Dr.-Ing. Anatoli Kalysch

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

Room: Room 12.136
Martensstr. 3
91058 Erlangen

Passionate about IT security with a focus on vulnerability research and malware analysis. I have been teaching and tutoring courses in IT Forensics, as well as offensive and defensive IT Security at the IT Security Infrastructures Lab since 2016. I’m currently focusing on deep learning, especially in combination with Natural Language Processing approaches.

Research Interests

Machine Learning Approaches:

  • Supervised and Unsupervised Machine Learning;
  • Deep Learning;
  • Natural Language Processing;

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 (est. 2020)


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