arXiv subjects

Christos Anagnostopoulos

Publications and source records attributed to Christos Anagnostopoulos.

At least 19 recordsLinked to original sources

arXiv2026

An Empirical Study of Observability Limits in Advanced Software Supply Chain Attacks

Advanced software supply chain (SSC) attacks are increasingly runtime-only and leave fragmented evidence across hosts, services, and build/dependency layers, making any single telemetry stream insufficient for chain reconstruction. Despite this, no existing dataset provides multi-source runtime monitoring data with end-to-end chain-level ground truth for SSC attacks, leaving the observability limits of such attacks poorly understood. We present SynthChain, a multi-source runtime dataset with chain-level ground truth derived from real-world malicious packages and exploit campaigns, and use it to empirically study observability limits in SSC attacks. SynthChain covers seven representative SSC exploit scenarios across PyPI, npm, and C++ supply chains, spanning Windows, Linux, and containerized environments, with annotations for 14 MITRE ATT&CK tactics, 161 techniques, and 2,919 manually verified Indicators of Compromise (IOC) annotations across 22 log files spanning 11 telemetry types. Our observability analysis shows that no single source is chain-complete: even the best single source recovers fewer than 40% of expected attack steps. Fusing just two complementary sources improves reconstruction by roughly $1.6\times$, but gains depend on which sources are combined rather than how many. We identify three systematic failure modes---missing-phase gaps, attribution breaks, and ambiguity---and derive telemetry planning guidelines that do not require prior knowledge of specific attacks. A preliminary sensitivity analysis confirms that the multi-source advantage persists under reduced per-source sampling rates. The corpus (approximately 0.59 million events) is released with ground truth and artifacts to support reproducible evaluation of runtime SSC defenses.

cs.CR↗

arXiv2026

Fast and Accurate Outlier-Aware LiDAR Super-Resolution for SLAM Applications

This work tackles the challenge of enhancing low-resolution LiDAR sensors for SLAM applications through a novel Deep Unrolling-based Super-Resolution (SR) model. We integrate an outlier removal module to ensure structural integrity while maintaining real-time performance. By leveraging a model-based optimization approach, our method efficiently reconstructs high-resolution point clouds while minimizing computational overhead. The proposed SR model is evaluated within a LiDAR SLAM framework, demonstrating significant improvements in pose estimation accuracy and efficiency compared to state-of-the-art SR methods.

cs.RO↗

arXiv2026

MCP-SandboxScan: WASM-based Secure Execution and Runtime Analysis for MCP Tools

Tool-augmented Large Language Model (LLM) agents create a new supply-chain surface: Model Context Protocol (MCP) tools are installed like third-party packages, yet their outputs can enter the agent's reasoning context. This enables confused-deputy risks in which attacker-controlled inputs cause otherwise benign tools to exercise legitimate authority over files, environment variables, or network-facing operations and reflect sensitive or instruction-like content into LLM-visible fields. We present SandScope, an MCP-aware audit framework that combines runtime witness detection with semantic tool profiling. SandScope executes portable tools under WebAssembly System Interface (WASI) or drives unmodified MCP servers over standard input/output (stdio), extracts LLM-visible sinks from tool results and prompt/message fields, and reports auditable source-to-sink witnesses from environment, file, and tool-input sources while separately recording network-intent and egress evidence. Its semantic layer recovers declared capabilities from tools/list metadata and static registrations to characterize attack surface when execution is incomplete. We evaluate SandScope on controlled cross-language subjects, an evasion benchmark, and a 100-repository MCP corpus. SandScope completes shallow dynamic scans for 35 repositories and, through a broader semantic profiling pass, recovers metadata for 1,127 tools across 71 repositories, including 886 tools with security-sensitive declared capabilities. A schema-guided exploration pass over the 35 dynamically scanned repositories re-executes 33 and observes source-to-sink witnesses in 12. These results show that SandScope provides practical, auditable evidence for MCP tool risk through controlled execution, MCP-aware sink extraction, runtime witness reporting, and semantic capability profiling.

cs.CR↗

arXiv2026

FuseChain: Runtime Evidence Reconstruction for Software Supply-Chain Attacks

Software supply-chain (SSC) attacks are increasingly multi-stage, cross-source, and temporally distributed. A single attack campaign may leave weak and fragmented traces across multi-source telemetry that captures different granularities and perspectives of runtime behavior. Existing runtime detection systems often analyze these sources independently, making it difficult to identify low-frequency attack evidence or reconstruct the temporal context in which it appears. We present FUSECHAIN, a runtime detection framework that represents multi-source software supply-chain telemetry as a temporal heterogeneous provenance graph over a unified event-time axis. By aligning package/runtime traces, process events, network telemetry, DNS/HTTP metadata, and security alerts on a unified temporal graph, FuseChain captures cross-source dependencies and sparse attack evidence that may be ambiguous within any individual source. It learns anomaly-centric temporal representations from benign-prefix telemetry and performs deployable attack-stage reconstruction through a lightweight decoder on top of a frozen anomaly backbone. Our experiments show that jointly optimizing anomaly detection and stage prediction is ineffective under sparse and imbalanced runtime supply-chain telemetry. Across seven SSC attack scenarios, FuseChain improves deployable stage reconstruction from 0.369 to 0.881 Stage Recall@500 with a frozen-backbone decoder, while adaptive retrieval further increases observable-stage recall from 0.524 to 0.655 without modifying the detector. These results highlight the deployable value of decoupling runtime SSC anomaly detection from downstream attack-stage interpretation.

cs.CR↗

arXiv2026

Operational Runtime Behavior Mining for Open-Source Supply Chain Security

Open-source software (OSS) is a critical component of modern software systems, yet supply chain security remains challenging in practice due to unavailable or obfuscated source code. Consequently, security teams often rely on runtime observations collected from sandboxed executions to investigate suspicious third-party components. We present HeteroGAT-Rank, an industry-oriented runtime behavior mining system that supports analyst-in-the-loop supply chain threat investigation. The system models execution-time behaviors of OSS packages as lightweight heterogeneous graphs and applies attention-based graph learning to rank behavioral patterns that are most relevant for security analysis. Rather than aiming for fully automated detection, HeteroGAT-Rank surfaces actionable runtime signals - such as file, network, and command activities - to guide manual investigation and threat hunting. To operate at ecosystem scale, the system decouples offline behavior mining from online analysis and integrates parallel graph construction for efficient processing across multiple ecosystems. An evaluation on a large-scale OSS execution dataset shows that HeteroGAT-Rank effectively highlights meaningful and interpretable behavioral indicators aligned with real-world vulnerability and attack trends, supporting practical security workflows under realistic operational constraints.

cs.CR↗

arXiv2026

Attesting LLM Pipelines: Enforcing Verifiable Training and Release Claims

Modern Large Language Model (LLM) systems are assembled from third-party artifacts such as pre-trained weights, fine-tuning adapters, datasets, dependency packages, and container images, fetched through automated pipelines. This speed comes with supply-chain risks, including compromised dependencies, malicious hub artifacts, unsafe deserialization, forged provenance, and backdoored models. A core gap is that training and release claims (e.g., data and code lineage, build environment, and security scanning results) are rarely cryptographically bound to the artifacts they describe, making enforcement inconsistent across teams and stages. We propose an attestation-aware promotion gate: before an artifact is admitted into trusted environments (training, fine-tuning, deployment), the gate verifies claim evidence, enforces safe loading and static scanning policies, and applies secure-by-default deployment constraints. When organizations operate runtime security tooling, the same gate can optionally ingest standardized dynamic signals via plugins to reduce uncertainty for high-risk artifacts. We outline a practical claims-to-controls mapping and an evaluation blueprint using representative supply-chain scenarios and operational metrics (coverage and decisions), charting a path toward a full research paper.

cs.CR↗

arXiv2026

An arithmetic method algorithm optimizing k-nearest neighbors compared to regression algorithms and evaluated on real world data sources

Linear regression analysis focuses on predicting a numeric regressand value based on certain regressor values. In this context, k-Nearest Neighbors (k-NN) is a common non-parametric regression algorithm, which achieves efficient performance when compared with other algorithms in literature. In this research effort an optimization of the k-NN algorithm is proposed by exploiting the potentiality of an introduced arithmetic method, which can provide solutions for linear equations involving an arbitrary number of real variables. Specifically, an Arithmetic Method Algorithm (AMA) is adopted to assess the efficiency of the introduced arithmetic method, while an Arithmetic Method Regression (AMR) algorithm is proposed as an optimization of k-NN adopting the potentiality of AMA. Such algorithm is compared with other regression algorithms, according to an introduced optimal inference decision rule, and evaluated on certain real world data sources, which are publicly available. Results are promising since the proposed AMR algorithm has comparable performance with the other algorithms, while in most cases it achieves better performance than the k-NN. The output results indicate that introduced AMR is an optimization of k-NN.

cs.LG↗

arXiv2026

Multi-Partner Project: COIN-3D -- Collaborative Innovation in 3D VLSI Reliability

As semiconductor manufacturing advances from the 3-nm process toward the sub-nanometer regime and transitions from FinFETs to gate-all-around field-effect transistors (GAAFETs), the resulting complexity and manufacturing challenges continue to increase. In this context, 3D chiplet-based approaches have emerged as key enablers to address these limitations while exploiting the expanded design space. Specifically, chiplets help address the lower yields typically associated with large monolithic designs. This paradigm enables the modular design of heterogeneous systems consisting of multiple chiplets (e.g., CPUs, GPUs, memory) fabricated using different technology nodes and processes. Consequently, it offers a capable and cost-effective strategy for designing heterogeneous systems. This paper introduces the Horizon Europe Twinning project COIN-3D (Collaborative Innovation in 3D VLSI Reliability), which aims to strengthen research excellence in 2.5D/3D VLSI systems reliability through collaboration between leading European institutions. More specifically, our primary scientific goal is the provision of novel open-source Electronic Design Automation (EDA) tools for reliability assessment of 3D systems, integrating advanced algorithms for physical- and system-level reliability analysis.

cs.AR↗

arXiv2025

STaRFormer: Semi-Supervised Task-Informed Representation Learning via Dynamic Attention-Based Regional Masking for Sequential Data

Understanding user intent is essential for situational and context-aware decision-making. Motivated by a real-world scenario, this work addresses intent predictions of smart device users in the vicinity of vehicles by modeling sequential spatiotemporal data. However, in real-world scenarios, environmental factors and sensor limitations can result in non-stationary and irregularly sampled data, posing significant challenges. To address these issues, we propose STaRFormer, a Transformer-based approach that can serve as a universal framework for sequential modeling. STaRFormer utilizes a new dynamic attention-based regional masking scheme combined with a novel semi-supervised contrastive learning paradigm to enhance task-specific latent representations. Comprehensive experiments on 56 datasets varying in types (including non-stationary and irregularly sampled), tasks, domains, sequence lengths, training samples, and applications demonstrate the efficacy of STaRFormer, achieving notable improvements over state-of-the-art approaches.

cs.LG↗

arXiv2025

Multi-Modal Robust Enhancement for Coastal Water Segmentation: A Systematic HSV-Guided Framework

Coastal water segmentation from satellite imagery presents unique challenges due to complex spectral characteristics and irregular boundary patterns. Traditional RGB-based approaches often suffer from training instability and poor generalization in diverse maritime environments. This paper introduces a systematic robust enhancement framework, referred to as Robust U-Net, that leverages HSV color space supervision and multi-modal constraints for improved coastal water segmentation. Our approach integrates five synergistic components: HSV-guided color supervision, gradient-based coastline optimization, morphological post-processing, sea area cleanup, and connectivity control. Through comprehensive ablation studies, we demonstrate that HSV supervision provides the highest impact (0.85 influence score), while the complete framework achieves superior training stability (84\% variance reduction) and enhanced segmentation quality. Our method shows consistent improvements across multiple evaluation metrics while maintaining computational efficiency. For reproducibility, our training configurations and code are available here: https://github.com/UofgCoastline/ICASSP-2026-Robust-Unet.

cs.CV↗

arXiv2025

A Risk-aware Spatial-temporal Trajectory Planning Framework for Autonomous Vehicles Using QP-MPC and Dynamic Hazard Fields

Trajectory planning is a critical component in ensuring the safety, stability, and efficiency of autonomous vehicles. While existing trajectory planning methods have achieved progress, they often suffer from high computational costs, unstable performance in dynamic environments, and limited validation across diverse scenarios. To overcome these challenges, we propose an enhanced QP-MPC-based framework that incorporates three key innovations: (i) a novel cost function designed with a dynamic hazard field, which explicitly balances safety, efficiency, and comfort; (ii) seamless integration of this cost function into the QP-MPC formulation, enabling direct optimization of desired driving behaviors; and (iii) extensive validation of the proposed framework across complex tasks. The spatial safe planning is guided by a dynamic hazard field (DHF) for risk assessment, while temporal safe planning is based on a space-time graph. Besides, the quintic polynomial sampling and sub-reward of comforts are used to ensure comforts during lane-changing. The sub-reward of efficiency is used to maintain driving efficiency. Finally, the proposed DHF-enhanced objective function integrates multiple objectives, providing a proper optimization tasks for QP-MPC. Extensive simulations demonstrate that the proposed framework outperforms benchmark optimization methods in terms of efficiency, stability, and comfort across a variety of scenarios likes lane-changing, overtaking, and crossing intersections.

cs.RO↗

arXiv2025

Integrated Simulation Framework for Adversarial Attacks on Autonomous Vehicles

Autonomous vehicles (AVs) rely on complex perception and communication systems, making them vulnerable to adversarial attacks that can compromise safety. While simulation offers a scalable and safe environment for robustness testing, existing frameworks typically lack comprehensive supportfor modeling multi-domain adversarial scenarios. This paper introduces a novel, open-source integrated simulation framework designed to generate adversarial attacks targeting both perception and communication layers of AVs. The framework provides high-fidelity modeling of physical environments, traffic dynamics, and V2X networking, orchestrating these components through a unified core that synchronizes multiple simulators based on a single configuration file. Our implementation supports diverse perception-level attacks on LiDAR sensor data, along with communication-level threats such as V2X message manipulation and GPS spoofing. Furthermore, ROS 2 integration ensures seamless compatibility with third-party AV software stacks. We demonstrate the framework's effectiveness by evaluating the impact of generated adversarial scenarios on a state-of-the-art 3D object detector, revealing significant performance degradation under realistic conditions.

cs.CR↗

arXiv2025

A holistic perception system of internal and external monitoring for ground autonomous vehicles: AutoTRUST paradigm

This paper introduces a holistic perception system for internal and external monitoring of autonomous vehicles, with the aim of demonstrating a novel AI-leveraged self-adaptive framework of advanced vehicle technologies and solutions that optimize perception and experience on-board. Internal monitoring system relies on a multi-camera setup designed for predicting and identifying driver and occupant behavior through facial recognition, exploiting in addition a large language model as virtual assistant. Moreover, the in-cabin monitoring system includes AI-empowered smart sensors that measure air-quality and perform thermal comfort analysis for efficient on and off-boarding. On the other hand, external monitoring system perceives the surrounding environment of vehicle, through a LiDAR-based cost-efficient semantic segmentation approach, that performs highly accurate and efficient super-resolution on low-quality raw 3D point clouds. The holistic perception framework is developed in the context of EU's Horizon Europe programm AutoTRUST, and has been integrated and deployed on a real electric vehicle provided by ALKE. Experimental validation and evaluation at the integration site of Joint Research Centre at Ispra, Italy, highlights increased performance and efficiency of the modular blocks of the proposed perception architecture.

cs.RO↗

arXiv2025

FedPhD: Federated Pruning with Hierarchical Learning of Diffusion Models

Federated Learning (FL), as a distributed learning paradigm, trains models over distributed clients' data. FL is particularly beneficial for distributed training of Diffusion Models (DMs), which are high-quality image generators that require diverse data. However, challenges such as high communication costs and data heterogeneity persist in training DMs similar to training Transformers and Convolutional Neural Networks. Limited research has addressed these issues in FL environments. To address this gap and challenges, we introduce a novel approach, FedPhD, designed to efficiently train DMs in FL environments. FedPhD leverages Hierarchical FL with homogeneity-aware model aggregation and selection policy to tackle data heterogeneity while reducing communication costs. The distributed structured pruning of FedPhD enhances computational efficiency and reduces model storage requirements in clients. Our experiments across multiple datasets demonstrate that FedPhD achieves high model performance regarding Fréchet Inception Distance (FID) scores while reducing communication costs by up to $88\%$. FedPhD outperforms baseline methods achieving at least a $34\%$ improvement in FID, while utilizing only $56\%$ of the total computation and communication resources.

cs.LG↗

arXiv2025

Distributed Temporal Graph Learning with Provenance for APT Detection in Supply Chains

Cyber supply chain, encompassing digital asserts, software, hardware, has become an essential component of modern Information and Communications Technology (ICT) provisioning. However, the growing inter-dependencies have introduced numerous attack vectors, making supply chains a prime target for exploitation. In particular, advanced persistent threats (APTs) frequently leverage supply chain vulnerabilities (SCVs) as entry points, benefiting from their inherent stealth. Current defense strategies primarly focus on prevention through blockchain for integrity assurance or detection using plain-text source code analysis in open-source software (OSS). However, these approaches overlook scenarios where source code is unavailable and fail to address detection and defense during runtime. To bridge this gap, we propose a novel approach that integrates multi-source data, constructs a comprehensive dynamic provenance graph, and detects APT behavior in real time using temporal graph learning. Given the lack of tailored datasets in both industry and academia, we also aim to simulate a custom dataset by replaying real-world supply chain exploits with multi-source monitoring.

cs.CR↗

arXiv2025

Distributed Log-driven Anomaly Detection System based on Evolving Decision Making

Effective anomaly detection from logs is crucial for enhancing cybersecurity defenses by enabling the early identification of threats. Despite advances in anomaly detection, existing systems often fall short in areas such as post-detection validation, scalability, and effective maintenance. These limitations not only hinder the detection of new threats but also impair overall system performance. To address these challenges, we propose CEDLog, a novel practical framework that integrates Elastic Weight Consolidation (EWC) for continual learning and implements distributed computing for scalable processing by integrating Apache Airflow and Dask. In CEDLog, anomalies are detected through the synthesis of Multi-layer Perceptron (MLP) and Graph Convolutional Networks (GCNs) using critical features present in event logs. Through comparisons with update strategies on large-scale datasets, we demonstrate the strengths of CEDLog, showcasing efficient updates and low false positives

cs.CR↗

arXiv2025

Artificial Intelligence-Driven Clinical Decision Support Systems

As artificial intelligence (AI) becomes increasingly embedded in healthcare delivery, this chapter explores the critical aspects of developing reliable and ethical Clinical Decision Support Systems (CDSS). Beginning with the fundamental transition from traditional statistical models to sophisticated machine learning approaches, this work examines rigorous validation strategies and performance assessment methods, including the crucial role of model calibration and decision curve analysis. The chapter emphasizes that creating trustworthy AI systems in healthcare requires more than just technical accuracy; it demands careful consideration of fairness, explainability, and privacy. The challenge of ensuring equitable healthcare delivery through AI is stressed, discussing methods to identify and mitigate bias in clinical predictive models. The chapter then delves into explainability as a cornerstone of human-centered CDSS. This focus reflects the understanding that healthcare professionals must not only trust AI recommendations but also comprehend their underlying reasoning. The discussion advances in an analysis of privacy vulnerabilities in medical AI systems, from data leakage in deep learning models to sophisticated attacks against model explanations. The text explores privacy-preservation strategies such as differential privacy and federated learning, while acknowledging the inherent trade-offs between privacy protection and model performance. This progression, from technical validation to ethical considerations, reflects the multifaceted challenges of developing AI systems that can be seamlessly and reliably integrated into daily clinical practice while maintaining the highest standards of patient care and data protection.

cs.AI↗

arXiv2025

Federated Data-Driven Kalman Filtering for State Estimation

This paper proposes a novel localization framework based on collaborative training or federated learning paradigm, for highly accurate localization of autonomous vehicles. More specifically, we build on the standard approach of KalmanNet, a recurrent neural network aiming to estimate the underlying system uncertainty of traditional Extended Kalman Filtering, and reformulate it by the adapt-then-combine concept to FedKalmanNet. The latter is trained in a distributed manner by a group of vehicles (or clients), with local training datasets consisting of vehicular location and velocity measurements, through a global server aggregation operation. The FedKalmanNet is then used by each vehicle to localize itself, by estimating the associated system uncertainty matrices (i.e, Kalman gain). Our aim is to actually demonstrate the benefits of collaborative training for state estimation in autonomous driving, over collaborative decision-making which requires rich V2X communication resources for measurement exchange and sensor fusion under real-time constraints. An extensive experimental and evaluation study conducted in CARLA autonomous driving simulator highlights the superior performance of FedKalmanNet over state-of-the-art collaborative decision-making approaches, in localizing vehicles without the need of real-time V2X communication.

cs.RO↗