Search: “cybersecurity”

arXiv:2609.08040v1 Announce Type: cross Abstract: The software supply chain has become an increasingly exposed attack surface because of its reliance on intricate yet fragile dependencies. Existing de

arXiv:2609.03189v1 Announce Type: cross Abstract: This article presents a structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artific

arXiv:2609.02965v1 Announce Type: cross Abstract: Dynamic networks are being applied in many domains, from social media to logistics systems, each with their own set of special characteristics. A mode

arXiv:2609.01836v1 Announce Type: cross Abstract: Long-running LLM agents rely on persistent memory to carry state across interactions, including permissions, restrictions, and revocations. When memor

arXiv:2511.09454v2 Announce Type: replace-cross Abstract: As algorithms increasingly mediate competitive decision-making, their influence extends beyond individual outcomes to shaping strategic market

arXiv:2602.17826v2 Announce Type: replace Abstract: Language models exhibit fundamental limitations -- hallucination, brittleness, and lack of formal grounding -- that are particularly problematic in

arXiv:2608.29529v1 Announce Type: cross Abstract: Semantic alignment between specialized normative texts is challenging when equivalent requirements use different terms, syntax, and levels of abstract

arXiv:2608.28394v1 Announce Type: cross Abstract: Cyber threat intelligence (CTI) is foundational to modern cyber defense, yet much of it resides in unstructured reports whose volume and heterogeneity

arXiv:2608.26237v1 Announce Type: cross Abstract: Capture-the-Flag (CTF) benchmarks are widely used to assess the offensive security capabilities of autonomous language-model agents. Evaluations rely

arXiv:2608.27102v1 Announce Type: new Abstract: Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authorita

arXiv:2608.24937v1 Announce Type: new Abstract: Multi-Modal Anomaly Detection (MMAD) detects rare abnormal events from heterogeneous data sources and is increasingly used in safety- and reliability-cr

arXiv:2608.23817v1 Announce Type: new Abstract: SHAP and LIME are now standard tools for interpreting black-box predictions, yet their outputs can vary substantially when the input is perturbed by sma

arXiv:2608.23893v1 Announce Type: new Abstract: Learning systems deployed over long periods must adapt not only to statistical changes in incoming data, but also to revisions of the definitions that g

arXiv:2608.21389v1 Announce Type: cross Abstract: Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject stu

arXiv:2608.22607v1 Announce Type: new Abstract: Financial fraud detection relies heavily on centralized machine learning models. This creates serious data privacy risks. Federated Learning (FL) decent

arXiv:2608.23468v1 Announce Type: new Abstract: Anomaly detection is often applied to data stored in relational databases, yet most existing methods require flattening multiple tables into a single fe

arXiv:2608.20421v1 Announce Type: cross Abstract: Large language models (LLMs) are now embedded in scientific, educational, and governance workflows, with debates centering on their capabilities, mech

arXiv:2608.20963v1 Announce Type: cross Abstract: Large language models increasingly generate complete web applications from natural-language prompts, raising the question of whether explicitly reques

arXiv:2608.19938v1 Announce Type: cross Abstract: Existing approaches to anomalous behaviour log detection, such as Wazuh rely primarily on predefined detection rules, while statistical anomaly detect

arXiv:2608.17093v1 Announce Type: cross Abstract: Existing automotive intrusion detection systems (IDSs) for the Controller Area Network (CAN) largely target discrepancies in message timing, frequency

arXiv:2509.10818v2 Announce Type: replace Abstract: When consequential decisions depend on knowledge that exists nowhere in writing, LLMs hallucinate not from retrieval failure but from model absence.

arXiv:2608.16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for rea

arXiv:2608.14036v1 Announce Type: new Abstract: Skills have emerged as a practical and effective approach for enhancing LLM agents at inference time through structured packages of knowledge. However,

arXiv:2608.13608v1 Announce Type: new Abstract: Agentic 'Continual Learning Harnesses', systems that pair an LLM with retrieval or memory to improve from feedback without retraining, have shown growin

arXiv:2608.12977v1 Announce Type: cross Abstract: The expanding operational capabilities of large language model (LLM) agents introduce sophisticated security threats. Runtime defenses have emerged as

arXiv:2608.11891v1 Announce Type: cross Abstract: Governments increasingly fund indigenous foundation models to strengthen national AI capability, digital sovereignty, and multilingual computing. Asse

arXiv:2608.12190v1 Announce Type: cross Abstract: With the increasing complexity of cyber assaults in cloud environments, adaptable security solutions are needed that can support real-time detection a

arXiv:2608.10644v1 Announce Type: new Abstract: Extraction produces candidate entities and relationships; writing them into a graph is where identity is decided, and identity decisions are destructive

arXiv:2608.07226v1 Announce Type: cross Abstract: Compact AI systems make local language-model experimentation increasingly accessible, yet practical evidence for multi-node training on desktop-class

arXiv:2608.04317v1 Announce Type: cross Abstract: Autonomous cyber defense systems based on Deep Reinforcement Learning (DRL) have attracted significant research attention, yet remain evaluated almost

arXiv:2505.23397v3 Announce Type: replace Abstract: This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibr

arXiv:2608.03695v1 Announce Type: cross Abstract: This work addresses community detection in temporal networks through GPU-accelerated extensions of spectral clustering and modularity-based algorithms

arXiv:2608.03591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to

arXiv:2608.02472v1 Announce Type: new Abstract: Trust is fundamental in modern regulatory ecosystems, and compliance checking plays a critical role in fostering that trust. Regulatory compliance verif

arXiv:2607.18295v2 Announce Type: replace Abstract: We study whether alignment schemes that reshape a base model's output distribution, combined with bounded safety filters, can drive the probability

arXiv:2607.29553v1 Announce Type: new Abstract: Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metri

arXiv:2607.28879v1 Announce Type: cross Abstract: Similarity search over sparse set-valued data is often dominated by frequent background attributes because classical measures such as Jaccard, cosine,

arXiv:2607.28955v1 Announce Type: cross Abstract: AI-generated videos are becoming increasingly realistic and difficult to distinguish from authentic ones, which facilitates malicious misuse and poses

arXiv:2607.26069v1 Announce Type: cross Abstract: As AI systems are rapidly integrated into critical economic, governmental, and national security functions, the gap between AI adoption and AI securit

arXiv:2607.26791v1 Announce Type: cross Abstract: Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host artifacts and command-line interfaces

arXiv:2607.23197v1 Announce Type: new Abstract: Anomaly detection on multivariate sensor time series is critical for industrial monitoring of cyber-physical systems (CPS), where even subtle deviations

arXiv:2607.24551v1 Announce Type: new Abstract: Maintenance regulations are complex legal texts that are difficult to exploit when addressing a specific case and challenging to integrate into operatio

arXiv:2607.20255v1 Announce Type: cross Abstract: LLM-driven autonomous agents are reshaping offensive security. Unlike traditional penetration-testing tooling -- deterministic, narrowly scoped, and o

arXiv:2507.19593v3 Announce Type: replace Abstract: Classical game-theoretic models typically assume rational agents, complete information, and common knowledge of payoffs - assumptions that are often

arXiv:2607.13073v1 Announce Type: new Abstract: Neuro-symbolic AI based on IFOL_B is a way to combine neural learning and symbolic reasoning to overcome limitations of purely neural systems (like lack

arXiv:2607.12619v1 Announce Type: new Abstract: The rapid emergence of LLM-powered autonomous and semi-autonomous agents is reshaping software systems from static, request-response components into goa