Endogenous Macrodynamics in Algorithmic Recourse

Existing work on Counterfactual Explanations (CE) and Algorithmic Recourse (AR) has largely focused on single individuals in a static environment: given some estimated model, the goal is to find valid counterfactuals for an individual instance that fulfill various desiderata. The ability of such counterfactuals to handle dynamics like data and...

On the Effect of Transitivity and Granularity on Vulnerability Propagation in the Maven Ecosystem

Reusing software libraries is a pillar of modern software engineering. In 2022, the average Java application depends on 40 third-party libraries. Relying on such libraries exposes a project to potential vulnerabilities and may put an application and its users at risk. Unfortunately, research on software ecosystems has shown that the number of...

Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binarie

Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving considering effort in labelling code with helpful summaries....

Guiding Automated Test Case Generation for Transaction-Reverting Statements in Smart Contracts

Transaction-reverting statements are key constructs within Solidity that are extensively used for authority and validity checks. Current state-of-the-art search-based testing and fuzzing approaches do not explicitly handle these statements and therefore can not effectively detect security vulnerabilities. In this paper, we argue that it is...

Accelerating RRAM Testing with a Low-cost Computation-in-Memory based DFT

Emerging non-volatile resistive RAM (RRAM) device technology has shown great potential to cultivate not only high-density memory storage, but also energy-efficient computing units. However, the unique challenges related to RRAM fabrication process render the traditional memory testing solutions inefficient and inadequate for high product quality...

Federated Learning for Tabular Data: Exploring Potential Risk to Privacy

Federated Learning (FL) has emerged as a potentially powerful privacy-preserving machine learning method-ology, since it avoids exchanging data between participants, but instead exchanges model parameters. FL has traditionally been applied to image, voice and similar data, but recently it has started to draw attention from domains including...

CamPressID: Optimizing Camera Configuration and Finger Pressure for Biometric Authentication

To protect sensitive information on smartphones, state-of-the-art (SoA) studies exploit the built-in camera to capture PPG signals from fingertips as a hard-to-forge biometric. However, those studies do not provide a comprehensive analysis to optimize the camera parameters and finger pressure, leading to distorted and unstable PPG signals that...

LightRoAD: Lightweight Rowhammer Attack Detector

Dynamic Random Access Memory (DRAM)-based systems are widely used in mobile and portable applications where low-cost and high-storage memory capability are required. However, such systems are prone to attacks. A latent threat to DRAM-based system security is the so-called Rowhammer attacks. By repeatedly accessing memory, an attacker is able to...

np-CECADA: Enhancing Ubiquitous Connectivity of LoRa Networks

Long Range Wide Area Networks (LoRaWAN) offer ubiquitous communications for The Internet of Things (IoT). However, there are many challenges in rolling out LoRaWAN - mainly scalability, energy efficiency, Packet Reception Ratio (PRR), and keeping the channel access as simple as unslotted ALOHA. To this end, we design non-persistent Capture...