Research domains of Android Security

Here are the list of research domains of Android security.

Android Application Analysis
Android Applications are analyzed in order to identify malware using machine learning techniques. We have used SVM for analyzing APK files. Assorted features are examined, and features are selected.

Android Security Visualization
Visualization of Android security risks is important to effectively communicate with the Android users.

Evaluation of the application ratings and evaluations
The ratings and evaluations of Android applications could be manipulated intentionally. Thouse manipulations are to be identified.

List of Publications on Android Security

  1. Takeshi Takahashi, Tao Ban, "Android Application Analysis using Machine Learning Techniques," Intelligent Systems Reference Library, 181 - 205, Springer Nature Switzerland AG, 2019.DOI:10.1007/978-3-319-98842-9_7
  2. Bo Sun, Tao Ban, Shun-Chieh Chang, Yeali S. Sun, Takeshi Takahashi, Daisuke Inoue, "A Scalable and Accurate Feature Representation Method for Identifying Malicious Mobile Applications," ACM Symposium On Applied Computing, ACM, April, 2019.
  3. Takeshi Takahashi, Tao Ban, Chin-Wei Tien, Chih-Hung Lin, Daisuke Inoue, Koji Nakao, "The Usability of Metadata for Android Application Analysis," The 23rd International Conference on Neural Information Processing, Kyoto, 2016.10.
  4. Tao Ban, Takeshi Takahashi, Shanqing Guo, Daisuke Inoue, Koji Nakao, "Integration of Multimodal Features for Android Malware Detection Based on Linear SVM," Asia Joint Conference on Information Security, Fukuoka, 2016.8.
  5. Takeshi Takahashi, Tao Ban, Takao Mimura, Koji Nakao, "Fine-Grained Risk Level Quantification Schemes based on APK Metadata," The 22nd International Conference on Neural Information Processing, 663 - 673, Istanbul, 2015.11. doi:10.1007/978-3-319-26555-1_75.
  6. Takeshi Takahashi, Koji Nakao, Akira Kanaoka, "Data Model for Android Package Information and Its Application to Risk Analysis System," First ACM Workshop on Information Sharing and Collaborative Security, 71 - 80, Arizona, 2014.11. doi:10.1145/2663876.2663881.
  7. Takeshi Takahashi, Keita Emura, Akira Kanaoka, Shin'ichiro Matsuo, Tadashi Minowa, "Risk Visualization and Alerting System: Architecture and Proof-of-Concept Implementation," International Workshop on Security in Embedded Systems and Smartphones, 3 - 10, Hangzhou, 2013.5. doi:10.1145/2484417.2484421.
  8. Takeshi Takahashi, Shin'ichiro Matsuo, Akira Kanaoka, Keita Emura, Yuuki Takano, "Visualization of user's end-to-end security risks," Symposium On Usable Privacy and Security, Washington, 2012.6.
  9. Takeshi Takahashi, Tao Ban, "Androidセキュリティ: Androidアプリの「マルウェア判定」「脆弱性検知」技術," アットマーク・アイティ, 2016.3.
  10. Takeshi Takahashi, "Androidセキュリティ: アプリからユーザー視点までイチから解説," アットマーク・アイティ, 2016.3. [preprint][bibtex]
  11. Takeshi Takahashi, Tao Ban, Takao Mimura, Koji Nakao, "メタ情報を活用したAndroidアプリケーションのリスク分析手法に関する検討," コンピュータセキュリティシンポジウム, 長崎, 2015.10.
  12. Takeshi Takahashi, Takao Mimura, Kanta Nishida, Koji Nakao, "カテゴリに基づくAndroid アプリのリスク値定量化技術の検討," 信学技報, 沖縄, 2015.3.
  13. Takeshi Takahashi, Yuuki Takano, Koji Nakao, Satoshi Ohta, Akira Kanaoka, Shoichi Sakane, Shin'ichiro Matsuo, "Android 端末のリスク評価フレームワークとそのプロトタイプ構築," 暗号と情報セキュリティシンポジウム, 鹿児島, 2014.1.

About NICT AICS

AICS is a research team working on cybersecurity using machine learning.

Active members are as follows:

  • Takeshi Takahashi, Research Manager at NICT CSL
  • Tao Ban, Senior Researcher at NICT CSL
  • Bo Sun, Researcher at NICT AIS

More details will be coming soon.

Contact

Please contact us via email: csl-ai [at] nict.go.jp