A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...

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...

Aether - Adaptive Exploit and Threat Hunting Engine for EVM-based Repositories

Aether is a Python-based framework for analyzing Solidity smart contracts, generating vulnerability findings, producing Foundry-based proof-of-concept PoC tests, and optionally validating those tests on mainnet forks. It combines static analysis, prompt-driven LLM analysis, and AI-ensemble...

Android Malware Detection: A Machine Learning Approach

This study examines machine learning techniques like Decision Trees, Support Vector Machines, Logistic Regression, Neural Networks, and ensemble methods to detect Android malware. The study evaluates these models on a dataset of Android applications and analyzes their accuracy, efficiency, and...

Securing IoT Communications Via Anomaly Traffic Detection: Synergy of Genetic Algorithm and Ensemble Method

The rapid growth of the Internet of Things IoT has transformed industries by enabling seamless data exchange among connected devices. However, IoT networks remain vulnerable to security threats such as denial of service DoS attacks, anomalous traffic, and data manipulation due to decentralized...

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-2025-30973

Malicious code in bioql PyPI...

Adaptive Deception Framework with Behavioral Analysis for Enhanced Cybersecurity Defense

This paper presents CADL Cognitive-Adaptive Deception Layer, an adaptive deception framework achieving 99.88% detection rate with 0.13% false positive rate on the CICIDS2017 dataset. The framework employs ensemble machine learning Random Forest, XGBoost, Neural Networks combined with behavioral...

NVIDIA Megatron-LM ensemble_classifer script code injection vulnerability

NVIDIA Megatron-LM is a PyTorch-based distributed training framework from NVIDIA that specializes in training large Transformer language models. A code injection vulnerability exists in the NVIDIA Megatron-LM ensembleclassifer script, which can be exploited by attackers to cause code execution,...

CVE-2025-23354

NVIDIA Megatron-LM for all platforms contains a vulnerability in the ensembleclassifer script where malicious data created by an attacker may cause an injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, Information disclosure, and data...

Arbitrary Code Injection

Overview megatron-core is a Megatron Core - a library for efficient and scalable training of transformer based models Affected versions of this package are vulnerable to Arbitrary Code Injection via the ensembleclassifer script. An attacker can execute arbitrary code, escalate privileges, disclos...

CVE-2025-23354

NVIDIA Megatron-LM for all platforms contains a vulnerability in the ensembleclassifer script where malicious data created by an attacker may cause an injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, Information disclosure, and data...

CVE-2025-23354

NVIDIA Megatron-LM for all platforms contains a vulnerability in the ensembleclassifer script where malicious data created by an attacker may cause an injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, Information disclosure, and data...

CVE-2025-23354

The CVE-2025-23354 issue affects NVIDIA Megatron-LM, specifically the ensemble_classifer script, with a code injection vulnerability that attacker-supplied data can trigger. The vulnerability may enable code execution, privilege escalation, information disclosure, and data tampering. Affected com...

CVE-2025-23354

NVIDIA Megatron-LM for all platforms contains a vulnerability in the ensembleclassifer script where malicious data created by an attacker may cause an injection. A successful exploit of this vulnerability may lead to code execution, escalation of privileges, Information disclosure, and data...

PT-2025-39263

Name of the Vulnerable Software and Affected Versions NVIDIA Megatron-LM affected versions not specified Description The software contains a flaw in the ensemble classifer script that could allow an attacker to inject malicious data. Successful exploitation of this issue may result in code...

NVIDIA Megatron-LM 代码注入漏洞

NVIDIA Megatron-LM is a PyTorch-based distributed training framework from NVIDIA that specializes in training large Transformer language models. A code injection vulnerability exists in the NVIDIA Megatron-LM ensembleclassifer script, which can be exploited by attackers to cause code execution,...

A Comparative Analysis of Ensemble-Based Machine Learning Approaches with Explainable AI for Multi-Class Intrusion Detection in Drone Networks

The growing integration of drones into civilian, commercial, and defense sectors introduces significant cybersecurity concerns, particularly with the increased risk of network-based intrusions targeting drone communication protocols. Detecting and classifying these intrusions is inherently...

Hierarchical Deep Fusion Framework for Multi-Dimensional Facial Forgery Detection - the 2024 Global Deepfake Image Detection Challenge

The proliferation of sophisticated deepfake technology poses significant challenges to digital security and authenticity. Detecting these forgeries, especially across a wide spectrum of manipulation techniques, requires robust and generalized models. This paper introduces the Hierarchical Deep...

Ensembling Large Language Models for Code Vulnerability Detection: an Empirical Evaluation

Code vulnerability detection is crucial for ensuring the security and reliability of modern software systems. Recently, Large Language Models LLMs have shown promising capabilities in this domain. However, notable discrepancies in detection results often arise when analyzing identical code segmen...