Neural Network-Based Detection and Multi-Class Classification of FDI Attacks in Smart Grid Home Energy Systems

False Data Injection Attacks FDIAs pose a significant threat to smart grid infrastructures, particularly Home Area Networks HANs, where real-time monitoring and control are highly adopted. Owing to the comparatively less stringent security controls and widespread availability of HANs, attackers...

Who'S the Evil Twin? Differential Auditing for Undesired Behavior

Detecting hidden behaviors in neural networks poses a significant challenge due to minimal prior knowledge and potential adversarial obfuscation. We explore this problem by framing detection as an adversarial game between two teams: the red team trains two similar models, one trained solely on...

ProvX: Generating Counterfactual-Driven Attack Explanations for Provenance-Based Detection

Provenance graph-based intrusion detection systems are deployed on hosts to defend against increasingly severe Advanced Persistent Threat. Using Graph Neural Networks to detect these threats has become a research focus and has demonstrated exceptional performance. However, the widespread adoption...

The vulnerability of the FortiMail email security system, a software-hardware solution for information protection based on AI and deep neural networks from Fortinet’s FortiNDR (Network Detection and Response), arises from the possibility of copying buffers without checking the size of the input data. This allows attackers to execute arbitrary code.

The vulnerability of the FortiMail email security system, a software-hardware solution for information protection based on AI and deep neural networks from Fortinet, is related to the copying of buffers without checking the size of the input data. Exploiting this vulnerability allows an attacker...

Drone Detection with Event Cameras

The diffusion of drones presents significant security and safety challenges. Traditional surveillance systems, particularly conventional frame-based cameras, struggle to reliably detect these targets due to their small size, high agility, and the resulting motion blur and poor performance in...

Privacy Risk Predictions Based on Fundamental Understanding of Personal Data and an Evolving Threat Landscape

It is difficult for individuals and organizations to protect personal information without a fundamental understanding of relative privacy risks. By analyzing over 5,000 empirical identity theft and fraud cases, this research identifies which types of personal data are exposed, how frequently...

Intrusion Detection in Heterogeneous Networks with Domain-Adaptive Multi-Modal Learning

Network Intrusion Detection Systems NIDS play a crucial role in safeguarding network infrastructure against cyberattacks. As the prevalence and sophistication of these attacks increase, machine learning and deep neural network approaches have emerged as effective tools for enhancing NIDS...

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...

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...

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...

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...

Next-Generation Quantum Neural Networks: Enhancing Efficiency, Security, and Privacy

This paper provides an integrated perspective on addressing key challenges in developing reliable and secure Quantum Neural Networks QNNs in the Noisy Intermediate-Scale Quantum NISQ era. In this paper, we present an integrated framework that leverages and combines existing approaches to enhance...

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...

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...

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...

HyDRA: a Hybrid Dual-Mode Network for Closed- and Open-Set RFFI with Optimized VMD

Device recognition is vital for security in wireless communication systems, particularly for applications like access control. Radio Frequency Fingerprint Identification RFFI offers a non-cryptographic solution by exploiting hardware-induced signal distortions. This paper proposes HyDRA, a Hybrid...

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...