV products and found all ten vulnerable to trivial transformations, the lowest of the three transformations “tiers”.”
• Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps (2017, methodology): “We also implement a syntactic app obfuscation tool called OCTOPUS similar to DroidChameleon [33].”
• Stealth attacks: An extended insight into the obfuscation effects on Android malware (2015, unknown): “Rastogi et al. [20, 21] 33], an online service with which it is possible to scan a malicious application by means of multiple anti-malware systems.”
• DroidDetector: Android Malware Characterization and Detection Using Deep Learning (2016, methodology): “However, these signature-based methods can be easily evaded by bytecode-level transformation attacks [13] .”
• Semantics-Aware Android Malware Classification Using Weighted Contextual API Dependency Graphs (2014, methodology): “D.2.4 [Software Engineering]: Software/Program Verification— Validation; D.4.6 [Operating Systems]: Security and Protection— Invasive software”
• MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (2016, background): “However, as it is unrealistic to deploy dynamic analysis techniques directly on users’ devices, due to the overhead they introduce, these are typically used offline [45], [50], [67].”
• Android Security: A Survey of Issues, Malware Penetration, and Defenses (2015, methodology): “Following the trends of the desktop platform, code obfuscation is also evolving on Android [73], [74].”
• Apposcopy: semantics-based detection of Android malware through static analysis (2014, background): “As shown in a recent study, malware detectors that are based on syntactic low-level signatures can be easily circumvented using simple program obfuscations [34].”
• A Multimodal Deep Learning Method for Android Malware Detection Using Various Features (2019, methodology): “Six obfuscation techniques in [60] and one technique (PI) we defined were tested in the experiments.”
• DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket (2014, background): “While obfuscation strategies, such as repackaging, code reordering or junk code insertion do not affect DREBIN, renaming of activities and components between the learning and detection phase may impair discriminative features [30, 38].”