相关论文: A New Security Boundary of Component Differentiall…

Advanced Persistent Threat (APT) attacks are highly sophisticated and employ a multitude of advanced methods and techniques to target organizations and steal sensitive and confidential information. APT attacks consist of multiple stages and…

Deep learning-based malware detection systems are vulnerable to adversarial EXEmples - carefully-crafted malicious programs that evade detection with minimal perturbation. As such, the community is dedicating effort to develop mechanisms to…

Among the promising approaches to enforce safety in control systems, learning Control Barrier Functions (CBFs) from expert demonstrations has emerged as an effective strategy. However, a critical challenge remains: verifying that the…

In this work, we examine the potential of Physical Unclonable Functions (PUFs) that have been implemented on NAND Flash memories using programming disturbances to act as sustainable primitives for the purposes of lightweight cryptography.…

With the ubiquity of IoT devices there is a growing demand for confidentiality and integrity of data. Solutions based on reconfigurable logic (CPLD or FPGA) have certain advantages over ASIC and MCU/SoC alternatives. Programmable logic…

RISC-V's limited security features hinder its use in confidential computing and heterogeneous platforms. This paper introduces RISecure-PUF, a security extension utilizing existing Physical Unclonable Functions for key generation and secure…

Encryption techniques demonstrate a great deal of security when implemented in an optical system (such as holography) due to the inherent physical properties of light and the precision it demands. However, such systems have shown to be…

Physically unclonable functions (PUFs) are used as low-cost cryptographic primitives in device authentication and secret key creation. SRAM-PUFs are well-known as entropy sources; nevertheless, due of non-deterministic noise environment…

As the Covid-19 pandemic grips the world, healthcare systems are being reshaped, where the e-health concepts become more likely to be accepted. Wearable devices often carry sensitive information from users which are exposed to security and…

The scope of this paper is to demonstrate a fully working and compact photonic Physical Unclonable Function (PUF) device capable of operating in real life scenarios as an authentication mechanism and random number generator. For this…

Federated learning (FL) represents a novel paradigm to machine learning, addressing critical issues related to data privacy and security, yet suffering from data insufficiency and imbalance. The emergence of foundation models (FMs) provides…

In today's digital age, the ease of data collection, transfer, and storage continue to shape modern society and the ways we interact with our world. The advantages are numerous, but there is also an increased risk of information…

A securely maintained key is the premise upon which data stored and transmitted by ubiquitously deployed resource limited devices, such as those in the Internet of Things (IoT), are protected. However, many of these devices lack a secure…

In the evolving landscape of IoT ecosystem, distinguishing between legitimate and compromised devices is a critical challenge. This research investigates the effectiveness of hardware performance counter (HPC)-derived signatures' uniqueness…

Machine learning is a powerful tool enabling full automation of a huge number of tasks without explicit programming. Despite recent progress of machine learning in different domains, these models have shown vulnerabilities when they are…

Due to the diverse and mobile nature of the deployment environment, smart commodity devices are vulnerable to various attacks which can grant unauthorized access to a rogue device in a large, connected network. Traditional digital…

It is shown that a class of optical physical unclonable functions (PUFs) can be learned to arbitrary precision with arbitrarily high probability, even in the presence of noise, given access to polynomially many challenge-response pairs and…

Federated Learning (FL) is a popular distributed machine learning paradigm that enables jointly training a global model without sharing clients' data. However, its repetitive server-client communication gives room for backdoor attacks with…

In this work, we present ioPUF+, which incorporates a novel Physical Unclonable Function (PUF) that generates unique fingerprints for Integrated Circuits (ICs) and the IoT nodes encompassing them. The proposed PUF generates device-specific…

Federated learning allows multiple participants to collaboratively train a central model without sharing their private data. However, this distributed nature also exposes new attack surfaces. In particular, backdoor attacks allow attackers…