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Seyit Camtepe
Also published under:Seyit Ahmet Camtepe, Seyit A. Camtepe, S. A. Camtepe
Affiliation
Cybersecurity and Quantum Systems Group, CSIRO's Data61, Australia
Topic
Adversarial Attacks,Internet Of Things,Internet Of Things Devices,Support Vector Machine,Adversarial Examples,Adversarial Training,Benchmark Schemes,Convolutional Neural Network,Hidden Layer,Long Short-term Memory,Machine Learning,Non-orthogonal Multiple Access,Signal Reception,Time Slot,Active Devices,Adversarial Perturbations,Attack Success Rate,Authentication Scheme,Binary Hypothesis Testing,Black-box Attacks,Computational Cost,Conventional Scheme,Decision Boundary,Deep Models,F1 Score,False Alarm,False Alarm Rate,Intrusion Detection,Intrusion Detection System,Key Generation,Long Short-term Memory Network,Machine Learning Models,Malicious Activities,Monic Polynomial,Neural Network,Parity-check,Physical Channel,Probe Channel,Projected Gradient Descent,Public Key,Recurrent Neural Network,Resource Block,Training Phase,Training Set,Abnormal Samples,Access Points,Accuracy Scores,Activity Patterns,Adaptive Threshold,Advanced Persistent Threats,
Biography
Seyit Camtepe (Senior Member, IEEE) received the Ph.D. degree from Rensselaer Polytechnic Institute in 2007. He is currently a Principal Research Scientist and the Team Leader with CSIRO Data61. He was with Technische Universitaet Berlin as a Senior Researcher and QUT as a Lecturer. He was among the first to investigate the security of android smartphones and inform society for the rising malware threat. His research interests include autonomous security, malware detection and prevention, smartphone security, applied and malicious cryptography, and CII security.