46 citations · 109 across the 13 of their papers we have counts for
12 papers · 1 filter
Kumo: A Security-Focused Serverless Cloud Simulator
Wei Shao, Khaled Khasawneh, Setareh Rafatirad +2
Serverless computing abstracts infrastructure management but also obscures system-level behaviors that can introduce security risks. Prior work has shown that serverless platforms…
Bit of a Close Talker: A Practical Guide to Serverless Cloud Co-Location Attacks
Wei Shao, Najmeh Nazari, Behnam Omidi +4
Serverless computing has revolutionized cloud computing by offering users an efficient, cost-effective way to develop and deploy applications without managing infrastructure detail…
On Jailbreaking Quantized Language Models Through Fault Injection Attacks
Noureldin Zahran, Ahmad Tahmasivand, Ihsen Alouani +2
The safety alignment of Language Models (LMs) is a critical concern, yet their integrity can be challenged by direct parameter manipulation attacks, such as those potentially induc…
Swipe2Pair: Secure and Fast In-Band Wireless Device Pairing
Yaqi He, Kai Zeng, Long Jiao +2
Wireless device pairing is a critical security mechanism to bootstrap the secure communication between two devices without a pre-shared secret. It has been widely used in many Inte…
Evasive Hardware Trojan through Adversarial Power Trace
Behnam Omidi, Khaled N. Khasawneh, Ihsen Alouani
The globalization of the Integrated Circuit (IC) supply chain, driven by time-to-market and cost considerations, has made ICs vulnerable to hardware Trojans (HTs). Against this thr…
Defending with Errors: Approximate Computing for Robustness of Deep Neural Networks
Amira Guesmi, Ihsen Alouani, Khaled N. Khasawneh +4
Machine-learning architectures, such as Convolutional Neural Networks (CNNs) are vulnerable to adversarial attacks: inputs crafted carefully to force the system output to a wrong l…
ROOM: Adversarial Machine Learning Attacks Under Real-Time Constraints
Amira Guesmi, Khaled N. Khasawneh, Nael Abu-Ghazaleh +1
Advances in deep learning have enabled a wide range of promising applications. However, these systems are vulnerable to Adversarial Machine Learning (AML) attacks; adversarially cr…
Stochastic-HMDs: Adversarial Resilient Hardware Malware Detectors through Voltage Over-scaling
Md Shohidul Islam, Ihsen Alouani, Khaled N. Khasawneh
Machine learning-based hardware malware detectors (HMDs) offer a potential game changing advantage in defending systems against malware. However, HMDs suffer from adversarial attac…
Defensive Approximation: Securing CNNs using Approximate Computing
Amira Guesmi, Ihsen Alouani, Khaled Khasawneh +4
In the past few years, an increasing number of machine-learning and deep learning structures, such as Convolutional Neural Networks (CNNs), have been applied to solving a wide rang…
SPECCFI: Mitigating Spectre Attacks using CFI Informed Speculation
Esmaeil Mohammadian Koruyeh, Shirin Haji Amin Shirazi, Khaled N. Khasawneh +2
Spectre attacks and their many subsequent variants are a new vulnerability class affecting modern CPUs. The attacks rely on the ability to misguide speculative execution, generally…
Spectre Returns! Speculation Attacks using the Return Stack Buffer
Esmaeil Mohammadian Koruyeh, Khaled Khasawneh, Chengyu Song +1
The recent Spectre attacks exploit speculative execution, a pervasively used feature of modern microprocessors, to allow the exfiltration of sensitive data across protection bounda…
SafeSpec: Banishing the Spectre of a Meltdown with Leakage-Free Speculation
Khaled N. Khasawneh, Esmaeil Mohammadian Koruyeh, Chengyu Song +3
Speculative execution which is used pervasively in modern CPUs can leave side effects in the processor caches and other structures even when the speculated instructions do not comm…