Defending against Stegomalware in Deep Neural Networks with Permutation Symmetry

Deep neural networks are being utilized in a growing number of applications, both in production systems and for personal use. Network checkpoints are as a consequence often shared and distributed on various platforms to ease the development process. This work considers the threat of neural networ...

Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks

Distributed Denial of Service DDoS attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel collaborative architecture that integrates a P4-programmable data plane with an SDN control plane to enable real-time DDo...

Robust DDoS-Attack Classification with 3D CNNs against Adversarial Methods

Distributed Denial-of-Service DDoS attacks remain a serious threat to online infrastructure, often bypassing detection by altering traffic in subtle ways. We present a method using hive-plot sequences of network data and a 3D convolutional neural network 3D CNN to classify DDoS traffic with high...

Machine Learning-Based AES Key Recovery Via Side-Channel Analysis on the ASCAD Dataset

Cryptographic algorithms like AES and RSA are widely used and they are mathematically robust and almost unbreakable but its implementation on physical devices often leak information through side channels, such as electromagnetic EM emissions, potentially compromising said theoretically secure...

Developing a Transferable Federated Network Intrusion Detection System

Intrusion Detection Systems IDS are a vital part of a network-connected device. In this paper, we develop a deep learning based intrusion detection system that is deployed in a distributed setup across devices connected to a network. Our aim is to better equip deep learning models against unknown...

MalFlows: Context-Aware Fusion of Heterogeneous Flow Semantics for Android Malware Detection

Static analysis, a fundamental technique in Android app examination, enables the extraction of control flows, data flows, and inter-component communications ICCs, all of which are essential for malware detection. However, existing methods struggle to leverage the semantic complementarity across...

BadBlocks: Low-Cost and Stealthy Backdoor Attacks Tailored for Text-To-Image Diffusion Models

In recent years,Diffusion models have achieved remarkable progress in the field of image generation.However,recent studies have shown that diffusion models are susceptible to backdoor attacks,in which attackers can manipulate the output by injecting covert triggers such as specific visual pattern...

Hierarchical Graph Neural Network for Compressed Speech Steganalysis

Steganalysis methods based on deep learning DL often struggle with computational complexity and challenges in generalizing across different datasets. Incorporating a graph neural network GNN into steganalysis schemes enables the leveraging of relational data for improved detection accuracy and...

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection

Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning ML algorithms have been applied to Android...

A Novel Post-Quantum Secure Digital Signature Scheme Based on Neural Network

Digital signatures are fundamental cryptographic primitives that ensure the authenticity and integrity of digital documents. In the post-quantum era, classical public key-based signature schemes become vulnerable to brute-force and key-recovery attacks due to the computational power of quantum...

SUSE CVE-2025-51480

Path Traversal vulnerability in onnx.externaldatahelper.saveexternaldata in ONNX 1.17.0 allows attackers to overwrite arbitrary files by supplying crafted externaldata.location paths containing traversal sequences, bypassing intended directory restrictions...

Directory Traversal

Overview onnx is an Open Neural Network Exchange Affected versions of this package are vulnerable to Directory Traversal via the saveexternaldata function. An attacker can overwrite arbitrary files by supplying crafted values to the externaldata.location parameter containing traversal sequences,...

PYSEC-2025-148

Path Traversal vulnerability in onnx.externaldatahelper.saveexternaldata in ONNX 1.17.0 allows attackers to overwrite arbitrary files by supplying crafted externaldata.location paths containing traversal sequences, bypassing intended directory restrictions...

ONNX 路径遍历漏洞

ONNX Open Neural Network Exchange is an open standard for machine learning interoperability open-sourced by ONNX. A security vulnerability exists in ONNX version 1.17.0, which stems from a path traversal vulnerability in onnx.externaldatahelper.saveexternaldata, which could lead to overwriting...

Towards Trustworthy AI: Secure Deepfake Detection Using CNNs and Zero-Knowledge Proofs

In the era of synthetic media, deepfake manipulations pose a significant threat to information integrity. To address this challenge, we propose TrustDefender, a two-stage framework comprising i a lightweight convolutional neural network CNN that detects deepfake imagery in real-time extended...

REAL-IoT: Characterizing GNN Intrusion Detection Robustness under Practical Adversarial Attack

Graph Neural Network GNN-based network intrusion detection systems NIDS are often evaluated on single datasets, limiting their ability to generalize under distribution drift. Furthermore, their adversarial robustness is typically assessed using synthetic perturbations that lack realism. This...

Efficient Private Inference Based on Helper-Assisted Malicious Security Dishonest Majority MPC

Private inference based on Secure Multi-Party Computation MPC addresses data privacy risks in Machine Learning as a Service MLaaS. However, existing MPC-based private inference frameworks focuses on semi-honest or honest majority models, whose threat models are overly idealistic, while malicious...

Detection of Intelligent Tampering in Wireless Electrocardiogram Signals Using Hybrid Machine Learning

With the proliferation of wireless electrocardiogram ECG systems for health monitoring and authentication, protecting signal integrity against tampering is becoming increasingly important. This paper analyzes the performance of CNN, ResNet, and hybrid Transformer-CNN models for tamper detection. ...

SecONNds: Secure Outsourced Neural Network Inference on ImageNet

The widespread adoption of outsourced neural network inference presents significant privacy challenges, as sensitive user data is processed on untrusted remote servers. Secure inference offers a privacy-preserving solution, but existing frameworks suffer from high computational overhead and...

Deep Spatial Neural Net Models with Functional Predictors: Application in Large-Scale Crop Yield Prediction

Accurate prediction of crop yield is critical for supporting food security, agricultural planning, and economic decision-making. However, yield forecasting remains a significant challenge due to the complex and nonlinear relationships between weather variables and crop production, as well as...