148 matches found
CVE-2025-69893
A side-channel vulnerability exists in the implementation of BIP-39 mnemonic processing, as observed in Trezor One v1.13.0 to v1.14.0, Trezor T v1.13.0 to v1.14.0, and Trezor Safe v1.13.0 to v1.14.0 hardware wallets. This originates from the BIP-39 standard guidelines, which induce non-constant...
Tracing the Chain: Deep Learning for Stepping-Stone Intrusion Detection
Stepping-stone intrusions SSIs are a prevalent network evasion technique in which attackers route sessions through chains of compromised intermediate hosts to obscure their origin. Effective SSI detection requires correlating the incoming and outgoing flows at each relay host at extremely low fal...
Deep Learning-Driven Friendly Jamming for Secure Multicarrier ISAC under Channel Uncertainty
Integrated sensing and communication ISAC systems promise efficient spectrum utilization by jointly supporting radar sensing and wireless communication. This paper presents a deep learning-driven framework for enhancing physical-layer security in multicarrier ISAC systems under imperfect channel...
Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face
Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. These attacks exploit a critical vulnerability in passport enrollment procedures adopted by many countries, where the facial image is often acquire...
An Empirical Study of the Imbalance Issue in Software Vulnerability Detection
Vulnerability detection is crucial to protect software security. Nowadays, deep learning DL is the most promising technique to automate this detection task, leveraging its superior ability to extract patterns and representations within extensive code volumes. Despite its promise, DL-based...
GPU-Fuzz: Finding Memory Errors in Deep Learning Frameworks
GPU memory errors are a critical threat to deep learning DL frameworks, leading to crashes or even security issues. We introduce GPU-Fuzz, a fuzzer locating these issues efficiently by modeling operator parameters as formal constraints. GPU-Fuzz utilizes a constraint solver to generate test cases...
Helper-Assisted Coding for Gaussian Wiretap Channels: Deep Learning Meets PhySec
Consider the Gaussian wiretap channel, where a transmitter wishes to send a confidential message to a legitimate receiver in the presence of an eavesdropper. It is well known that if the eavesdropper experiences less channel noise than the legitimate receiver, then it is impossible for the...
NVIDIA RunX security vulnerabilities
NVIDIA runx is a deep learning experiment management tool developed by NVIDIA Corporation. NVIDIA runx contains a security vulnerability, which stems from code injection. This vulnerability may lead to code execution, denial of service, privilege escalation, information leakage, and data corrupti...
MAD-OOD: A Deep Learning Cluster-Driven Framework for an Out-Of-Distribution Malware Detection and Classification
Out of distribution OOD detection remains a critical challenge in malware classification due to the substantial intra family variability introduced by polymorphic and metamorphic malware variants. Most existing deep learning based malware detectors rely on closed world assumptions and fail to...
SHERLOCK: A Deep Learning Approach to Detect Software Vulnerabilities
The increasing reliance on software in various applications has made the problem of software vulnerability detection more critical. Software vulnerabilities can lead to security breaches, data theft, and other negative outcomes. Traditional software vulnerability detection techniques, such as...
Systematically Deconstructing APVD Steganography and Its Payload with a Unified Deep Learning Paradigm
In the era of digital communication, steganography allows covert embedding of data within media files. Adaptive Pixel Value Differencing APVD is a steganographic method valued for its high embedding capacity and invisibility, posing challenges for traditional steganalysis. This paper proposes a...
Adaptive Intrusion Detection for Evolving RPL IoT Attacks Using Incremental Learning
The routing protocol for low-power and lossy networks RPL has become the de facto routing standard for resource-constrained IoT systems, but its lightweight design exposes critical vulnerabilities to a wide range of routing-layer attacks such as hello flood, decreased rank, and version number...
Machine and Deep Learning for Indoor UWB Jammer Localization
Ultra-wideband UWB localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in smart buildings. Although machine learning ML and deep learning DL methods have improved tag localization, localizing...
Penetrating the Hostile: Detecting DeFi Protocol Exploits through Cross-Contract Analysis
Decentralized finance DeFi protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes...
MalDataGen: A Modular Framework for Synthetic Tabular Data Generation in Malware Detection
High-quality data scarcity hinders malware detection, limiting ML performance. We introduce MalDataGen, an open-source modular framework for generating high-fidelity synthetic tabular data using modular deep learning models e.g., WGAN-GP, VQ-VAE. Evaluated via dual validation TR-TS/TS-TR, seven...
Attack-Specialized Deep Learning with Ensemble Fusion for Network Anomaly Detection
The growing scale and sophistication of cyberattacks pose critical challenges to network security, particularly in detecting diverse intrusion types within imbalanced datasets. Traditional intrusion detection systems IDS often struggle to maintain high accuracy across both frequent and rare...
EUVD-2017-14796
Malware in sbrugna...
Enhancing Automotive Security with a Hybrid Approach Towards Universal Intrusion Detection System
Security measures are essential in the automotive industry to detect intrusions in-vehicle networks. However, developing a one-size-fits-all Intrusion Detection System IDS is challenging because each vehicle has unique data profiles. This is due to the complex and dynamic nature of the data...