Search Results for author:
Found 15 papers, 8 papers with code
Analysis of Contagion Dynamics with Active Cyber Defenders
In this paper, we analyze the infection spreading dynamics of malware in a population of cyber nodes (i. e., computers or devices).
PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
To promote defense effectiveness, we propose a new mixture of attacks to instantiate PAD to enhance deep neural network-based measurements and malware detectors.
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style Transformation
Recent studies show that current source code authorship attribution methods can be compromised by attackers exploiting adversarial examples and coding style manipulation.
Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
Our main findings are: (i) predictive uncertainty indeed helps achieve reliable malware detection in the presence of dataset shift, but cannot cope with adversarial evasion attacks; (ii) approximate Bayesian methods are promising to calibrate and generalize malware detectors to deal with dataset shift, but cannot cope with adversarial evasion attacks; (iii) adversarial evasion attacks can render calibration methods useless, and it is an open problem to quantify the uncertainty associated with the predicted labels of adversarial examples (i. e., it is not effective to use predictive uncertainty to detect adversarial examples).
Towards Making Deep Learning-based Vulnerability Detectors Robust
Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
Arms Race in Adversarial Malware Detection: A Survey
In this paper, we survey and systematize the field of Adversarial Malware Detection (AMD) through the lens of a unified conceptual framework of assumptions, attacks, defenses, and security properties.
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware
By conducting experiments with the Drebin Android malware dataset, we show that the framework can achieve a 98. 49\% accuracy (on average) against grey-box attacks, where the attacker knows some information about the defense and the defender knows some information about the attack, and an 89. 14% accuracy (on average) against the more capable white-box attacks, where the attacker knows everything about the defense and the defender knows some information about the attack.
$μ$VulDeePecker: A Deep Learning-Based System for Multiclass Vulnerability Detection
Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i. e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i. e., incapable of addressing multiclass classification).
Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge
However, machine learning is known to be vulnerable to adversarial evasion attacks that manipulate a small number of features to make classifiers wrongly recognize a malware sample as a benign one.
Cryptography and Security 68-06
Election with Bribed Voter Uncertainty: Hardness and Approximation Algorithm
In this paper, we introduce a novel variant of the bribery problem, "Election with Bribed Voter Uncertainty" or BVU for short, accommodating the uncertainty that the vote of a bribed voter may or may not be counted.
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples
Adversarial machine learning in the context of image processing and related applications has received a large amount of attention.
SySeVR: A Framework for Using Deep Learning to Detect Software Vulnerabilities
Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database.
VulDeePecker: A Deep Learning-Based System for Vulnerability Detection
Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection.
Characterizing Honeypot-Captured Cyber Attacks: Statistical Framework and Case Study
In this paper, we propose the {\em first} statistical framework for rigorously analyzing honeypot-captured cyber attack data.
Cryptography and Security Applications
An Evasion and Counter-Evasion Study in Malicious Websites Detection
Within this framework, we show that an adaptive attacker can make malicious websites evade powerful detection models, but proactive training can be an effective counter-evasion defense mechanism.
