137 citations · 329 across the 21 of their papers we have counts for
8 papers · 1 filter
ForASec: Formal Analysis of Security Vulnerabilities in Sequential Circuits
Faiq Khalid, Imran Hafeez Abbassi, Semeen Rehman +3
Security vulnerability analysis of Integrated Circuits using conventional design-time validation and verification techniques (like simulations, emulations, etc.) is generally a com…
ApproxCS: Near-Sensor Approximate Compressed Sensing for IoT-Healthcare Systems
Ayesha Siddique, Osman Hasan, Faiq Khalid +1
Internet of Things (IoTs) is an emerging trend that has enabled an upgrade in the design of wearable healthcare monitoring systems through the (integrated) edge, fog, and cloud com…
A Roadmap Towards Resilient Internet of Things for Cyber-Physical Systems
Denise Ratasich, Faiq Khalid, Florian Geissler +3
The Internet of Things (IoT) is a ubiquitous system connecting many different devices - the things - which can be accessed from the distance. The cyber-physical systems (CPS) monit…
TrojanZero: Switching Activity-Aware Design of Undetectable Hardware Trojans with Zero Power and Area Footprint
Imran Hafeez Abbassi, Faiq Khalid, Semeen Rehman +4
Conventional Hardware Trojan (HT) detection techniques are based on the validation of integrated circuits to determine changes in their functionality, and on non-invasive side-chan…
FAdeML: Understanding the Impact of Pre-Processing Noise Filtering on Adversarial Machine Learning
Faiq Khalid, Muhammmad Abdullah Hanif, Semeen Rehman +2
Deep neural networks (DNN)-based machine learning (ML) algorithms have recently emerged as the leading ML paradigm particularly for the task of classification due to their superior…
SSCNets: Robustifying DNNs using Secure Selective Convolutional Filters
Hassan Ali, Faiq Khalid, Hammad Tariq +4
In this paper, we introduce a novel technique based on the Secure Selective Convolutional (SSC) techniques in the training loop that increases the robustness of a given DNN by allo…
QuSecNets: Quantization-based Defense Mechanism for Securing Deep Neural Network against Adversarial Attacks
Faiq Khalid, Hassan Ali, Hammad Tariq +4
Adversarial examples have emerged as a significant threat to machine learning algorithms, especially to the convolutional neural networks (CNNs). In this paper, we propose two quan…
TrISec: Training Data-Unaware Imperceptible Security Attacks on Deep Neural Networks
Faiq Khalid, Muhammad Abdullah Hanif, Semeen Rehman +2
Most of the data manipulation attacks on deep neural networks (DNNs) during the training stage introduce a perceptible noise that can be catered by preprocessing during inference o…