CANnolo: An Anomaly Detection System Based on LSTM Autoencoders for Controller Area Network

[1]  Chun-Hung Richard Lin,et al.  Intrusion detection system: A comprehensive review , 2013, J. Netw. Comput. Appl..

[2]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[3]  Bogdan Groza,et al.  Efficient Intrusion Detection With Bloom Filtering in Controller Area Networks , 2019, IEEE Transactions on Information Forensics and Security.

[4]  Craig A. Smith,et al.  The Car Hacker's Handbook: A Guide for the Penetration Tester , 2016 .

[5]  Hafiz Malik,et al.  State-of-the-Art Survey on In-Vehicle Network Communication (CAN-Bus) Security and Vulnerabilities , 2018, ArXiv.

[6]  Antonella Santone,et al.  Car hacking identification through fuzzy logic algorithms , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[7]  Mirco Marchetti,et al.  Reverse engineering of automotive data frames , 2018 .

[8]  Hovav Shacham,et al.  Comprehensive Experimental Analyses of Automotive Attack Surfaces , 2011, USENIX Security Symposium.

[9]  Radha Poovendran,et al.  Cloaking the Clock: Emulating Clock Skew in Controller Area Networks , 2017, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).

[10]  2018 16th Annual Conference on Privacy, Security and Trust (PST) , 2018 .

[11]  Bogdan Groza,et al.  Source Identification Using Signal Characteristics in Controller Area Networks , 2014, IEEE Signal Processing Letters.

[12]  Nathalie Japkowicz,et al.  Frequency-based anomaly detection for the automotive CAN bus , 2015, 2015 World Congress on Industrial Control Systems Security (WCICSS).

[13]  Mirco Marchetti,et al.  READ: Reverse Engineering of Automotive Data Frames , 2003, IEEE Transactions on Information Forensics and Security.

[14]  Kang G. Shin,et al.  Error Handling of In-vehicle Networks Makes Them Vulnerable , 2016, CCS.

[15]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[16]  Stefano Zanero.  When Cyber Got Real: Challenges in Securing Cyber-Physical Systems , 2018, 2018 IEEE SENSORS.

[17]  Michele Colajanni,et al.  Detecting attacks to internal vehicle networks through Hamming distance , 2017, 2017 AEIT International Annual Conference.

[18]  Nathalie Japkowicz,et al.  Anomaly Detection in Automobile Control Network Data with Long Short-Term Memory Networks , 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[19]  Sepp Hochreiter,et al.  The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[20]  T. Dagan.  Parrot , a software-only anti-spoofing defense system for the CAN bus , 2016 .

[21]  Stacy J. Prowell,et al.  Modeling inter-signal arrival times for accurate detection of CAN bus signal injection attacks: a data-driven approach to in-vehicle intrusion detection , 2017, CISRC.

[22]  Dong Hoon Lee,et al.  Identifying ECUs Using Inimitable Characteristics of Signals in Controller Area Networks , 2016, IEEE Transactions on Vehicular Technology.

[23]  Anupam Joshi,et al.  OBD_SecureAlert: An Anomaly Detection System for Vehicles , 2016, 2016 IEEE International Conference on Smart Computing (SMARTCOMP).

[24]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[25]  Huy Kang Kim,et al.  GIDS: GAN based Intrusion Detection System for In-Vehicle Network , 2018, 2018 16th Annual Conference on Privacy, Security and Trust (PST).

[26]  Je-Won Kang,et al.  Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security , 2016, PloS one.

[27]  Matthew Spicer,et al.  Intrusion Detection System for Electronic Communication Buses: A New Approach , 2018 .

[28]  Matti Valovirta,et al.  Experimental Security Analysis of a Modern Automobile , 2011 .

[29]  Michele Colajanni,et al.  Evaluation of anomaly detection for in-vehicle networks through information-theoretic algorithms , 2016, 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI).

[30]  Je-Won Kang,et al.  A Novel Intrusion Detection Method Using Deep Neural Network for In-Vehicle Network Security , 2016, 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring).

[31]  Jana Dittmann,et al.  Security threats to automotive CAN networks - Practical examples and selected short-term countermeasures , 2008, Reliab. Eng. Syst. Saf..

[32]  Huy Kang Kim,et al.  OTIDS: A Novel Intrusion Detection System for In-vehicle Network by Using Remote Frame , 2017, 2017 15th Annual Conference on Privacy, Security and Trust (PST).

[33]  Andreas Meisel,et al.  Deep learning for anomaly detectionin multivariate time series data , 2017 .

[34]  Stefano Zanero,et al.  ReCAN – Dataset for reverse engineering of Controller Area Networks , 2020, Data in brief.

[35]  Adrian Taylor,et al.  Anomaly-Based Detection of Malicious Activity in In-Vehicle Networks , 2017 .

[36]  Stefano Zanero,et al.  CopyCAN: An Error-Handling Protocol based Intrusion Detection System for Controller Area Network , 2019, CPS-SPC@CCS.

[37]  Majid S alDosari,et al.  UNSUPERVISED ANOMALY DETECTION IN SEQUENCES USING LONG SHORT TERM MEMORY RECURRENT NEURAL , 2016 .

[38]  Felix C. Freiling,et al.  A structured approach to anomaly detection for in-vehicle networks , 2010, 2010 Sixth International Conference on Information Assurance and Security.

[39]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[40]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[41]  Huy Kang Kim,et al.  Intrusion detection system based on the analysis of time intervals of CAN messages for in-vehicle network , 2016, 2016 International Conference on Information Networking (ICOIN).

[42]  Zhu Han,et al.  Sec-IoV: A Multi-Stage Anomaly Detection Scheme for Internet of Vehicles , 2019, PERSIST-IoT '19.

[43]  Stefano Zanero,et al.  A Stealth, Selective, Link-Layer Denial-of-Service Attack Against Automotive Networks , 2017, DIMVA.

[44]  Lovekesh Vig,et al.  LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection , 2016, ArXiv.

[45]  Miaowen Wen,et al.  MBID: Micro-Blockchain-Based Geographical Dynamic Intrusion Detection for V2X , 2019, IEEE Communications Magazine.

[46]  Azzedine Boukerche,et al.  A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications , 2020, Future Gener. Comput. Syst..

[47]  Vincent Nicomette,et al.  A language-based intrusion detection approach for automotive embedded networks , 2015, Int. J. Embed. Syst..

[48]  Lovekesh Vig,et al.  Long Short Term Memory Networks for Anomaly Detection in Time Series , 2015, ESANN.

[49]  Tree-Based Intelligent Intrusion Detection System in Internet of Vehicles , 2019, 2019 IEEE Global Communications Conference (GLOBECOM).

[50]  Aryeh Kontorovich,et al.  Advanced Analytics for Connected Car Cybersecurity , 2017, 2018 IEEE 87th Vehicular Technology Conference (VTC Spring).