CCS

This paper aims to provide a thorough study on the effectiveness of the transformation-based ensemble defence for image classification and its reasons. It has been empirically shown that they can enhance the robustness against evasion attacks, while ...

Convolutional Neural Networks (CNNs) are deployed in more and more classification systems, but adversarial samples can be maliciously crafted to trick them, and are becoming a real threat. There have been various proposals to improve CNNs' adversarial ...

Conditional generative models, such as Schott et al.'s Analysis-by-Synthesis (ABS), have state-of-the-art robustness on MNIST, but fail in more challenging datasets. In this paper, we present E-ABS, an improvement on ABS that achieves state-of-the-art ...

Adversarial attacks have gained popularity recently due to their simplicity and impact. Their applicability to diverse security scenarios is however less understood. In particular, in some scenarios, attackers may come up naturally with ad-hoc black-box ...

Machine learning (ML) techniques are being used to detect increasing amounts of malware and variants. Despite successful applications of ML, we hypothesize that the full potential of ML is not realized in malware analysis (MA) due to a semantic gap ...

Training classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious ...

Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-intensive, even for expert users. Few tools exist and relatively little ...

State of the art deep learning techniques are known to be vulnerable to evasion attacks where an adversarial sample is generated from a malign sample and misclassified as benign.

Detection of encrypted malware command and control traffic based on TCP/...

Outsourcing machine learning inference creates a confidentiality dilemma: either the client has to trust the server with potentially sensitive input data, or the server has to share his commercially valuable model. Known remedies include homomorphic ...

Impersonation attacks against web authentication servers have been increasing in complexity over the last decade. Tunnelling services, such as VPNs or proxies, can be for instance used to faithfully impersonate victims in foreign countries. In this ...

Backdoor attacks are poisoning attacks and serious threats to deep neural networks. When an adversary mixes poison data into a training dataset, the training dataset is called a poison training dataset. A model trained with the poison training dataset ...