APNAS: Accuracy-and-performance-aware neural architecture search for neural hardware accelerators
Designing resource-efficient deep neural networks (DNNs) is a challenging task due to the
enormous diversity of applications as well as their time-consuming design, training …
enormous diversity of applications as well as their time-consuming design, training …
Neuroattack: Undermining spiking neural networks security through externally triggered bit-flips
V Venceslai, A Marchisio, I Alouani… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
Due to their proven efficiency, machine-learning systems are deployed in a wide range of
complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as …
complex real-life problems. More specifically, Spiking Neural Networks (SNNs) emerged as …
Is spiking secure? a comparative study on the security vulnerabilities of spiking and deep neural networks
A Marchisio, G Nanfa, F Khalid… - … Joint Conference on …, 2020 - ieeexplore.ieee.org
Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological
plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs) …
plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs) …
Mldemon: Deployment monitoring for machine learning systems
Post-deployment monitoring of ML systems is critical for ensuring reliability, especially as
new user inputs can differ from the training distribution. Here we propose a novel approach …
new user inputs can differ from the training distribution. Here we propose a novel approach …
Minimax robust detection: Classic results and recent advances
This paper provides an overview of results and concepts in minimax robust hypothesis
testing for two and multiple hypotheses. It starts with an introduction to the subject …
testing for two and multiple hypotheses. It starts with an introduction to the subject …
Fadec: A fast decision-based attack for adversarial machine learning
Due to the excessive use of cloud-based machine learning (ML) services, the smart cyber-
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …
physical systems (CPS) are increasingly becoming vulnerable to black-box attacks on their …
Rohnas: A neural architecture search framework with conjoint optimization for adversarial robustness and hardware efficiency of convolutional and capsule networks
Neural Architecture Search (NAS) algorithms aim at finding efficient Deep Neural Network
(DNN) architectures for a given application under given system constraints. DNNs are …
(DNN) architectures for a given application under given system constraints. DNNs are …
Navigating the IoT landscape: Unraveling forensics, security issues, applications, research challenges, and future
SF Ahmed, S Shuravi, A Bhuyian, S Afrin… - arXiv preprint arXiv …, 2023 - arxiv.org
Given the exponential expansion of the internet, the possibilities of security attacks and
cybercrimes have increased accordingly. However, poorly implemented security …
cybercrimes have increased accordingly. However, poorly implemented security …
Overview of security for smart cyber-physical systems
The tremendous growth of interconnectivity and dependencies of physical and cyber
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …
domains in cyber-physical systems (CPS) makes them vulnerable to several security threats …
MulBERRY: Enabling Bit-Error Robustness for Energy-Efficient Multi-Agent Autonomous Systems
The adoption of autonomous swarms, consisting of a multitude of unmanned aerial vehicles
(UAVs), operating in a collaborative manner, has become prevalent in mainstream …
(UAVs), operating in a collaborative manner, has become prevalent in mainstream …