44 citations · 70 across the 22 of their papers we have counts for
23 papers
Revisiting Logic Encryption
Rupesh Raj Karn, Lakshmi Likhitha Mankali, Zeng Wang +5
Modern circuits face various threats like reverse engineering, theft of intellectual property (IP), side-channel attacks, etc. Here, we present a novel approach for IP protection b…
LLMs and the Future of Chip Design: Unveiling Security Risks and Building Trust
Zeng Wang, Lilas Alrahis, Likhitha Mankali +2
Chip design is about to be revolutionized by the integration of large language, multimodal, and circuit models (collectively LxMs). While exploring this exciting frontier with trem…
TroLLoc: Logic Locking and Layout Hardening for IC Security Closure against Hardware Trojans
Fangzhou Wang, Qijing Wang, Lilas Alrahis +7
Due to cost benefits, supply chains of integrated circuits (ICs) are largely outsourced nowadays. However, passing ICs through various third-party providers gives rise to many secu…
MaliGNNoma: GNN-Based Malicious Circuit Classifier for Secure Cloud FPGAs
Lilas Alrahis, Hassan Nassar, Jonas Krautter +4
The security of cloud field-programmable gate arrays (FPGAs) faces challenges from untrusted users attempting fault and side-channel attacks through malicious circuit configuration…
Always be Pre-Training: Representation Learning for Network Intrusion Detection with GNNs
Zhengyao Gu, Diego Troy Lopez, Lilas Alrahis +1
Graph neural network-based network intrusion detection systems have recently demonstrated state-of-the-art performance on benchmark datasets. Nevertheless, these methods suffer fro…
AutoLock: Automatic Design of Logic Locking with Evolutionary Computation
Zeng Wang, Lilas Alrahis, Dominik Sisejkovic +1
Logic locking protects the integrity of hardware designs throughout the integrated circuit supply chain. However, recent machine learning (ML)-based attacks have challenged its fun…
FPGA-Patch: Mitigating Remote Side-Channel Attacks on FPGAs using Dynamic Patch Generation
Mahya Morid Ahmadi, Lilas Alrahis, Ozgur Sinanoglu +1
We propose FPGA-Patch, the first-of-its-kind defense that leverages automated program repair concepts to thwart power side-channel attacks on cloud FPGAs. FPGA-Patch generates isof…
Graph Neural Networks for Hardware Vulnerability Analysis -- Can you Trust your GNN?
Lilas Alrahis, Ozgur Sinanoglu
The participation of third-party entities in the globalized semiconductor supply chain introduces potential security vulnerabilities, such as intellectual property piracy and hardw…
PoisonedGNN: Backdoor Attack on Graph Neural Networks-based Hardware Security Systems
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif +2
Graph neural networks (GNNs) have shown great success in detecting intellectual property (IP) piracy and hardware Trojans (HTs). However, the machine learning community has demonst…
DNN-Alias: Deep Neural Network Protection Against Side-Channel Attacks via Layer Balancing
Mahya Morid Ahmadi, Lilas Alrahis, Ozgur Sinanoglu +1
Extracting the architecture of layers of a given deep neural network (DNN) through hardware-based side channels allows adversaries to steal its intellectual property and even launc…
ALMOST: Adversarial Learning to Mitigate Oracle-less ML Attacks via Synthesis Tuning
Animesh Basak Chowdhury, Lilas Alrahis, Luca Collini +5
Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where k…
TrojanSAINT: Gate-Level Netlist Sampling-Based Inductive Learning for Hardware Trojan Detection
Hazem Lashen, Lilas Alrahis, Johann Knechtel +1
We propose TrojanSAINT, a graph neural network (GNN)-based hardware Trojan (HT) detection scheme working at the gate level. Unlike prior GNN-based art, TrojanSAINT enables both pre…
Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs
Lilas Alrahis, Johann Knechtel, Ozgur Sinanoglu
Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) for performance in learning and predicting on large-scale data present in social networks, biology, etc. Since…
Security Closure of IC Layouts Against Hardware Trojans
Fangzhou Wang, Qijing Wang, Bangqi Fu +7
Due to cost benefits, supply chains of integrated circuits (ICs) are largely outsourced nowadays. However, passing ICs through various third-party providers gives rise to many thre…
AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks
Tim Bucher, Lilas Alrahis, Guilherme Paim +3
The globalization of the Integrated Circuit (IC) market is attracting an ever-growing number of partners, while remarkably lengthening the supply chain. Thereby, security concerns,…
Embracing Graph Neural Networks for Hardware Security (Invited Paper)
Lilas Alrahis, Satwik Patnaik, Muhammad Shafique +1
Graph neural networks (GNNs) have attracted increasing attention due to their superior performance in deep learning on graph-structured data. GNNs have succeeded across various dom…
GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation
Lilas Alrahis, Johann Knechtel, Florian Klemme +2
Process variations and device aging impose profound challenges for circuit designers. Without a precise understanding of the impact of variations on the delay of circuit paths, gua…
NeuroUnlock: Unlocking the Architecture of Obfuscated Deep Neural Networks
Mahya Morid Ahmadi, Lilas Alrahis, Alessio Colucci +2
The advancements of deep neural networks (DNNs) have led to their deployment in diverse settings, including safety and security-critical applications. As a result, the characterist…
MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Prediction
Lilas Alrahis, Satwik Patnaik, Muhammad Shafique +1
Logic locking has received considerable interest as a prominent technique for protecting the design intellectual property from untrusted entities, especially the foundry. Recently,…
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction
Lilas Alrahis, Satwik Patnaik, Muhammad Abdullah Hanif +2
Logic locking aims to prevent intellectual property (IP) piracy and unauthorized overproduction of integrated circuits (ICs). However, initial logic locking techniques were vulnera…
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…
UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Locking
Lilas Alrahis, Satwik Patnaik, Johann Knechtel +4
Logic locking aims to protect the intellectual property (IP) of integrated circuit (IC) designs throughout the globalized supply chain. The SAIL attack, based on tailored machine l…
ScanSAT: Unlocking Static and Dynamic Scan Obfuscation
Lilas Alrahis, Muhammad Yasin, Nimisha Limaye +4
While financially advantageous, outsourcing key steps, such as testing, to potentially untrusted Outsourced Assembly and Test (OSAT) companies may pose a risk of compromising on-ch…