Necmiye Ozay

239 papers A* 2B 1C 31Misc 3Journal 110Unranked 89
YearRankTypeTitle / Venue / Authors
2025 ed.
L4DC
Necmiye Ozay, Laura Balzano, Dimitra Panagou, Alessandro Abate
2025 conf
CDC
Mohamad Louai Shehab, Alperen Tercan, Necmiye Ozay
2025 J jnl
CoRR
Mohamad Louai Shehab, Alperen Tercan, Necmiye Ozay
2025 J jnl
ACM Trans. Cyber Phys. Syst.
Madhur Behl, Necmiye Ozay, Truong Nghiem
2025 conf
CDC
Alperen Tercan, Necmiye Ozay
2025 J jnl
CoRR
Alperen Tercan, Necmiye Ozay
2025 J jnl
IEEE Trans. Autom. Control.
Andrew Wintenberg, Necmiye Ozay, Stéphane Lafortune
2025 J jnl
CoRR
Amir Bayat, Alessandro Abate, Necmiye Ozay, Raphaël M. Jungers
2025 J jnl
CoRR
Mohamad Louai Shehab, Antoine Aspeel, Necmiye Ozay
2025 J jnl
Trans. Mach. Learn. Res.
Mohamad Louai Shehab, Antoine Aspeel, Necmiye Ozay
2025 J jnl
IEEE Control. Syst. Lett.
Ram Padmanabhan, Antoine Aspeel, Necmiye Ozay, Melkior Ornik
2025 C conf
ACC
Xiong Zeng, Laurent Bako, Necmiye Ozay
2025 J jnl
Autom.
Zexiang Liu, Necmiye Ozay, Eduardo D. Sontag
2025 J jnl
IEEE Trans. Autom. Control.
Zexiang Liu, Necmiye Ozay
2025 conf
CDC
Mo Yang, Jing Yu, Necmiye Ozay
2025 J jnl
CoRR
Mo Yang, Jing Yu, Necmiye Ozay
2025 J jnl
CoRR
Ruya Karagulle, Cristian-Ioan Vasile, Necmiye Ozay
2025 J jnl
CoRR
Xiong Zeng, Jing Yu, Necmiye Ozay
2025 J jnl
IEEE Control. Syst. Lett.
Xiong Zeng, Jing Yu, Necmiye Ozay
2025 conf
CDC
Mario Sznaier, Frank Allgöwer, Arthur Castello Branco de Oliveira, Necmiye Ozay, Eduardo D. Sontag
2024 J jnl
IEEE Robotics Autom. Lett.
Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan A. DeCastro, Necmiye Ozay
2024 conf
ADHS
Antoine Aspeel, Necmiye Ozay
2024 J jnl
CoRR
Antoine Aspeel, Necmiye Ozay
2024 J jnl
IEEE Trans. Control. Netw. Syst.
Sunho Jang, Necmiye Ozay, Johanna L. Mathieu
2024 J jnl
CoRR
Daphna Raz, Varun Joshi, Brian R. Umberger, Necmiye Ozay
2024 J jnl
IEEE Trans. Autom. Control.
Tzanis Anevlavis, Zexiang Liu, Necmiye Ozay, Paulo Tabuada
2024 conf
CDC
Sunho Jang, Johanna L. Mathieu, Necmiye Ozay
2024 conf
HSCC
Ruya Karagulle, Necmiye Ozay, Nikos Aréchiga, Jonathan A. DeCastro, Andrew Best
2024 conf
L4DC
Mohamad Louai Shehab, Antoine Aspeel, Nikos Aréchiga, Andrew Best, Necmiye Ozay
2024 conf
CDC
Antoine Aspeel, Laurent Bako, Necmiye Ozay
2024 J jnl
CoRR
Antoine Aspeel, Laurent Bako, Necmiye Ozay
2024 J jnl
CoRR
Xiong Zeng, Laurent Bako, Necmiye Ozay
2024 C conf
ACC
Mohamed Serry, Liren Yang, Necmiye Ozay, Jun Liu
2023 J jnl
CoRR
Antoine Aspeel, Jakob Nylöf, Jing Shuang Li, Necmiye Ozay
2023 J jnl
IEEE Control. Syst. Lett.
Antoine Aspeel, Jakob Nylöf, Jing Shuang Li, Necmiye Ozay
2023 J jnl
CoRR
Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan A. DeCastro, Necmiye Ozay
2023 J jnl
CoRR
Haldun Balim, Zhe Du, Samet Oymak, Necmiye Ozay
2023 J jnl
IEEE Control. Syst. Lett.
Zhe Du, Haldun Balim, Samet Oymak, Necmiye Ozay
2023 C conf
ACC
Kwesi Rutledge, Yuhang Mei, Necmiye Ozay
2023 conf
ECC
Daphna Raz, Liren Yang, Brian R. Umberger, Necmiye Ozay
2023 J jnl
CoRR
Sara Shoouri, Shayan Jalili, Jiahong Xu, Isabelle Gallagher, Yuhao Zhang, Joshua Wilhelm, Necmiye Ozay, Jean-Baptiste Jeannin
2023 J jnl
Autom.
Yunus Emre Sahin, Necmiye Ozay
2023 J jnl
IEEE Control. Syst. Lett.
Haldun Balim, Antoine Aspeel, Zexiang Liu, Necmiye Ozay
2023 J jnl
CoRR
Haldun Balim, Antoine Aspeel, Zexiang Liu, Necmiye Ozay
2023 conf
CDC
Xiong Zeng, Zexiang Liu, Zhe Du, Necmiye Ozay, Mario Sznaier
2023 J jnl
CoRR
Xiong Zeng, Zexiang Liu, Zhe Du, Necmiye Ozay, Mario Sznaier
2023 J jnl
IEEE Control. Syst. Lett.
Andrew Wintenberg, Stéphane Lafortune, Necmiye Ozay
2023 J jnl
IEEE Control. Syst. Lett.
Zexiang Liu, Hao Chen, Yulong Gao, Necmiye Ozay
2023 conf
HSCC
Daphna Raz, Liren Yang, Brian R. Umberger, Necmiye Ozay
2023 conf
HSCC
Ruya Karagulle, Nikos Aréchiga, Andrew Best, Jonathan A. DeCastro, Necmiye Ozay
2023 J jnl
CoRR
Sunho Jang, Necmiye Ozay, Johanna L. Mathieu
2023 J jnl
CoRR
Zexiang Liu, Necmiye Ozay, Eduardo D. Sontag
2023 J jnl
CoRR
Zexiang Liu, Necmiye Ozay
2022 conf
ICCPS
Andrew Wintenberg, Matthew Blischke, Stéphane Lafortune, Necmiye Ozay
2022 J jnl
Discret. Event Dyn. Syst.
Andrew Wintenberg, Matthew Blischke, Stéphane Lafortune, Necmiye Ozay
2022 J jnl
CoRR
Sunho Jang, Necmiye Ozay, Johanna L. Mathieu
2022 C conf
ACC
Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Samet Oymak, Laura Balzano, Necmiye Ozay
2022 conf
FORMATS
Ruya Karagulle, Nikos Aréchiga, Jonathan A. DeCastro, Necmiye Ozay
2022 conf
L4DC
Zhe Du, Necmiye Ozay, Laura Balzano
2022 C conf
ACC
Andrew Wintenberg, Stéphane Lafortune, Necmiye Ozay
2022 conf
HSCC
Kwesi Rutledge, Necmiye Ozay
2022 J jnl
IEEE Robotics Autom. Lett.
Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson
2022 J jnl
IEEE Trans. Control. Syst. Technol.
Yuxiao Chen, Necmiye Ozay
2022 C conf
ACC
Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak
2022 conf
Allerton
Sunho Jang, Necmiye Ozay, Johanna L. Mathieu
2022 J jnl
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.
Liren Yang, Hang Zhang, Jean-Baptiste Jeannin, Necmiye Ozay
2022 J jnl
CoRR
Liren Yang, Hang Zhang, Jean-Baptiste Jeannin, Necmiye Ozay
2022 conf
CDC
Yahya Sattar, Samet Oymak, Necmiye Ozay
2022 J jnl
CoRR
Yahya Sattar, Samet Oymak, Necmiye Ozay
2022 J jnl
Auton. Robots
Glen Chou, Necmiye Ozay, Dmitry Berenson
2022 J jnl
CoRR
Zhe Du, Laura Balzano, Necmiye Ozay
2022 conf
CDC
Zexiang Liu, Necmiye Ozay
2022 J jnl
CoRR
Zexiang Liu, Necmiye Ozay
2022 conf
ITSC
Dan Li, Mohamad Louai Shehab, Zexiang Liu, Nikos Aréchiga, Jonathan A. DeCastro, Necmiye Ozay
2022 J jnl
IEEE Trans. Autom. Control.
Samet Oymak, Necmiye Ozay
2022 C conf
WAFR
Glen Chou, Necmiye Ozay, Dmitry Berenson
2022 J jnl
CoRR
Glen Chou, Necmiye Ozay, Dmitry Berenson
2022 conf
CDC
Zhe Du, Zexiang Liu, Jack Weitze, Necmiye Ozay
2022 J jnl
IEEE Control. Syst. Lett.
Liren Yang, Necmiye Ozay
2021 ed.
ADHS
Raphaël M. Jungers, Necmiye Ozay, Alessandro Abate
2021 J jnl
CoRR
Andrew Wintenberg, Matthew Blischke, Stéphane Lafortune, Necmiye Ozay
2021 Misc conf
CISS
Necmiye Ozay
2021 C conf
ACC
Tzanis Anevlavis, Zexiang Liu, Necmiye Ozay, Paulo Tabuada
2021 conf
CDC
Zexiang Liu, Tzanis Anevlavis, Necmiye Ozay, Paulo Tabuada
2021 J jnl
CoRR
Zexiang Liu, Tzanis Anevlavis, Necmiye Ozay, Paulo Tabuada
2021 J jnl
CoRR
Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh, Laura Balzano, Samet Oymak, Necmiye Ozay
2021 conf
HSCC
Kwesi J. Rutledge, Glen Chou, Necmiye Ozay
2021 J jnl
CoRR
Tzanis Anevlavis, Zexiang Liu, Necmiye Ozay, Paulo Tabuada
2021 J jnl
Autom.
Sahar Mohajerani, Robi Malik, Andrew Wintenberg, Stéphane Lafortune, Necmiye Ozay
2021 conf
CDC
Andrew Wintenberg, Matthew Blischke, Stéphane Lafortune, Necmiye Ozay
2021 J jnl
CoRR
Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh, Laura Balzano, Necmiye Ozay, Samet Oymak
2021 C conf
WAFR
Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson
2021 C conf
ACC
Sunho Jang, Necmiye Ozay, Johanna L. Mathieu
2021 J jnl
Int. J. Robotics Res.
Glen Chou, Dmitry Berenson, Necmiye Ozay
2021 conf
CDC
Glen Chou, Necmiye Ozay, Dmitry Berenson
2021 J jnl
CoRR
Glen Chou, Necmiye Ozay, Dmitry Berenson
2021 C conf
ACC
Zexiang Liu, Necmiye Ozay
2021 J jnl
CoRR
Zexiang Liu, Necmiye Ozay
2021 J jnl
CoRR
Antoine Aspeel, Kwesi Rutledge, Raphaël M. Jungers, Benoît Macq, Necmiye Ozay
2021 J jnl
IEEE Robotics Autom. Lett.
Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson
2021 conf
ADHS
Zexiang Liu, Necmiye Ozay
2021 conf
ADHS
Liren Yang, Necmiye Ozay
2021 J jnl
CoRR
Liren Yang, Necmiye Ozay
2021 J jnl
CoRR
Liren Yang, Necmiye Ozay
2021 J jnl
ACM Trans. Embed. Comput. Syst.
Liren Yang, Necmiye Ozay
2021 conf
CCTA
Daphna Raz, Edgar A. Bolívar-Nieto, Necmiye Ozay, Robert D. Gregg
2021 conf
SEAMS@ICSE
Danny Weyns, Bradley R. Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei
2021 J jnl
CoRR
Danny Weyns, Bradley R. Schmerl, Masako Kishida, Alberto Leva, Marin Litoiu, Necmiye Ozay, Colin Paterson, Kenji Tei
2020 conf
ECC
Kwesi J. Rutledge, Necmiye Ozay
2020 J jnl
IEEE Trans. Autom. Control.
Petter Nilsson, Necmiye Ozay
2020 conf
CCTA
Michael Arwashan, Tiancheng Ge, Zexiang Liu, Necmiye Ozay
2020 conf
CDC
Liren Yang, Necmiye Ozay
2020 conf
Robotics: Science and Systems
Glen Chou, Necmiye Ozay, Dmitry Berenson
2020 J jnl
CoRR
Glen Chou, Necmiye Ozay, Dmitry Berenson
2020 J jnl
CoRR
Yunus Emre Sahin, Necmiye Ozay
2020 J jnl
IEEE Trans. Control. Syst. Technol.
Liren Yang, Amey Y. Karnik, Benjamin Pence, Md Tawhid Bin Waez, Necmiye Ozay
2020 conf
CDC
Andrew Wintenberg, Necmiye Ozay
2020 J jnl
CoRR
Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson
2020 conf
HSCC
Necmiye Ozay
2020 J jnl
IEEE Robotics Autom. Lett.
Glen Chou, Necmiye Ozay, Dmitry Berenson
2020 J jnl
CoRR
Glen Chou, Necmiye Ozay, Dmitry Berenson
2020 J jnl
IEEE Trans. Robotics
Yunus Emre Sahin, Petter Nilsson, Necmiye Ozay
2020 J jnl
CoRR
Rupak Majumdar, Necmiye Ozay, Anne-Kathrin Schmuck
2020 conf
HSCC
Rupak Majumdar, Necmiye Ozay, Anne-Kathrin Schmuck
2020 C conf
ACC
Liren Yang, Denise M. Rizzo, Matthew P. Castanier, Necmiye Ozay
2020 J jnl
Autom.
Xiangru Xu, Necmiye Ozay, Vijay Gupta
2020 J jnl
CoRR
Craig Knuth, Glen Chou, Necmiye Ozay, Dmitry Berenson
2020 C conf
ACC
Zexiang Liu, Liren Yang, Necmiye Ozay
2020 J jnl
CoRR
Zexiang Liu, Liren Yang, Necmiye Ozay
2020 Misc conf
CoRL
Glen Chou, Dmitry Berenson, Necmiye Ozay
2020 J jnl
CoRR
Glen Chou, Necmiye Ozay, Dmitry Berenson
2019 conf
HSCC
Liren Yang, Necmiye Ozay
2019 C conf
ACC
Liren Yang, Xiaofan Cui, Al-Thaddeus Avestruz, Necmiye Ozay
2019 J jnl
CoRR
Liren Yang, Xiaofan Cui, Al-Thaddeus Avestruz, Necmiye Ozay
2019 conf
HSCC
Kwesi J. Rutledge, Sze Zheng Yong, Necmiye Ozay
2019 conf
CDC
Liren Yang, Necmiye Ozay
2019 conf
CCTA
Yunus Emre Sahin, Zexiang Liu, Kwesi J. Rutledge, Dimitra Panagou, Sze Zheng Yong, Necmiye Ozay
2019 Misc conf
CoRL
Glen Chou, Necmiye Ozay, Dmitry Berenson
2019 J jnl
CoRR
Glen Chou, Necmiye Ozay, Dmitry Berenson
2019 C conf
ACC
Stephanie C. Ross, Petter Nilsson, Necmiye Ozay, Johanna L. Mathieu
2019 conf
CAMSAP
Zhe Du, Necmiye Ozay, Laura Balzano
2019 J jnl
CoRR
Zhe Du, Necmiye Ozay, Laura Balzano
2019 C conf
ACC
Samet Oymak, Necmiye Ozay
2019 J jnl
IEEE Trans. Autom. Control.
Liren Yang, Oscar Mickelin, Necmiye Ozay
2019 C conf
ACC
Kwesi J. Rutledge, Sze Zheng Yong, Necmiye Ozay
2019 ed.
HSCC
Necmiye Ozay, Pavithra Prabhakar
2019 conf
CCTA
Liren Yang, Amey Katnik, Necmiye Ozay
2019 conf
ECC
Vishnu S. Chipade, Qiang Shen, Lixing Huang, Necmiye Ozay, Sze Zheng Yong, Dimitra Panagou
2019 conf
CDC
Zexiang Liu, Necmiye Ozay
2019 J jnl
CoRR
Zexiang Liu, Necmiye Ozay
2019 conf
HSCC
Zexiang Liu, Necmiye Ozay
2019 conf
CDC
Liren Yang, Necmiye Ozay
2018 J jnl
CoRR
Zhe Du, Necmiye Ozay, Laura Balzano
2018 conf
CDC
Yuxiao Chen, Huei Peng, Jessy W. Grizzle, Necmiye Ozay
2018 C conf
ACC
Sze Zheng Yong, Necmiye Ozay
2018 conf
CDC
Liren Yang, Necmiye Ozay
2018 J jnl
Autom.
Farshad Harirchi, Necmiye Ozay
2018 conf
ADHS
Kanishka Raj Singh, Yuhao Ding, Necmiye Ozay, Sze Zheng Yong
2018 C conf
WAFR
Glen Chou, Dmitry Berenson, Necmiye Ozay
2018 J jnl
CoRR
Glen Chou, Dmitry Berenson, Necmiye Ozay
2018 conf
DARS
Yunus Emre Sahin, Necmiye Ozay, Stavros Tripakis
2018 J jnl
CoRR
Yunus Emre Sahin, Petter Nilsson, Necmiye Ozay
2018 J jnl
CoRR
Samet Oymak, Necmiye Ozay
2018 conf
ADHS
Oscar Lindvall Bulancea, Petter Nilsson, Necmiye Ozay
2018 J jnl
CoRR
Oscar Lindvall Bulancea, Petter Nilsson, Necmiye Ozay
2018 J jnl
CoRR
Liren Yang, Oscar Mickelin, Necmiye Ozay
2018 J jnl
IEEE Trans. Control. Syst. Technol.
Chaozhe R. He, Wubing B. Qin, Necmiye Ozay, Gábor Orosz
2018 conf
ICCPS
Yuhao Ding, Farshad Harirchi, Sze Zheng Yong, Emil Jacobsen, Necmiye Ozay
2018 conf
ADHS
Kwesi J. Rutledge, Sze Zheng Yong, Necmiye Ozay
2018 J jnl
CoRR
Xiangru Xu, Necmiye Ozay, Vijay Gupta
2018 conf
CDC
Supratim Ghosh, Mustafa Kara, Necmiye Ozay
2018 J jnl
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst.
Glen Chou, Yunus Emre Sahin, Liren Yang, Kwesi J. Rutledge, Petter Nilsson, Necmiye Ozay
2018 J jnl
CoRR
Glen Chou, Yunus Emre Sahin, Liren Yang, Kwesi J. Rutledge, Petter Nilsson, Necmiye Ozay
2017 J jnl
Discret. Event Dyn. Syst.
Petter Nilsson, Necmiye Ozay, Jun Liu
2017 J jnl
CoRR
Petter Nilsson, Necmiye Ozay
2017 C conf
ACC
Liren Yang, Amey Y. Karnik, Benjamin Pence, Md Tawhid Bin Waez, Necmiye Ozay
2017 conf
CDC
Farshad Harirchi, Sze Zheng Yong, Necmiye Ozay
2017 J jnl
Discret. Event Dyn. Syst.
Necmiye Ozay, Paulo Tabuada
2017 conf
CDC
Petter Nilsson, Necmiye Ozay
2017 conf
HSCC
Petter Nilsson, Necmiye Ozay
2017 J jnl
CoRR
Emil Jacobsen, Farshad Harirchi, Sze Zheng Yong, Necmiye Ozay
2017 conf
ICCPS
Yunus Emre Sahin, Petter Nilsson, Necmiye Ozay
2017 conf
CDC
Liren Yang, Necmiye Ozay
2017 conf
CDC
Yunus Emre Sahin, Petter Nilsson, Necmiye Ozay
2017 J jnl
CoRR
Sze Zheng Yong, Lingyun Gao, Necmiye Ozay
2017 C conf
ACC
Sze Zheng Yong, Lingyun Gao, Necmiye Ozay
2016 conf
Allerton
Andrew J. Wagenmaker, Necmiye Ozay
2016 C conf
ACC
Necmiye Ozay
2016 conf
HSCC
Petter Nilsson, Necmiye Ozay
2016 C conf
CCA
Ioannis Filippidis, Sumanth Dathathri, Scott C. Livingston, Necmiye Ozay, Richard M. Murray
2016 J jnl
IEEE Trans. Control. Syst. Technol.
Petter Nilsson, Omar Hussien, Ayca Balkan, Yuxiao Chen, Aaron D. Ames, Jessy W. Grizzle, Necmiye Ozay, Huei Peng, Paulo Tabuada
2016 conf
CDC
Stanley W. Smith, Petter Nilsson, Necmiye Ozay
2016 C conf
ACC
Farshad Harirchi, Zheng Luo, Necmiye Ozay
2016 J jnl
CoRR
Yunus Emre Sahin, Necmiye Ozay
2016 C conf
ACC
Liren Yang, Necmiye Ozay, Amey Y. Karnik
2016 C conf
ACC
Petter Nilsson, Necmiye Ozay
2016 J jnl
J. Aerosp. Inf. Syst.
Sweewarman Balachandran, Necmiye Ozay, Ella M. Atkins
2016 conf
ICCPS
Yunus Emre Sahin, Necmiye Ozay
2015 conf
ADHS
Yinan Li, Jun Liu, Necmiye Ozay
2015 J jnl
CoRR
Yinan Li, Jun Liu, Necmiye Ozay
2015 conf
ADHS
Farshad Harirchi, Necmiye Ozay
2015 J jnl
CoRR
Xiangru Xu, Necmiye Ozay, Vijay Gupta
2015 conf
CDC
Xiangru Xu, Necmiye Ozay, Vijay Gupta
2015 J jnl
Autom.
Necmiye Ozay, Constantino M. Lagoa, Mario Sznaier
2015 J jnl
CoRR
Petter Nilsson, Necmiye Ozay
2014 J jnl
IEEE Access
Pierluigi Nuzzo, Huan Xu, Necmiye Ozay, John B. Finn, Alberto L. Sangiovanni-Vincentelli, Richard M. Murray, Alexandre Donzé, Sanjit A. Seshia
2014 conf
HSCC
Jun Liu, Necmiye Ozay
2014 J jnl
J. Aerosp. Inf. Syst.
Robert Rogersten, Huan Xu, Necmiye Ozay, Ufuk Topcu, Richard M. Murray
2014 J jnl
IEEE Trans. Autom. Control.
Necmiye Ozay, Mario Sznaier, Constantino M. Lagoa
2014 C conf
ACC
Fei Sun, Necmiye Ozay, Eric M. Wolff, Jun Liu, Richard M. Murray
2014 conf
CDC
Petter Nilsson, Necmiye Ozay
2014 conf
CDC
Petter Nilsson, Omar Hussien, Yuxiao Chen, Ayca Balkan, Matthias Rungger, Aaron D. Ames, Jessy W. Grizzle, Necmiye Ozay, Huei Peng, Paulo Tabuada
2014 conf
CDC
Mario Sznaier, Octavia I. Camps, Necmiye Ozay, Constantino M. Lagoa
2014 C conf
ACC
Oscar Mickelin, Necmiye Ozay, Richard M. Murray
2013 conf
HSCC
Robert Rogersten, Huan Xu, Necmiye Ozay, Ufuk Topcu, Richard M. Murray
2013 C conf
ACC
Necmiye Ozay, Jun Liu, Pavithra Prabhakar, Richard M. Murray
2013 conf
CDC
Quentin Maillet, Huan Xu, Necmiye Ozay, Richard M. Murray
2013 J jnl
IEEE Trans. Autom. Control.
Jun Liu, Necmiye Ozay, Ufuk Topcu, Richard M. Murray
2012 J jnl
IEEE Trans. Autom. Control.
Necmiye Ozay, Mario Sznaier, Constantino M. Lagoa, Octavia I. Camps
2012 conf
CDC
Yongfang Cheng, Yin Wang, Mario Sznaier, Necmiye Ozay, Constantino M. Lagoa
2012 conf
HSCC
Ufuk Topcu, Necmiye Ozay, Jun Liu, Richard M. Murray
2012 conf
CDC
Jun Liu, Ufuk Topcu, Necmiye Ozay, Richard M. Murray
2012 C conf
ACC
Jun Liu, Necmiye Ozay, Ufuk Topcu, Richard M. Murray
2012 C conf
ACC
Petter Nilsson, Necmiye Ozay, Ufuk Topcu, Richard M. Murray
2011 conf
CDC/ECC
Mustafa Ayazoglu, Mario Sznaier, Necmiye Ozay
2011 conf
ICCPS
Necmiye Ozay, Ufuk Topcu, Richard M. Murray, Tichakorn Wongpiromsarn
2011 conf
CDC/ECC
Necmiye Ozay, Ufuk Topcu, Richard M. Murray
2011 conf
CDC/ECC
Necmiye Ozay, Mario Sznaier
2011 conf
HSCC
Tichakorn Wongpiromsarn, Ufuk Topcu, Necmiye Ozay, Huan Xu, Richard M. Murray
2010 A* conf
CVPR
Necmiye Ozay, Mario Sznaier, Constantino M. Lagoa, Octavia I. Camps
2010 conf
CDC
Chao Feng, Constantino M. Lagoa, Necmiye Ozay, Mario Sznaier
2010 B conf
ICPR
Sila Kurugol, Necmiye Ozay, Jennifer G. Dy, Gregory C. Sharp, Dana H. Brooks
2010 conf
CDC
Necmiye Ozay, Mario Sznaier, Constantino M. Lagoa
2009 conf
CVPR Workshops
Necmiye Ozay, Yan Tong, Frederick W. Wheeler, Xiaoming Liu
2009 conf
CDC
Necmiye Ozay, Constantino M. Lagoa, Mario Sznaier
2008 conf
CDC
Necmiye Ozay, Mario Sznaier, Constantino M. Lagoa, Octavia I. Camps
2008 A* conf
CVPR
Necmiye Ozay, Mario Sznaier, Octavia I. Camps
2007 conf
CDC
Necmiye Ozay, Mario Sznaier
2006 conf
ICPR (1)
Roberto Lublinerman, Necmiye Ozay, Dimitrios Zarpalas, Octavia I. Camps
docs/js_analysis.md
← Index docs/js_analysis.md markdown
# JavaScript Malware Analysis

REDB extracts features from JavaScript files using five dedicated extractors plus two shared extractors (IOCs and strings). Magika detects the file as `javascript`; the file must be listed in `SUPPORTED_FORMATS` in `.env` to be processed.

## Configuration

Add `javascript` to `SUPPORTED_FORMATS` in `.env`:

```
SUPPORTED_FORMATS=['pebin', 'elf', 'macho', 'apk', 'javascript']
```

| Variable | Default | Required | Description |
|----------|---------|----------|-------------|
| `SUPPORTED_FORMATS` | `['pebin']` | Yes | Must include `javascript` for JS files to be processed |
| `JS_DEOBFUSCATOR_PATH` | `webcrack` | No | Path or name of an external JS deobfuscator. If not installed, falls back to `jsbeautifier` (Python library, always available) |
| `JS_DEOBFUSCATE_TIMEOUT` | `60` | No | Timeout in seconds for the external deobfuscator subprocess |
| `JS_XRAY_RUNNER_PATH` | bundled `redb/extractors/js_extractors/scripts/js-xray-runner.js` | No | Node bridge that runs `@nodesecure/js-x-ray` and emits JSON. Falls back to heuristic-only when the bridge or its `node_modules` are missing |
| `JS_XRAY_NODE_BIN` | `node` | No | Node binary to invoke the bridge with |
| `JS_XRAY_TIMEOUT` | `30` | No | Timeout in seconds for the js-x-ray subprocess |

### Python dependencies

Installed via `requirements.txt`:
- `jsbeautifier` — code normalization and fallback deobfuscation
- `chardet` — source encoding detection
- `pyjsparser` — ES5.1 AST parser. The obfuscation heuristic's `avg_identifier_length<2` strong signal depends on AST identifier walking, so without pyjsparser the JS pipeline runs in a degraded "regex-only" mode that misses a key obfuscator.io tell. Listed as required, not optional.

### External Node tools

The Docker image bundles everything below; host CLI installs need to be done once.

- **webcrack** — reverses webpack bundling, obfuscator.io output, and common packing patterns. Significantly better than jsbeautifier for real-world obfuscated malware. Pinned to **2.16.0** in the `Dockerfile` and installed globally inside the container; on the host run `npm install -g webcrack@2.16.0` (or set `JS_DEOBFUSCATOR_PATH` to a non-default path).
- **@nodesecure/js-x-ray** — static AST analyser used by the NodeSecure project (and npm's package scanning) that recognises specific obfuscator families (`jsfuck`, `obfuscator.io`, `morse`, `jjencode`, `freejsobfuscator`, ...) and emits structured warnings. We invoke it via the bundled Node bridge at `redb/extractors/js_extractors/scripts/js-xray-runner.js`. The Dockerfile runs `npm install --omit=dev` in that directory at build time; on the host run the same once: `cd redb/extractors/js_extractors/scripts && npm install --omit=dev`. When `node_modules/@nodesecure/js-x-ray` is absent, the Python wrapper short-circuits without forking a subprocess and the pipeline falls back to heuristic-only obfuscation detection (no error, just a `debug` log line). A local patch (see *Patches* below) is auto-applied by `patch-package` during `npm install` to fix a Node 22 compatibility regression.

#### Patches

`redb/extractors/js_extractors/scripts/patches/` holds local patches applied to `node_modules/` after every `npm install` via the `postinstall: patch-package` hook in `package.json`. There's currently one:

| File | Upstream | What it fixes |
|---|---|---|
| `@nodesecure+js-x-ray+7.3.0.patch` | [@nodesecure/js-x-ray#???](https://github.com/NodeSecure/js-x-ray) | Changes `import { builtinModules } from "repl"` to `from "module"` in `src/probes/isLiteral.js`. `repl.builtinModules` was a deprecated re-export that Node 22.x stopped exposing as a named ESM export somewhere between 22.10 and 22.22; `module.builtinModules` is the canonical location and works on every Node ≥9.3. Without the patch, importing js-x-ray throws `SyntaxError: The requested module 'repl' does not provide an export named 'builtinModules'` and the bridge falls back to heuristic-only. |

Patches apply automatically — no manual step required. They're regenerated with `npx patch-package <package-name>` after editing the file in `node_modules/`. Drop a patch by deleting its file in `patches/` once upstream ships a fix.

### Host CLI vs Docker

| | Host CLI | Docker (SaaS) |
|---|---|---|
| Python deps | `pip install -r requirements.txt` | done at image build |
| Node 22 LTS | install once on host (see below) | bundled in image |
| webcrack | `sudo npm install -g webcrack@2.16.0`* | bundled in image |
| @nodesecure/js-x-ray | `cd redb/extractors/js_extractors/scripts && npm install --omit=dev` | bundled in image |

\* Global `npm install -g` writes into `/usr/lib/node_modules/` on a system-installed Node (apt / NodeSource), which is root-owned — so `sudo` is required. Skip the `sudo` if you installed Node via `nvm` or a user-owned prefix. The js-x-ray install is local to the repo so it does *not* need root; running it under `sudo` only makes `node_modules/` root-owned (harmless, the Python wrapper only reads, but tidier without).

Both paths produce the same fully-equipped pipeline. The Docker image is self-contained — unlike Binary Ninja (which is mounted from the host because of size and licensing), the JS Node tools are small enough to bundle.

#### Installing Node 22 LTS on the host

Pick whichever matches your OS — all paths land you on `node --version` reporting `v22.x`.

**macOS (Homebrew).** Most REDB developers run macOS; `brew` is the path of least resistance:

```bash
brew install node@22
brew link --overwrite node@22
node --version  # v22.x
```

**Linux (Debian / Ubuntu via NodeSource).** Same recipe the Dockerfile uses, so behaviour matches the container exactly:

```bash
curl -fsSL https://deb.nodesource.com/setup_22.x | sudo -E bash -
sudo apt-get install -y nodejs
node --version
```

**Linux/macOS via `nvm` (multiple Node versions on one host).** Useful if other projects on the same machine want different majors:

```bash
curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.40.1/install.sh | bash
nvm install 22 --lts
nvm use 22
```

After Node is in place, run the two `npm install` commands from the table above. Verify the toolchain with these commands (run them from the repo root — adjust the path if your repo lives elsewhere):

```bash
# webcrack on PATH (global install)
which webcrack && webcrack --version                  # 2.16.0

# js-x-ray installed locally next to the bridge
ls -d redb/extractors/js_extractors/scripts/node_modules/@nodesecure/js-x-ray

# end-to-end smoke test — should print one line of JSON
node redb/extractors/js_extractors/scripts/js-xray-runner.js test_files/test_malicious.js
```

If either of the first two checks fails, the JS pipeline still runs — webcrack falls back to `jsbeautifier` and js-x-ray short-circuits to heuristic-only obfuscation detection — but you lose the obfuscator-family identification and most semantic deobfuscation. The Python side never raises on a missing tool; it logs at `debug` and moves on.

---

## Pipeline architecture

For every JS sample, `workers.py` builds **one `JSContext`** (`redb/extractors/js_extractors/js_context.py`) and threads it into every JS extractor that runs. The context owns all per-sample shared state:

| `JSContext` field | Computed | Consumed by |
|---|---|---|
| `raw_bytes` | Single `open(...).read()` at construction | `BasicPropertiesExtractor`/`HashExtractor` go through their own paths; `self.binary` on each JS extractor delegates here |
| `source` | Decoded once at construction (BOM → UTF-8 → chardet → latin-1 fallback) | `self.js_source` on every JS extractor |
| `lines` | `source.splitlines()`, cached on first access | `self.lines` on every JS extractor |
| `text_entropy` | Shannon entropy over `source`, cached | `JSFeaturesExtractor` (stored as `text_entropy` column), `JSDeobfuscationExtractor` (`original_entropy`) |
| `scan` | One `scan_source()` pass producing `{pattern_name: {count, lines}}` for every regex in `js_patterns.PATTERNS` and `js_patterns.FEATURE_PATTERNS`, cached | `JSFeaturesExtractor` (per-pattern counts + obfuscation score + technique detection), `JSSuspiciousAPIsExtractor` (every finding), `JSDeobfuscationExtractor` (original-side `new_apis_found` set) |
| `ast` | `pyjsparser.parse(source)` lazily on first access, returns `None` if pyjsparser is absent or parsing fails | `JSFeaturesExtractor` for `total_function_count` / `total_variable_count` / `max_nesting_depth` / `avg_identifier_length` |
| `deobfuscated` | External JS deobfuscator (default `webcrack`) with `jsbeautifier` fallback, run lazily once per sample. Returns `(text, normalizer_used)` or `(None, None)` when neither produced output | `JSDeobfuscationExtractor` (metrics row), `JSContentExtractor` (persisted text) — both read the same cached value, so the subprocess runs at most once |
| `xray` | `@nodesecure/js-x-ray` invoked via the bundled Node bridge, run lazily once per sample. Returns `XRayResult(obfuscator, warnings)`; empty when the bridge or its `node_modules` are missing, when Node is absent, or when the subprocess errors out | `JSFeaturesExtractor` reads `obfuscator` for the `obfuscator_name` column and uses it as the authoritative signal in the obfuscation verdict |
| `content_type` | The magika label workers.py dispatched on (`"javascript"`), carried through so `JSContentExtractor` can record it without re-running magika | `JSContentExtractor` |

The shape eliminates the per-extractor disk reads, source decodes, scan passes, AST parses, deobfuscation runs, and entropy computations the pipeline used to do independently for each extractor instance.

### Shared regex catalogue

All compiled regexes live in `redb/extractors/js_extractors/js_patterns.py`:

- `PATTERNS` — 46 named entries that double as suspicious-API row labels and as count sources for the features extractor (the 8 patterns shared across both extractors are defined exactly once here).
- `CATEGORIES` — pattern name → category (`code_execution` / `network` / `filesystem` / `process` / `registry` / `crypto_encoding` / `dom_manipulation`).
- `FEATURE_PATTERNS` — 11 additional regexes used only by `JSFeaturesExtractor` (hex/unicode escapes, base64 strings, comments, string concatenation, etc.).
- `STRING_PATTERNS` — 6 regexes used only by `JSStringsExtractor` for encoded-string discovery (`hex_escape_seq`, `unicode_escape_seq`, `charcode_call`, `base64_quoted`, `long_quoted`, `concat_chain`). Distinct from the look-alike entries in `FEATURE_PATTERNS` (e.g. `STRING_PATTERNS["hex_escape_seq"]` matches 4+ consecutive `\xHH` while `FEATURE_PATTERNS["hex_escape"]` matches a single one). Not folded into `JSContext.scan` because the strings extractor needs the match objects (capture groups, raw text) and is the sole consumer.
- `scan_source(source, patterns=...)` — runs every compiled pattern against `source` once, with O(log N) line lookup via a precomputed line-offset table, and returns `{name: {"count": int, "lines": [unique_sorted]}}` for any pattern that matched.

All `PATTERNS` are compiled with `re.IGNORECASE`. JS is case-sensitive at runtime, but the patterns themselves match literal-case identifiers (`eval`, `atob`, `WScript.Shell`, etc.) that real-world JS spells exactly as written, so IGNORECASE produces no extra matches in normal code while making the catalogue easier to share. Two pinned tests (`test_pattern_match_is_case_insensitive`, `test_pattern_counts_are_case_insensitive`) guard against an accidental flag regression.

---

## JSFeaturesExtractor

**Table:** `redb_js_features` (1 row per sample)

Extracts structural metadata and obfuscation indicators from JavaScript source code. No external tools required — pure regex (via the shared `JSContext.scan`) and optional AST parsing.

### Fields

File size and byte-level entropy are not stored here — they are written by `BasicPropertiesExtractor` (`redb_basic_properties.filesize`, `redb_basic_properties.file_entropy`) and joinable on `sha256`. Character-level entropy is stored separately as `text_entropy` because it differs meaningfully from byte entropy on non-ASCII sources (e.g. UTF-16 inflates byte counts and depresses byte entropy).

| Field | How it is extracted |
|-------|-------------------|
| `line_count` | `source.splitlines()` count |
| `char_count` | Length of decoded text (distinct from `filesize` for non-ASCII sources) |
| `text_entropy` | Shannon entropy over the character distribution of the decoded source text. Distinct from `redb_basic_properties.file_entropy`, which is over raw bytes. Obfuscated/packed JS typically scores above 5.0; clean code is usually 4.04.8. The obfuscation-score thresholds are tuned on this value |
| `max_line_length` | Longest line in characters. Values above 5–10K suggest minification or single-line obfuscation |
| `avg_line_length` | Mean line length across all lines |
| `is_minified` | True when the file has fewer than 5 lines but more than 500 characters, or when `avg_line_length` exceeds 500. These thresholds come from observing webpack/uglify output vs hand-written code |
| `is_likely_obfuscated` | True when `@nodesecure/js-x-ray` recognised the obfuscator family, OR when the heuristic score reaches 60 *and* at least one strong signal fired (encoding density >5%, single line >10K chars, avg identifier length <2, or text entropy >5.0). The two-tier check stops mid-band entropy + single eval + handful of `\xHH` escapes from masquerading as a verdict — the failure mode of the original score-only threshold |
| `obfuscator_name` | Family name reported by js-x-ray (`jsfuck`, `obfuscator.io`, `morse`, `jjencode`, `freejsobfuscator`, ...) or empty when js-x-ray didn't flag the sample / wasn't installed. When this is non-empty, `is_likely_obfuscated` is forced True regardless of the heuristic |
| `obfuscation_score` | Weighted heuristic score 0100 (see section below). Kept as the explainability layer even when the verdict comes from js-x-ray |
| `obfuscation_techniques` | Array of detected technique labels (see section below) |
| `eval_count` | Regex `\beval\s*\(` — direct eval calls, the most common JS code execution vector |
| `function_constructor_count` | Regex `\bnew\s+Function\s*\(` — `new Function("code")` is equivalent to eval but harder to grep for |
| `settimeout_setinterval_count` | Regex `\b(setTimeout\|setInterval)\s*\(` — when called with a string argument these execute code after a delay, commonly used to evade sandbox timeouts |
| `document_write_count` | Regex `\bdocument\.write(ln)?\s*\(` — injects HTML/script into the page, used by exploit kits |
| `innerhtml_count` | Regex `\.innerHTML\s*=` — DOM injection, common in XSS and skimmers |
| `unescape_count` | Regex `\bunescape\s*\(` — deprecated decoding function, almost exclusively found in malware |
| `fromcharcode_count` | Regex `String\.fromCharCode\s*\(` — converts integer arrays to strings, used to hide payloads from static string matching |
| `atob_count` | Regex `\batob\s*\(` — base64 decode, commonly wraps encoded payloads |
| `decodeuri_count` | Regex `\b(decodeURI\|decodeURIComponent)\s*\(` — URL decoding used to unpack percent-encoded payloads |
| `total_function_count` | AST: counts `FunctionDeclaration`, `FunctionExpression`, `ArrowFunctionExpression` nodes. Falls back to regex `\bfunction\s+\w+\s*\(\|\bfunction\s*\(` when pyjsparser is not installed |
| `total_variable_count` | AST: counts declarations inside `VariableDeclaration` nodes. Regex fallback: `\b(var\|let\|const)\s+` |
| `max_nesting_depth` | AST: tracks depth through `BlockStatement` and function nodes. 0 when AST is unavailable. Deep nesting (>5) correlates with obfuscation wrappers |
| `avg_identifier_length` | AST: mean character length of all `Identifier` node names. Obfuscators like javascript-obfuscator produce 12 character names (`_0x4a2f`, `a`, `b`); clean code averages 610. Computed by pyjsparser when the source is ES5.1; on ES2015+ sources (destructuring, classes, optional chaining, etc.) pyjsparser fails parse and the value falls back to `idsLengthAvg` from `@nodesecure/js-x-ray`, which uses a modern parser. Equals `0.0` only when both paths are unavailable |
| `hex_string_count` | Count of `\xHH` escape sequences via regex `\\x[0-9a-fA-F]{2}`. High counts indicate hex-encoded string literals |
| `unicode_escape_count` | Count of `\uHHHH` escape sequences. Same reasoning as hex — used to hide readable strings |
| `long_string_count` | String literals longer than 256 chars inside quotes. Long strings often contain encoded payloads |
| `base64_string_count` | Sequences of 40+ base64 characters. Matches `[A-Za-z0-9+/]{40,}={0,2}` |
| `comment_ratio` | Ratio of characters inside `//` and `/* */` comments to total characters. Obfuscated code rarely has comments; a ratio near 0 combined with large file size is suspicious |
| `script_type` | First-match file-format classification (see *Script type values* below). Distinct from `detected_environment`, which classifies the runtime API surface — an HTA, for example, is `script_type=hta` *and* `detected_environment=wscript` |
| `detected_environment` | First-match runtime classification by API presence (see *Environment values* below) |

#### Script type values

Checked in this order; first match wins. The ordering encodes specificity — encoded JScript can only be `jse`, a WSF wrapper can only be `wsf`, etc.

| Value | Trigger |
|-------|---------|
| `jse` | Source starts with `#@~^` (JScript.Encode marker). Body is unanalysable until decoded |
| `wsf` | First 4KB contains `<job`/`<package` *and* `<script` — Windows Script File XML wrapper |
| `hta` | First 4KB contains `<hta:application` or the `application/hta` MIME hint — runs under mshta.exe |
| `embedded_html` | Starts with `<!`/`<html` or contains `<script` in first 2000 chars (generic HTML host) |
| `wscript` | Contains `WScript.` or `WSH.` (loose `.js` invoked via `wscript.exe` / `cscript.exe`) |
| `esm` | Line-anchored `import …from "…"` / bare side-effect `import "…"` / top-level `export …` |
| `node_module` | Contains `require(` or `module.exports` (CommonJS) |
| `standalone` | Fallback when nothing above matches |
| `unknown` | Empty source |

#### Environment values

Checked in this order; first match wins.

| Value | Trigger |
|-------|---------|
| `wscript` | `WScript.`, `WSH.`, `ActiveXObject`, `Scripting.FileSystemObject`, `WScript.Shell`, `ADODB.Stream` |
| `browser_extension` | `chrome.runtime`, `chrome.tabs`, `chrome.storage`, `chrome.webRequest`, `browser.runtime`, `browser.tabs` (MV2/MV3 extension APIs) |
| `service_worker` | `self.addEventListener('fetch'`, `self.importScripts`, `self.skipWaiting`, `caches.match`, `caches.open` (worker-only APIs not present in regular pages) |
| `deno` | `Deno.` (Deno runtime global) |
| `node` | `require(`, `module.exports`, `process.env`, `__dirname`, `__filename`, `Buffer.`, `child_process` |
| `browser` | `document.`, `window.`, `navigator.`, `localStorage`, `sessionStorage`, `XMLHttpRequest`, `addEventListener` |
| `unknown` | Fallback |

### Two-tier obfuscation verdict

The `is_likely_obfuscated` boolean is the answer to "should an analyst treat this file as obfuscated." It comes from two sources, in priority order:

1. **js-x-ray hit (authoritative).** When `@nodesecure/js-x-ray` recognises the obfuscator family, the verdict is `True` and `obfuscator_name` carries the family label. js-x-ray catches `jsfuck`, `obfuscator.io`, `morse`, `jjencode`, and `freejsobfuscator` by AST shape, which is far more precise than any heuristic.
2. **Heuristic with strong-signal corroboration.** When js-x-ray either didn't flag the sample or isn't installed, the heuristic decides: `obfuscation_score >= 40` AND at least one *strong* signal fired. Strong signals are unambiguous on their own; weak signals are commonly seen in legitimate code and only count toward the score, not toward the strong-signal gate. The strong-signal gate (not the score threshold) is what does the heavy lifting against false positives — a clean file with multiple weak ticks but no strong signal cannot be flagged regardless of the score.

The two-tier check is a deliberate response to the score-only threshold's failure mode: a non-obfuscated file with mid-band entropy, a single `eval`, and a handful of `\xHH` escapes used to clear `>= 40` and show up as `is_obfuscated: Yes` even though it was just legitimate code with one or two ambient indicators. With strong-signal corroboration, three weak ticks alone no longer cross the line.

### Obfuscation score breakdown

The score is a sum of weighted indicators, capped at 100:

| Indicator | Tier | Weight | Rationale |
|-----------|------|--------|-----------|
| Hex/unicode escape density > 5% of source | strong | +20 | Encoded payload — at this density the source is mostly escape sequences |
| Hex/unicode escape density > 1% | weak | +8 | Notable encoding but could also be a few hex literals in legitimate code |
| Avg identifier length < 2 chars | strong | +15 | Obfuscators shorten everything to single chars; clean code averages 6+ |
| Avg identifier length < 3 chars | weak | +6 | Slightly longer but still suspicious |
| Max line > 10K chars | strong | +15 | Single enormous line — hallmark of packer output |
| Max line > 5K chars | weak | +8 | Long single line |
| `text_entropy` > 5.0 | strong | +15 | Encoded payload range. The old 4.54.8 weak band caught jQuery and is dropped |
| Each `eval()` call (capped at +12) | weak | +4 each | One eval is normal in templating / AngularJS / polyfills; only piles of them count |
| `String.fromCharCode` present | weak | +6 | Common in legacy escapers but worth a tick |
| String concat density > 20 per 100 lines | weak | +8 | Excessive `"a" + "b" + "c"` rebuild of greppable strings |
| Comment ratio < 1% + few lines + size > 1 KB | weak | +5 | Minifier/packer tell |
| Non-ASCII codepoint density > 30% | strong | +20 | Unicode-codepoint payload (e.g. WSH droppers building a runtime string of >0x7f chars). Real-world JS averages <5% non-ASCII; >30% is almost always obfuscation. The strong-signal gate prevents the corner-case false-positive on heavy-localization files (which can cross 30% legitimately) — a localization file scoring only this signal can't reach the threshold |
| Non-ASCII codepoint density > 10% | weak | +8 | Notable non-ASCII presence — could be substantial i18n in legitimate code, or the start of a Unicode-codepoint obfuscation pattern |
| Line-uniqueness ratio < 10% (line_count > 100) | strong | +15 | Junk-padded bulk: thousands of duplicate lines burying the actual logic. Hand-written code has near-1 uniqueness even in repetitive sections (CSS-in-JS, fixture data, etc.) |
| Line-uniqueness ratio < 30% (line_count > 100) | weak | +6 | Significant repetition; could be a packer working from a small template, or padding warming up |

### Obfuscation techniques detected

Each technique is flagged when its threshold is exceeded. Density-based tags use the same bar as the score's strong-signal threshold so the displayed tags reflect what the score actually credited:

| Technique label | Detection rule |
|----------------|---------------|
| `eval_usage` | `eval(` present |
| `function_constructor` | Function-constructor invocation present (`new Function(...)`) |
| `hex_encoding` | More than 5 `\xHH` sequences AND density > 0.1% of source |
| `unicode_encoding` | More than 5 `\uHHHH` sequences AND density > 0.1% of source |
| `charcode_encoding` | More than 3 `String.fromCharCode(` calls |
| `string_concatenation` | More than 10 `"..." + "..."` patterns |
| `base64_decoding` | `atob(` present |
| `unescape_usage` | `unescape(` present |
| `array_function_calls` | Pattern `[0xNN](` or `[N](` — calling functions via array index lookup, typical of javascript-obfuscator output |
| `short_identifiers` | `0 < avg_identifier_length < 3.0` — identifiers averaging under 3 chars, typical of obfuscator.io's `_0xNNNN` renaming. Sourced from pyjsparser when the file parses as ES5.1, falling back to js-x-ray's `idsLengthAvg` on ES2015+ sources |
| `packed_single_line` | `max_line_length > 5000` — single enormous line, hallmark of packer/minifier output |
| `high_entropy` | `text_entropy > 5.0` — character distribution in encoded-payload range; distinct from `redb_basic_properties.file_entropy` (byte entropy) |
| `non_ascii_payload` | Non-ASCII codepoint density > 10%. Catches Unicode-codepoint stuffing (e.g. `this.x += "<U+1184><U+159b>..."` repeated thousands of times) — a pattern the per-escape `unicode_encoding` tag misses because the source contains the actual codepoints, not literal `\uHHHH` escape sequences |
| `repetitive_padding` | Line-uniqueness ratio < 30% with line_count > 100. Junk-filled bulk burying the actual payload; the line-count floor prevents false positives on tiny files that happen to repeat a few lines |

---

## JSSuspiciousAPIsExtractor

**Table:** `redb_js_suspicious_apis` (multi-row per sample, one row per detected API)

Reads `JSContext.scan` and emits one row per `js_patterns.PATTERNS` entry that matched the source. Findings are emitted in the canonical insertion order of `PATTERNS` (`code_execution` → `network` → `filesystem` → `process` → `registry` → `crypto_encoding` → `dom_manipulation`) so output ordering is deterministic. Each pattern matches a specific API call or object instantiation known to be used in malicious JavaScript.

### Categories and patterns

**code_execution** — APIs that execute arbitrary code:
`eval()`, `new Function()`, `execScript()`, `document.write()`, `.innerHTML =`, `.outerHTML =`, `.insertAdjacentHTML()`

**network** — APIs that make network requests:
`new XMLHttpRequest`, `fetch()`, `new WebSocket()`, `navigator.sendBeacon()`, `ActiveXObject("MSXML2.XMLHTTP")`, `require("http"/"https"/"net"/"dgram")`, `axios`

**filesystem** — APIs that access the filesystem:
`require("fs")`, `require("path")`, `Scripting.FileSystemObject`, `ADODB.Stream`, `Shell.Application`, `WScript.CreateObject`

**process** — APIs that spawn processes:
`require("child_process")`, `child_process.exec/spawn/execFile/fork`, `WScript.Shell`, `.Run()`, `.Exec()`, `ShellExecute`, `"powershell"`, `"cmd.exe"`, `require("os")`

**registry** — Windows registry access:
`.RegRead()`, `.RegWrite()`, `.RegDelete()`, `StdRegProv`

**crypto_encoding** — Encoding/decoding/crypto operations:
`atob()`, `btoa()`, `String.fromCharCode()`, `unescape()`, `decodeURIComponent()`, `Buffer.from()`, `crypto.createCipher/Decipher/Hash/Hmac`

**dom_manipulation** — DOM operations typical of skimmers/injectors:
`document.forms`, `document.cookie`, `querySelector` targeting password/credit/card/cvv/ssn inputs, `addEventListener("submit")`, `createElement("script"/"iframe")`, `.src = "http://..."`

### Output fields

| Field | Description |
|-------|-------------|
| `api_name` | Human-readable name of the matched API |
| `api_category` | One of the 7 categories above |
| `call_count` | Number of lines where the pattern matched |
| `line_numbers` | Array of line numbers (1-indexed) where the API was found |
| `context_snippet` | Up to 3 truncated source lines where the API appears (max 200 chars each, joined by ` \| `) |

---

## JSStringsExtractor

**Table:** `code_binja_strings_raw` (shared with binary string extraction)

Finds encoded strings in JS source, decodes them, and writes both the decoded value and the original encoded form to the same table used by DecompileBinja and DecompileAPK. This means `string:"powershell"` queries return results from all formats.

The 6 detection regexes live in `js_patterns.STRING_PATTERNS` (compiled once at module load); the per-match concat tokeniser is also compiled once. Line numbers for each finding (`string_offset`) are looked up in O(log L) via `bisect` against a newline-offset table built once per `extract()` call — the historical O(N·M) `source[:start].count('\n')` pass is gone.

### Decoding methods

Scope: only *hidden* strings — values whose decoded form is not visible to a substring search over the raw text. Plain long literals are not extracted here because they're already preserved in `code_text_content.text_raw` and scraped by the IOC pipeline over the same `text_raw` / `text_normalized` surfaces (column names match the `redb_iocs.source_type` enum values, so a join across the two tables doesn't have to translate names).

| `string_encoding` value | What it decodes | Example input | Example output |
|------------------------|----------------|---------------|----------------|
| `hex` | `\xHH` escape sequences (4+ consecutive) | `\x68\x74\x74\x70` | `http` |
| `unicode` | `\uHHHH` escape sequences (3+ consecutive) | `WScript` | `WScript` |
| `charcode` | `String.fromCharCode(N, N, ...)` calls | `String.fromCharCode(112, 111, 119)` | `pow` |
| `base64` | Base64 strings (40+ chars) inside quotes. Only kept if decoding produces >80% printable UTF-8 text | `"cG93ZXJzaGVsbA=="` | `powershell` |
| `concat` | Reassembled `"a" + "b" + "c"` concatenation (3+ parts) | `"ht" + "tp" + "://" + "evil" + ".com"` | `http://evil.com` |

### Field mapping to shared table

| Shared column | JS value |
|--------------|----------|
| `string` | Decoded/reconstructed string value |
| `string_raw` | Original encoded form as it appeared in source |
| `string_encoding` | One of: hex, unicode, charcode, base64, concat, plaintext |
| `string_offset` | Line number in the JS source file (1-indexed) |
| `string_length` | Length of the decoded string |
| `string_raw_length` | Length of the original encoded form |
| `string_entropy` | Shannon entropy of the decoded string |

---

## JSDeobfuscationExtractor

**Table:** `redb_js_deobfuscation` (1 row per sample)

Attempts to deobfuscate the JS source using external tools, then compares pre/post metrics to measure how much was hidden.

### Tool chain

1. **Primary: webcrack** (or any tool at `JS_DEOBFUSCATOR_PATH` env var). Run as a subprocess with `JS_DEOBFUSCATE_TIMEOUT` seconds timeout (default 60). The tool receives the source file path and its stdout is captured as the deobfuscated output. Process group management handles cleanup on timeout (SIGTERM then SIGKILL).

2. **Fallback: jsbeautifier** (Python library). Used when the primary tool is not installed. Normalizes formatting (indentation, line breaks) but does not perform semantic deobfuscation. Still useful because it makes minified code readable and can reveal strings that were hidden by formatting tricks.

### Output fields

| Field | Description |
|-------|-------------|
| `deobfuscator_used` | Name of the tool that produced the output (`webcrack`, `jsbeautifier`, etc.) |
| `deobfuscation_successful` | 1 if the tool produced non-empty output |
| `original_size` | Character count of the input source |
| `deobfuscated_size` | Character count of the deobfuscated output |
| `size_change_ratio` | `deobfuscated_size / original_size`. Values significantly different from 1.0 indicate the tool transformed the code |
| `original_entropy` | Shannon entropy of the input. Reused from `JSContext.text_entropy` so the same Shannon computation is not redone here |
| `deobfuscated_entropy` | Shannon entropy of the output. A drop in entropy after deobfuscation suggests encoded content was unpacked into readable text |
| `new_strings_found` | Count of string literals (4+ chars) present in the deobfuscated output but absent in the original. These are strings that were hidden by the obfuscation |
| `new_apis_found` | Count of `PATTERNS` entries that matched the deobfuscated output but did not match the original. The original-side pattern set is read from `JSContext.scan` (already computed once for this sample); only the deobfuscated text triggers an additional `scan_source()` pass since that text is unique to this extractor. Reveals API calls that were concealed |
| `deobfuscated_sha256` | SHA-256 of the deobfuscated output, for deduplication and cross-referencing |

---

## JSContentExtractor

**Table:** `code_text_content` (1 row per sample, shared with future text-content extractors)

Persists the actual text of the sample (raw + normalised) so analysts can re-query the source content directly and so future improvements to IOC extraction or pattern matching can be re-applied without re-running the deobfuscator. The same table is intended to host any text-based artefact in the future (PowerShell, Python, plain text, email bodies, extracted PDF/Office text); the `content_type` column carries the magika label so callers can filter without joining other tables.

The deobfuscation pass is computed once per sample and shared with `JSDeobfuscationExtractor` (which writes the metrics row), so this extractor adds no extra subprocess cost.

| Field | Description |
|-------|-------------|
| `content_type` | The magika label for the artefact (`"javascript"` for JS samples). Lets a single table hold heterogeneous text content without per-format tables |
| `text_raw` | The decoded source as it sits on disk. Column name matches the `redb_iocs.source_type='text_raw'` enum value, so an analyst tracing an IOC back to its surface lands on the column with the same identifier |
| `text_normalized` | Output of the deobfuscator (or jsbeautifier fallback). `NULL` when neither produced output, distinguishing "we tried and got nothing" from a successful normalisation. Same naming alignment with `redb_iocs.source_type='text_normalized'` |
| `normalizer_used` | Name of the tool that produced the normalised text (`"webcrack"`, `"jsbeautifier"`, etc.). `NULL` when `text_normalized` is `NULL` |

Both `text_raw` and `text_normalized` are stored with ClickHouse `CODEC(ZSTD(3))` to keep storage cost reasonable across millions of samples.

---

## IOC extraction

**Table:** `redb_iocs` (shared with all formats)

JavaScript IOC extraction uses the same `IOCExtractorFromResults` class as DecompileBinja and DecompileAPK. JS samples get the same 22 IOC types (IPv4, IPv6, FQDN, URL, email, crypto addresses, CVEs, file paths, registry keys, etc.) with defanging support and IANA TLD validation. Called automatically in `workers.py` after the JS-specific extractors complete.

### Windows paths & registry keys in source-code form

`WINDOWS_PATH_PATTERN` and `REGISTRY_KEY_PATTERN` accept both the runtime form (`C:\Windows\Temp`, `HKLM\SYSTEM\...`) and the source-escaped form (`C:\\Windows\\Temp`, `HKLM\\SYSTEM\\...`) that appears inside JS / JSON / PowerShell string literals. Doubled backslashes are normalised to single before storage so an analyst querying for `C:\Users\Public` sees both forms collapsed to one IOC. Wildcard segments (e.g. `C:\Users\*\AppData\Local\Temp`) are preserved.

Registry hives recognised: `HKLM`, `HKCU`, `HKCR`, `HKU`, `HKCC`, `HKPD`, `HKEY_LOCAL_MACHINE`, `HKEY_CURRENT_USER`, `HKEY_CLASSES_ROOT`, `HKEY_USERS`, `HKEY_CURRENT_CONFIG`, `HKEY_PERFORMANCE_DATA`. A bare hive mention with no path component does not match (avoids prose false positives).

Three surfaces are scraped for every JS sample, each tagged with its own `redb_iocs.source_type` value so analysts can tell where an IOC was first visible:

| `source_type` | Surface | Catches |
|---|---|---|
| `text_raw` | The decoded source as it sits on disk | URLs, IPs, emails, etc. that aren't hidden by encoding or wrapping |
| `text_normalized` | The deobfuscated/beautified form (only added when it differs from raw) | IOCs unwrapped by webcrack from `eval(atob(...))` payloads, identifiers exposed by jsbeautifier on minified code |
| `string` | The decoded strings produced by `JSStringsExtractor` (hex/unicode/charcode/base64/concat unpacked into plaintext) | URLs and FQDNs hidden behind `String.fromCharCode(...)`, base64-wrapped tokens, concatenated `"a" + "b" + ...` chains, etc. |

The `text_raw` and `text_normalized` values are universal across text-based artefacts — the same two `SourceType` values are intended to host PowerShell, Python, email body, and extracted PDF/Office text in the future.