CCS

Graph Neural Networks (GNNs), inspired by Convolutional Neural Networks (CNNs), aggregate the message of nodes' neighbors and structure information to acquire expressive representations of nodes for node classification, graph classification, and link ...

Machine learning models are often trained on sensitive data, such as medical records or bank transactions, posing high privacy risks. In fact, membership inference attacks can use the model parameters or predictions to determine whether a given data ...

A membership inference (MI) attack predicts whether a data point was used for training a machine learning (ML) model. MI attacks are currently the most widely deployed attack for auditing privacy of a ML model. A recent work by Thudi et. al. [18] show ...

Federated learning is a distributed framework where a server computes a global model by aggregating the local models trained on users' private data. However, for a stronger data privacy guarantee, the server should not access the local models except the ...

Federated Learning (FL) has become a popular distributed learning method for training classifiers by using data that are private to individual clients. The clients´ data are typically assumed to be confidential, but their heterogeneity and potential ...

Unobservable physiological signals enhance biometric authentication systems. Photoplethysmography (PPG) signals are convenient owing to its ease of measurement and are usually well protected against remote adversaries in authentication. Any leaked PPG ...

Recent development has revealed that deep neural networks used in image classification systems are vulnerable to adversarial attacks. Thus, it is critical to understand the possible adversarial attacks to develop effective defense mechanisms. In this ...

The decision of neural networks is easily shifted at an attacker's will by so-called adversarial attacks. Initially only successful when directly applied to the input, recent advances allow attacks to breach the digital realm, leading to over-the-air ...

Recent works have demonstrated that deep learning models are vulnerable to backdoor poisoning attacks, where these attacks instill spurious correlations to external trigger patterns or objects (e.g., stickers, sunglasses, etc.). We find that such ...

Adversarial attacks can mislead a Deep Learning (DL) algorithm into generating erroneous predictions via feeding maliciously-disturbed inputs called adversarial examples. DL-based Natural Language Processing (NLP) algorithms are severely threatened by ...

Phishing is by far the most common and disruptive type of cyber-attack faced by most organizations. Phishing messages may share common attributes such as the same or similar subject lines, the same sending infrastructure, similar URLs with certain parts ...

We propose a hybrid machine learning architecture that simultaneously employs multiple deep learning models analyzing contextual and behavioral characteristics of Windows portable executable, producing a final prediction based on a decision from the ...

False positives generated by vulnerability scanners are an industry-wide challenge in web application security. Accordingly, this paper presents a novel multi-view deep learning architecture to optimise Dynamic Application Security Testing (DAST) ...

Leveraging security automation and orchestration technologies enables security analysts to respond more quickly and accurately to threats. However, current tooling is limited to automating very finely scoped and hand-coded situations, such as ...