Insider-Threat

Модели графов для угроз изнутри R5.2 и R6.2 Этот репозиторий содержит подготовку данных, извлечение признаков и графовые модели GCN-NN, GCN-BiLSTM и GCN-BiLSTM+Attention для неявных+явных сигналов для обнаружения угроз изнутри с использованием набора данных CMU CERT Insider Threat Test Dataset...

Behavior-Centric Malware Classification with Fine-Grained Malicious Logic Localization

Effective malware analysis requires understanding not only whether a program is malicious, but also which behaviors it exhibits and where those behaviors originate in the code. Existing machine-learning-based malware detectors largely operate as black boxes, providing limited insight into the...

Residual Community Prototypes Under-Reject Held-Out Malware Families in FCG-MFD

Open-set malware-family recognition must classify known families while rejecting families absent from training. We test whether Louvain-community summaries add rejection information beyond a graph neural network embedding and dimension-matched generic topology. The study uses a deduplicated,...

Enhancing Web Application Firewalls with BERT-GNN for SQL Injection Detection

Detecting sophisticated SQL Injection SQLi attacks remains among the most critical challenges in web applications security. This research study has resulted in an optimised hybrid BERT-GNN pipeline with improved detection accuracy and robustness while reducing false-positive and false-negative...

C-RE-ACT: Causal RE-ACTing Agent for O-RAN Forensic Triage

The shift to O-RAN architectures marks a turning point in cellular security, where increased openness and modularity directly translate into a broader attack surface. Among the security threats cataloged by the O-RAN Alliance Working Group 11, performance-degradation attacks constitute the larges...

LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection

Malicious Python packages have become a major threat to software supply chain ecosystems due to the widespread adoption of open-source repositories such as PyPI. Existing learning-based detection methods struggle to capture the hierarchical organization and heterogeneous interactions among...

Software Vulnerability Detection Using a Lightweight Graph Neural Network

Large Language Models LLMs have emerged as a popular choice in vulnerability detection studies given their foundational capabilities, open source availability, and variety of models, but have limited scalability due to extensive compute requirements. Using the natural graph relational structure o...

TrojanGYM: A Detector-In-The-Loop LLM for Adaptive RTL Hardware Trojan Insertion

Hardware Trojans HTs remain a critical threat because learning-based detectors often overfit to narrow trigger/payload patterns and small, stylized benchmarks. We introduce TrojanGYM, an agentic, LLM-driven framework that automatically curates HT insertions to expose detector blind spots while...

Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection Via Latent Space Representation Learning and Alignment

Although AI-based models have achieved high accuracy in IoT threat detection, their deployment in enterprise environments is constrained by reliance on stationary datasets that fail to reflect the dynamic nature of real-world IoT NetFlow traffic, which is frequently affected by concept drift...

IoT-Based Android Malware Detection Using Graph Neural Network with Adversarial Defense

Since the Internet of Things IoT is widely adopted using Android applications, detecting malicious Android apps is essential. In recent years, Android graph-based deep learning research has proposed many approaches to extract relationships from applications as graphs to generate graph embeddings...

RoBCtrl: Attacking GNN-Based Social Bot Detectors Via Reinforced Manipulation of Bots Control Interaction

Social networks have become a crucial source of real-time information for individuals. The influence of social bots within these platforms has garnered considerable attention from researchers, leading to the development of numerous detection technologies. However, the vulnerability and robustness...

EvoMail: Self-Evolving Cognitive Agents for Adaptive Spam and Phishing Email Defense

Modern email spam and phishing attacks have evolved far beyond keyword blacklists or simple heuristics. Adversaries now craft multi-modal campaigns that combine natural-language text with obfuscated URLs, forged headers, and malicious attachments, adapting their strategies within days to bypass...

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...

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...

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...

Graph Privacy: a Heterogeneous Federated GNN for Trans-Border Financial Data Circulation

The sharing of external data has become a strong demand of financial institutions, but the privacy issue has led to the difficulty of interconnecting different platforms and the low degree of data openness. To effectively solve the privacy problem of financial data in trans-border flow and sharin...

Dual Explanations Via Subgraph Matching for Malware Detection

Interpretable malware detection is crucial for understanding harmful behaviors and building trust in automated security systems. Traditional explainable methods for Graph Neural Networks GNNs often highlight important regions within a graph but fail to associate them with known benign or maliciou...

On the Consistency of GNN Explanations for Malware Detection

Control Flow Graphs CFGs are critical for analyzing program execution and characterizing malware behavior. With the growing adoption of Graph Neural Networks GNNs, CFG-based representations have proven highly effective for malware detection. This study proposes a novel framework that dynamically...

Clustering and Analysis of User Behaviour in Blockchain: a Case Study of Planet IX

Decentralised applications dApps that run on public blockchains have the benefit of trustworthiness and transparency as every activity that happens on the blockchain can be publicly traced through the transaction data. However, this introduces a potential privacy problem as this data can be track...