Search Results for author:
Found 23 papers, 10 papers with code
ModSec-Learn: Boosting ModSecurity with Machine Learning
However, we argue that this strategy is largely ineffective against web attacks, as detection is only based on heuristics and not customized on the application to protect.
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Relevant literature has claimed contradictory remarks in support of and against the robustness of over-parameterized networks.
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines
As a result of decades of research, Windows malware detection is approached through a plethora of techniques.
A New Formulation for Zeroth-Order Optimization of Adversarial EXEmples in Malware Detection
Machine learning malware detectors are vulnerable to adversarial EXEmples, i. e. carefully-crafted Windows programs tailored to evade detection.
Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
As a consequence, the injected content is confined to an integer number of chunks without tampering the other chunks containing the real bytes of the input examples, allowing us to extend our certified robustness guarantees to content insertion attacks.
AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
While novel attacks are continuously proposed, each is shown to outperform its predecessors using different experimental setups, hyperparameter settings, and number of forward and backward calls to the target models.
Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation
The living-off-the-land (LOTL) offensive methodologies rely on the perpetration of malicious actions through chains of commands executed by legitimate applications, identifiable exclusively by analysis of system logs.
Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
In this work, we show that this problem also affects robustness to adversarial examples, thereby hindering the development of secure model update practices.
Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors
Machine-learning phishing webpage detectors (ML-PWD) have been shown to suffer from adversarial manipulations of the HTML code of the input webpage.
Nebula: Self-Attention for Dynamic Malware Analysis
Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment, and storing their actions in log reports.
Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks
We empirically show that this defense improves the performances of RGB-D systems against adversarial examples even when they are computed ad-hoc to circumvent this detection mechanism, and that is also more effective than adversarial training.
Adversarial ModSecurity: Countering Adversarial SQL Injections with Robust Machine Learning
To overcome these issues, we design a robust machine learning model, named AdvModSec, which uses the CRS rules as input features, and it is trained to detect adversarial SQLi attacks.
A Survey on Reinforcement Learning Security with Application to Autonomous Driving
Reinforcement learning allows machines to learn from their own experience.
Explaining Machine Learning DGA Detectors from DNS Traffic Data
One of the most common causes of lack of continuity of online systems stems from a widely popular Cyber Attack known as Distributed Denial of Service (DDoS), in which a network of infected devices (botnet) gets exploited to flood the computational capacity of services through the commands of an attacker.
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts.
Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectors
Adversarial attacks against deep learning-based object detectors have been studied extensively in the past few years.
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
We showcase the usefulness of this dataset by testing the effectiveness of the computed patches against 127 models.
Adversarial EXEmples: Functionality-preserving Optimization of Adversarial Windows Malware
Windows malware classifiers that rely on static analysis have been proven vulnerable to adversarial EXEmples, i. e., malware samples carefully manipulated to evade detection.
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Evaluating robustness of machine-learning models to adversarial examples is a challenging problem.
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection
Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples in this paper - can bypass machine learning-based detection relying on static code analysis by perturbing relatively few input bytes.
Functionality-preserving Black-box Optimization of Adversarial Windows Malware
Windows malware detectors based on machine learning are vulnerable to adversarial examples, even if the attacker is only given black-box query access to the model.
Cryptography and Security
secml: A Python Library for Secure and Explainable Machine Learning
We present \texttt{secml}, an open-source Python library for secure and explainable machine learning.
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Based on this finding, we propose a novel attack algorithm that generates adversarial malware binaries by only changing few tens of bytes in the file header.
Cryptography and Security
