Related papers: HYDRA: HYbrid Design for Remote Attestation (Using…
Despite advancements in machine learning for security, rule-based detection remains prevalent in Security Operations Centers due to the resource intensiveness and skill gap associated with ML solutions. While traditional rule-based methods…
Trusted Execution Environments (TEEs) are rapidly emerging as a root-of-trust for protecting sensitive applications and data using hardware-backed isolated worlds of execution. TEEs provide robust assurances regarding critical algorithm…
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 remote side-channel attacks (SCAs), and shared on-chip…
Hydra is a system which utilizes computer vision to perform near real time data quality management, initially developed for Hall-D in 2019. Since then, it has been deployed across all experimental halls at Jefferson Lab, with the CLAS12…
Virtualization promises significant benefits in security, efficiency, dependability, and cost. Achieving these benefits depends upon the reliability of the underlying virtual machine monitors (hypervisors). This paper describes an ongoing…
Ethereum smart contracts are programs that are deployed and executed in a consensus-based blockchain managed by a peer-to-peer network. Several re-entrancy attacks that aim to steal Ether, the cryptocurrency used in Ethereum, stored in…
The security goals of cloud providers and users include memory confidentiality and integrity, which requires implementing Replay-Attack protection (RAP). RAP can be achieved using integrity trees or mutually authenticated channels.…
The design of tiny trust anchors has received significant attention over the past decade, to secure low-end MCU-s that cannot afford expensive security mechanisms. In particular, hardware/software (hybrid) co-designs offer low hardware…
Process attestation systems verify that a continuous physical process, such as human authorship, actually occurred, rather than merely checking system state. These systems face a fundamental dependability challenge: the evidence collection…
The main issue with most evaluation schemes today is their "static" nature: the same problems are reused repeatedly, allowing for memorization, format exploitation, and eventual saturation. To measure genuine AI progress, we need evaluation…
Robust reinforcement learning (RL) aims to learn policies that remain effective despite uncertainties in its environment, which frequently arise in real-world applications due to variations in environment dynamics. The robust RL methods…
Trusted Execution Environments (TEEs) such as ARM TrustZone are widely used in IoT and embedded devices to protect sensitive code and data. However, most existing defenses focus on secure boot or REE-side monitoring and provide little…
In distributed systems, trust decisions are made on the basis of integrity evidence generated via remote attestation. Examples of the kinds of evidence that might be collected are boot time image hash values; fingerprints of initialization…
As processor designs grow more complex, verification remains bottlenecked by slow software simulation and low-quality random test stimuli. Recent research has applied software fuzzers to hardware verification, but these rely on semantically…
The proliferation of high-fidelity synthetic media, coupled with exploitable hardware vulnerabilities in conventional imaging pipelines, has precipitated a crisis of trust in digital content. Existing countermeasures, from post-hoc…
Intel(R) Software Guard Extensions (Intel(R) SGX) is a promising technology to securely process information in otherwise untrusted environments. An important aspect of Intel SGX is the ability to perform remote attestation to assess the…
Customers of cloud services have to trust the cloud providers, as they control the building blocks that form the cloud. This includes the hypervisor enabling the sharing of a single hardware platform among multiple tenants. AMD Secure…
Deep neural networks (DNNs) offer plenty of challenges in executing efficient computation at edge nodes, primarily due to the huge hardware resource demands. The article proposes HYDRA, hybrid data multiplexing, and runtime layer…
Hardware-firmware co-verification is critical to design trustworthy systems. While formal methods can provide verification guarantees, due to the complexity of firmware and hardware, it can lead to state space explosion. There are promising…
The world needs diverse and unbiased data to train deep learning models. Currently data comes from a variety of sources that are unmoderated to a large extent. The outcomes of training neural networks with unverified data yields biased…