for Security and Services Antonio Rizzo, Alessandro Rossi, - PowerPoint PPT Presentation

Jan 07, 2024 •5 likes •275 views

CyberPhysical systems for Security and Services Antonio Rizzo, Alessandro Rossi, Francesco Montefoschi, Giovanni Burresi Carlo Festucci, Maurizio Caporali Siena, 14 Sett 2018 Case of study: ATM-Sense TREND Rapine vs Attacchi ATM - Fonte

CyberPhysical systems for Security and Services Antonio Rizzo, Alessandro Rossi, Francesco Montefoschi, Giovanni Burresi Carlo Festucci, Maurizio Caporali Siena, 14 Sett 2018
Case of study: ATM-Sense
TREND Rapine vs Attacchi ATM - Fonte ABI 2017 Numero Rapine per Anno 1.400 1.200 1.000 800 600 400 200 0 2012 2013 2014 2015 2016
ATM attacks
New Attacks https://www.europol.europa.eu/newsroom/news/27-arrested-in-successful-hit-against-atm-black-box-attacks
Video Surveillance Approach
Intel RealSense Depth Cameras • Powerful Open Souce SDK • Easily Embeddable
Intel RealSense Depth Cameras
Convolutional Neural Networks maxpool maxpool convolutions source image convolutions fully connected
Image Convolutions
Image Convolutions
Image Convolutions
Convolutions
Max Pooling maxpool source image convolutions
More layers... maxpool maxpool convolutions source image convolutions
Visualizing Convolutional Layers References: - Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009, June). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proceedings of the 26th annual international conference on machine learning (pp. 609-616). ACM.
Convolutional Neural Networks maxpool maxpool convolutions source image convolutions fully connected
CNN: ImageNet Classification Error References: - Russakovsky, Olga, et al. "Imagenet large scale visual recognition challenge." International Journal of Computer Vision 115.3 (2015): 211-252 - Hardware Architectures for Deep Neural Networks, ISCA Tutorial, MIT
Machine Learning Process 1. Get a dataset 2. Define the network architecture 3. Train and Test the model
1. Get a Dataset
2. Define the Network Architecture
3. Train and Test the Model
Results Single Frame analysis: Five Frames analysis: ● No false alarms Test Dataset Classification Accuracy ● No undetected attacks ● Attack detection time: Background 98.19% ○ mean: 2.4 sec Withdrawal 97.05% ○ max: 3.3 sec Attack 98.32% Average 97.85% ○ mean: 0.5 sec Model Running on SECO SBC – A80 with Intel Braswell CPU
Predicting Security Thank You

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