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03/11/2023
SHIELD: An Adaptive and Lightweight Defense against the Remote Power Side-Channel Attacks on Multi-tenant FPGAs
Dynamic partial reconfiguration enables multi-tenancy in cloud-based FPG...
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09/22/2021
Security Analysis of Capsule Network Inference using Horizontal Collaboration
The traditional convolution neural networks (CNN) have several drawbacks...
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06/16/2021
Side-Channel Attacks on RISC-V Processors: Current Progress, Challenges, and Opportunities
Side-channel attacks on microprocessors, like the RISC-V, exhibit securi...
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06/13/2021
FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack
Convolutional Neural Networks (CNN) have shown impressive performance in...
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05/05/2021
Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks
From tiny pacemaker chips to aircraft collision avoidance systems, the s...
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12/10/2020
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking
In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less m...
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11/21/2020
MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices
Traditional learning-based approaches for run-time Hardware Trojan detec...
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12/03/2019
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
With a constant improvement in the network architectures and training me...
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02/04/2019
SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?
Recently, many adversarial examples have emerged for Deep Neural Network...
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01/29/2019
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
Due to data dependency and model leakage properties, Deep Neural Network...
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01/28/2019
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks
Capsule Networks envision an innovative point of view about the represen...
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11/05/2018
TrojanZero: Switching Activity-Aware Design of Undetectable Hardware Trojans with Zero Power and Area Footprint
Conventional Hardware Trojan (HT) detection techniques are based on the ...
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11/05/2018
Security for Machine Learning-based Systems: Attacks and Challenges during Training and Inference
The exponential increase in dependencies between the cyber and physical ...
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11/05/2018
ForASec: Formal Analysis of Security Vulnerabilities in Sequential Circuits
Security vulnerability analysis of Integrated Circuits using conventiona...
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11/04/2018
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning
Deep neural networks (DNN)-based machine learning (ML) algorithms have r...
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11/04/2018
SSCNets: A Selective Sobel Convolution-based Technique to Enhance the Robustness of Deep Neural Networks against Security Attacks
Recent studies have shown that slight perturbations in the input data ca...
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11/04/2018
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Deep Neural Networks (DNNs) have recently been shown vulnerable to adver...
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11/02/2018
ISA4ML: Training Data-Unaware Imperceptible Security Attacks on Machine Learning Modules of Autonomous Vehicles
Due to big data analysis ability, machine learning (ML) algorithms are b...
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10/16/2018