Search Results for author:
Found 9 papers, 0 papers with code
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning
We thoroughly evaluate our proposed approach and compare it to existing state-of-the-art defenses using multiple architectures, datasets, and poison budgets.
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem
We introduce MaleficNet 2. 0, a novel technique to embed self-extracting, self-executing malware in neural networks.
OliVaR: Improving Olive Variety Recognition using Deep Neural Networks
The morphological classification consists of the visual pairwise comparison of different organs of the olive tree, where the most important organ is considered to be the endocarp.
Minerva: A File-Based Ransomware Detector
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future.
MalPhase: Fine-Grained Malware Detection Using Network Flow Data
Economic incentives encourage malware authors to constantly develop new, increasingly complex malware to steal sensitive data or blackmail individuals and companies into paying large ransoms.
PassFlow: Guessing Passwords with Generative Flows
In this paper, we propose PassFlow, a flow-based generative model approach to password guessing.
Reliable Detection of Compressed and Encrypted Data
We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB.
EnCoD: Distinguishing Compressed and Encrypted File Fragments
To address this issue, we design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data, starting with fragments as small as 512 bytes.
The Naked Sun: Malicious Cooperation Between Benign-Looking Processes
Recent progress in machine learning has generated promising results in behavioral malware detection.
