137 citations · 329 across the 21 of their papers we have counts for
21 papers
DeMiST: Detection and Mitigation of Stealthy Analog Hardware Trojans
Enahoro Oriero, Faiq Khalid, Syed Rafay Hasan
The global semiconductor supply chain involves design and fabrication at various locations, which leads to multiple security vulnerabilities, e.g., Hardware Trojan (HT) insertion.…
SHIELD: An Adaptive and Lightweight Defense against the Remote Power Side-Channel Attacks on Multi-tenant FPGAs
Mahya Morid Ahmadi, Faiq Khalid, Radha Vaidya +3
Dynamic partial reconfiguration enables multi-tenancy in cloud-based FPGAs, which presents security challenges for tenants, IPs, and data. Malicious users can exploit FPGAs for rem…
Security Analysis of Capsule Network Inference using Horizontal Collaboration
Adewale Adeyemo, Faiq Khalid, Tolulope A. Odetola +1
The traditional convolution neural networks (CNN) have several drawbacks like the Picasso effect and the loss of information by the pooling layer. The Capsule network (CapsNet) was…
Side-Channel Attacks on RISC-V Processors: Current Progress, Challenges, and Opportunities
Mahya Morid Ahmadi, Faiq Khalid, Muhammad Shafique
Side-channel attacks on microprocessors, like the RISC-V, exhibit security vulnerabilities that lead to several design challenges. Hence, it is imperative to study and analyze thes…
FeSHI: Feature Map Based Stealthy Hardware Intrinsic Attack
Tolulope Odetola, Faiq Khalid, Travis Sandefur +2
To reduce the time-to-market and access to state-of-the-art techniques, CNN hardware mapping and deployment on embedded accelerators are often outsourced to untrusted third parties…
Exploiting Vulnerabilities in Deep Neural Networks: Adversarial and Fault-Injection Attacks
Faiq Khalid, Muhammad Abdullah Hanif, Muhammad Shafique
From tiny pacemaker chips to aircraft collision avoidance systems, the state-of-the-art Cyber-Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs…
GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking
Lilas Alrahis, Satwik Patnaik, Faiq Khalid +4
In this paper, we propose GNNUnlock, the first-of-its-kind oracle-less machine learning-based attack on provably secure logic locking that can identify any desired protection logic…
MacLeR: Machine Learning-based Run-Time Hardware Trojan Detection in Resource-Constrained IoT Edge Devices
Faiq Khalid, Syed Rafay Hasan, Sara Zia +3
Traditional learning-based approaches for run-time Hardware Trojan detection require complex and expensive on-chip data acquisition frameworks and thus incur high area and power ov…
FANNet: Formal Analysis of Noise Tolerance, Training Bias and Input Sensitivity in Neural Networks
Mahum Naseer, Mishal Fatima Minhas, Faiq Khalid +3
With a constant improvement in the network architectures and training methodologies, Neural Networks (NNs) are increasingly being deployed in real-world Machine Learning systems. H…
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks
Alberto Marchisio, Giorgio Nanfa, Faiq Khalid +3
Spiking Neural Networks (SNNs) claim to present many advantages in terms of biological plausibility and energy efficiency compared to standard Deep Neural Networks (DNNs). Recent w…
RED-Attack: Resource Efficient Decision based Attack for Machine Learning
Faiq Khalid, Hassan Ali, Muhammad Abdullah Hanif +3
Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them…
CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks
Alberto Marchisio, Giorgio Nanfa, Faiq Khalid +3
Capsule Networks preserve the hierarchical spatial relationships between objects, and thereby bears a potential to surpass the performance of traditional Convolutional Neural Netwo…
SIMCom: Statistical Sniffing of Inter-Module Communications for Run-time Hardware Trojan Detection
Faiq Khalid, Syed Rafay Hasan, Osman Hasan +1
Timely detection of Hardware Trojans (HTs) has become a major challenge for secure integrated circuits. We present a run-time methodology for HT detection that employs a multi-para…
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…