Graph-Based Floor Separation Using Node Embeddings and Clustering of WiFi Trajectories

Indoor positioning systems IPSs are increasingly vital for location-based services in complex multi-storey environments. This study proposes a novel graph-based approach for floor separation using Wi-Fi fingerprint trajectories, addressing the challenge of vertical localization in indoor settings...

FicGCN: Unveiling the Homomorphic Encryption Efficiency from Irregular Graph Convolutional Networks

Graph Convolutional Neural Networks GCNs have gained widespread popularity in various fields like personal healthcare and financial systems, due to their remarkable performance. Despite the growing demand for cloud-based GCN services, privacy concerns over sensitive graph data remain significant...

[SECURITY] Fedora 42 Update: qt6-qtdatavis3d-6.9.1-1.fc42

Qt Data Visualization module provides multiple graph types to visualize data in 3D space both with C++ and Qt Quick 2...

[SECURITY] Fedora 42 Update: qt6-qtcharts-6.9.1-1.fc42

Qt Charts module provides a set of easy to use chart components. It uses the Qt Graphics View Framework, therefore charts can be easily integrated to modern user interfaces. Qt Charts can be used as QWidgets, QGra phicsWidget, or QML types. Users can easily create impressive graphs by selecting o...

AI-Based Software Vulnerability Detection: a Systematic Literature Review

Software vulnerabilities in source code pose serious cybersecurity risks, prompting a shift from traditional detection methods e.g., static analysis, rule-based matching to AI-driven approaches. This study presents a systematic review of software vulnerability detection SVD research from 2018 to...

Devil'S Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols

Graph neural networks GNNs have achieved significant success in graph representation learning and have been applied to various domains. However, many real-world graphs contain sensitive personal information, such as user profiles in social networks, raising serious privacy concerns when graph...

WGLE:Backdoor-Free and Multi-Bit Black-Box Watermarking for Graph Neural Networks

Graph Neural Networks GNNs are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model...

VulnCheck KEV: CVE-2023-48728

A cross-site scripting xss vulnerability exists in the functiongetOpenGraph videoName functionality of WWBN AVideo 11.6 and dev master commit 3c6bb3ff. A specially crafted HTTP request can lead to arbitrary Javascript execution. An attacker can get a user to visit a webpage to trigger this...

Ai-Driven Vulnerability Analysis in Smart Contracts: Trends, Challenges and Future Directions

Smart contracts, integral to blockchain ecosystems, enable decentralized applications to execute predefined operations without intermediaries. Their ability to enforce trustless interactions has made them a core component of platforms such as Ethereum. Vulnerabilities such as numerical overflows,...

VulnCheck KEV: CVE-2024-52763

A cross-site scripting XSS vulnerability in the component /graphallperiods.php of Ganglia-web v3.73 to v3.75 allows attackers to execute arbitrary web scripts or HTML via a crafted payload injected into the "g" parameter...

Obfuscation-Resilient Binary Code Similarity Analysis Using Dominance Enhanced Semantic Graph

Binary code similarity analysis BCSA serves as a core technique for binary analysis tasks such as vulnerability detection. While current graph-based BCSA approaches capture substantial semantics and show strong performance, their performance suffers under code obfuscation due to the unstable...

Unintended Proxy or Intermediary ('Confused Deputy')

Overview Affected versions of this package are vulnerable to Unintended Proxy or Intermediary 'Confused Deputy' via the ResourceGraphDefinition resources. An attacker can execute arbitrary code on cluster nodes by supplying attacker-controlled images. This is only exploitable if the user has...

Unintended Proxy or Intermediary ('Confused Deputy')

Overview Affected versions of this package are vulnerable to Unintended Proxy or Intermediary 'Confused Deputy' via the ResourceGraphDefinition resources. An attacker can execute arbitrary code on cluster nodes by supplying attacker-controlled images. This is only exploitable if the user has...

Keyed Chaotic Dynamics for Privacy-Preserving Neural Inference

Neural network inference typically operates on raw input data, increasing the risk of exposure during preprocessing and inference. Moreover, neural architectures lack efficient built-in mechanisms for directly authenticating input data. This work introduces a novel encryption method for ensuring...

Heterogeneous Secure Transmissions in IRS-Assisted NOMA Communications: CO-GNN Approach

Intelligent Reflecting Surfaces IRS enhance spectral efficiency by adjusting reflection phase shifts, while Non-Orthogonal Multiple Access NOMA increases system capacity. Consequently, IRS-assisted NOMA communications have garnered significant research interest. However, the passive nature of the...

An Accurate and Efficient Vulnerability Propagation Analysis Framework

Identifying the impact scope and scale is critical for software supply chain vulnerability assessment. However, existing studies face substantial limitations. First, prior studies either work at coarse package-level granularity, producing many false positives, or fail to accomplish whole-ecosyste...

PackHero: a Scalable Graph-Based Approach for Efficient Packer Identification

Anti-analysis techniques, particularly packing, challenge malware analysts, making packer identification fundamental. Existing packer identifiers have significant limitations: signature-based methods lack flexibility and struggle against dynamic evasion, while Machine Learning approaches require...

Amatriciana: Exploiting Temporal GNNs for Robust and Efficient Money Laundering Detection

Money laundering is a financial crime that poses a serious threat to financial integrity and social security. The growing number of transactions makes it necessary to use automatic tools that help law enforcement agencies detect such criminal activity. In this work, we present Amatriciana, a nove...

Unlearning Inversion Attacks for Graph Neural Networks

Graph unlearning methods aim to efficiently remove the impact of sensitive data from trained GNNs without full retraining, assuming that deleted information cannot be recovered. In this work, we challenge this assumption by introducing the graph unlearning inversion attack: given only black-box...

The Cost of Restaking Vs. Proof-Of-Stake

We compare the efficiency of restaking and Proof-of-Stake PoS protocols in terms of stake requirements. First, we consider the sufficient condition for the restaking graph to be secure. We show that the condition implies that it is always possible to transform such a restaking graph into secure P...