Asaf Shabtai

Research interests

Publications

• The Translucent Patch: A Physical and Universal Attack on Object Detectors

  · 2021 · 10.1109/cvpr46437.2021.01498

• Efficient Cyber Attack Detection in Industrial Control Systems Using Lightweight Neural Networks and PCA

  IEEE Transactions on Dependable and Secure Computing · 2021 · https://doi.org/10.1109/tdsc.2021.3050101

• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain

  ACM Computing Surveys · 2021 · 10.1145/3453158

• SoK: Security and Privacy in the Age of Commercial Drones

  · 2021 · 10.1109/sp40001.2021.00005

• When Explainability Meets Adversarial Learning: Detecting Adversarial Examples using SHAP Signatures

  · 2020 · 10.1109/ijcnn48605.2020.9207637

• Detecting Cyber Attacks in Industrial Control Systems Using Convolutional Neural Networks

  Journal · 2018 · https://doi.org/10.1145/3264888.3264896

• Detection of malicious and low throughput data exfiltration over the DNS protocol

  Computers & Security · 2018 · 10.1016/j.cose.2018.09.006

• Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

  · 2018 · 10.14722/ndss.2018.23204

• Security Testbed for Internet-of-Things Devices

  IEEE Transactions on Reliability · 2018 · https://doi.org/10.1109/tr.2018.2864536

• Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

  Lecture notes in computer science · 2018 · https://doi.org/10.1007/978-3-030-00470-5_23

• Using LSTM encoder-decoder algorithm for detecting anomalous ADS-B messages

  Computers & Security · 2018 · 10.1016/j.cose.2018.07.004

• SIPHON

  · 2017 · 10.1145/3055186.3055192

• ProfilIoT

  Journal · 2017 · https://doi.org/10.1145/3019612.3019878

• Detection of Unauthorized IoT Devices Using Machine Learning Techniques

  arXiv (Cornell University) · 2017 · https://doi.org/10.48550/arxiv.1709.04647

• Fast-CBUS: A fast clustering-based undersampling method for addressing the class imbalance problem

  Neurocomputing · 2017 · 10.1016/j.neucom.2017.03.011

• Mobile malware detection through analysis of deviations in application network behavior

  Computers & Security · 2014 · https://doi.org/10.1016/j.cose.2014.02.009

• A Survey of Data Leakage Detection and Prevention Solutions

  SpringerBriefs in computer science · 2012 · https://doi.org/10.1007/978-1-4614-2053-8

• Detecting unknown malicious code by applying classification techniques on OpCode patterns

  Security Informatics · 2012 · https://doi.org/10.1186/2190-8532-1-1

• “Andromaly”: a behavioral malware detection framework for android devices

  Journal of Intelligent Information Systems · 2011 · https://doi.org/10.1007/s10844-010-0148-x

• Automated Static Code Analysis for Classifying Android Applications Using Machine Learning

  Journal · 2010 · https://doi.org/10.1109/cis.2010.77

• Google Android: A Comprehensive Security Assessment

  IEEE Security & Privacy · 2010 · https://doi.org/10.1109/msp.2010.2

• Intrusion detection for mobile devices using the knowledge-based, temporal abstraction method

  Journal of Systems and Software · 2010 · 10.1016/j.jss.2010.03.046

• Securing Android-Powered Mobile Devices Using SELinux

  IEEE Security & Privacy · 2009 · 10.1109/msp.2009.144

• Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

  Information Security Technical Report · 2009 · https://doi.org/10.1016/j.istr.2009.03.003

• Improving malware detection by applying multi-inducer ensemble

  Computational Statistics & Data Analysis · 2008 · 10.1016/j.csda.2008.10.015

Current projects

No projects listed.