Anatoli Kalysch

Dr.-Ing. Anatoli Kalysch

Department of Computer Science
Chair of Computer Science 1 (IT Security Infrastructures)


I’m a security researcher in the field of mobile security associated with the IT Security Infrastructures Lab. Prior, I was a research fellow and Ph.D. candidate at Dr.-Ing. Tilo Müller’s System Security and Software Protection group here at the i1 lab. My main interests are authentication, app & backend security, and app hardening.

Research Interests

Machine Learning Approaches:

  • Supervised Learning;
  • Natural Language Processing;
  • Reinforcement Learning;

Mobile Security:

  • Application UI Security and Data Leakage;
  • Application Packers and Protectors;
  • Emulation and Analysis Detection;
  • Security of Cross-Compilation Frameworks;

Program Analysis Techniques:

  • Taint-tracking;
  • Code similarity measures;
  • Symbolic execution;
  • Fuzzing;

Obfuscation Techniques:

  • Virtualization-based Obfuscation;
  • Opaque Predicates;


Professional Activities

Supervised Student Theses

  • (Master’s Thesis) An Empirical Study of Malicious Native Libraries on Android (WiSe2017)
  • (Master’s Thesis) Evaluating the Effectiveness of Machine Learning for Android Packer Detection and Classification (WiSe2017)
  • (Master’s Thesis) Clickjacking Revised: An Automated Framework for Clickjacking Attacks (SoSe2018)
  • (Master’s Thesis) Dismantling On-Device Android Malware Protection (SoSe2018)
  • (Bachelor’s Thesis) Android UI-Instrumentation for Malware Analysis and Forensic Trace Generation (WiSe2018)
  • (Bachelor’s Thesis) Automated Static Vulnerability Detection for Android Third Party Applications (WiSe2018)
  • (Master’s Thesis) Enhancing Malware Analysis Through Automated IR-based Functionality Extraction (WiSe2018)
  • (Bachelor’s Thesis) Android Inter-Process Communication Fuzzing (WiSe2018)
  • (Master’s Thesis) Opaque Predicate and Junk Code resistant Decompilation (WiSe2018)
  • (Master’s Thesis) Automated Entropy-Based Detection of Cryptographic Functions in Binaries (WiSe2018)
  • (Master’s Thesis) A Study on Code and Functionality Reuse among Android Mass Malware Families (WiSe2018)
  • (Bachelor’s Thesis) Code and Vulnerability Reuse in Android 3rd Party Frameworks and Applications (SoSe2019)
  • (Master’s Thesis) Virtualization-Based Android App Obfuscation (SoSe2019)
  • (Master’s Thesis) Deep Learning in Automated Vulnerability Assessments for Android Applications (WiSe2019)
  • (Master’s Thesis) A Common Baseline for the Comparison of Code Similarity Measures (SoSe2020)

Supervised Seminar Papers

  • Location obfuscation techniques on Android-based devices (WiSe2016)
  • An anonymity conscious analysis of selected available cryptocurrency solutions (SoSe2017)
  • Evolution of Clickjacking on Android (WiSe2017)
  • Malware Analysis for Android – An Overview (WiSe2017)
  • Architecture Centric Security Analysis (WiSe2017)
  • A Post-Quantum Cryptography-based Evaluation of Banking Frontends (WiSe2018)
  • Beyond Record and Replay – UI-based Android Application Testing (WiSe2018)
  • Automated and Machine Learning Approaches at Malware Analysis on Android (SoSe2019)
  • Systematischer Vergleich der Sicherheitsfeatures von Android and iOS (SoSe2019)



ID: F95069D5
SHA1 Fingerprint: 0470 4645 32E3 4C29 7732 7CFA 9B04 DCF8 F950 69D5
Public Key: ASCII Armored




  • Körber, M., Kalysch, A., Massonne, W., & Benenson, Z. (2022). Usability of Antivirus Tools in a Threat Detection Scenario. In Weizhi Meng, Simone Fischer-Hübner, Christian D. Jensen (Eds.), IFIP Advances in Information and Communication Technology (pp. 306-322). Copenhagen, DNK: Springer Science and Business Media Deutschland GmbH.