arXiv Machine Learning
Jun 17
Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation
arXiv:2606. 18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs.
PHASE: Passive Human Activity Simulation Evaluation
arXiv:2507. 13505v2 Announce Type: replace-cross Abstract: Cybersecurity simulation environments, such as cyber ranges, honeypots, and sandboxes, require realistic human behavior to be effective, yet no quantitative method exists to assess the behavioral fidelity of synthetic user personas.
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arXiv AI
Aug 17
Interactive Analysis of Global Explanations using Aggregated Class Activation Maps for Network Data
arXiv:2608. 13575v1 Announce Type: cross Abstract: Recent machine learning (ML) advances have demonstrated that deep learning (DL) achieves impressive results in different application domains, including the classification of computer network traffic to corresponding applications.
arXiv AI
3d ago
SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling
arXiv:2609.38397v1 Announce Type: new Abstract: Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation....
arXiv Machine Learning
Jul 1
Using AI Agents to Automate Black-Box Audits of Personalization Algorithms at Scale
arXiv:2606. 30801v1 Announce Type: cross Abstract: Personalization algorithms determine what content users encounter on online platforms.
arXiv AI
Sep 2
Measuring the Behavioral Fidelity of Long-Horizon Human Activity Simulations
arXiv:2609.01257v1 Announce Type: new Abstract: As LLM-based human simulators are increasingly used for policy, evaluation, and training, they must faithfully reproduce real behavioral patterns. Whil...
arXiv Machine Learning
Jun 15
Dynamic Free-Rider Detection in Federated Learning via Simulated Attack Patterns
arXiv:2604. 04611v2 Announce Type: replace Abstract: Federated learning (FL) enables multiple clients to collaboratively train a global model by aggregating local updates without sharing private data.
arXiv AI
Aug 11
Privacy-Preserving Data Drift Detection and Recovery for Large-Scale LLM Applications via Proxy Representations
arXiv:2608. 08245v1 Announce Type: cross Abstract: LLM applications deployed at scale face a fundamental challenge: privacy constraints prevent direct inspection of user interactions, making it difficult to obtain any representative evaluation dataset or to track the ongoing evolution of production traffic.
arXiv Machine Learning
Jul 2
Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intelligence
arXiv:2607. 00763v1 Announce Type: cross Abstract: Digital forensic investigations of network intrusions require analytical outputs that are traceable, reproducible, and court-defensible - requirements existing machine learning pipelines do not satisfy, since they treat original evidence as training data and produce opaque classifications without instance-level justification.
arXiv AI
Jul 7
FedAvg for HAR: Exploring the Tradeoff Between Personalized and Generalization Accuracy
arXiv:2607. 03334v1 Announce Type: cross Abstract: The federated learning (FL) paradigm fosters distributed pervasive computing combined with artificial intelligence techniques, allowing for optimized data usage and improved mitigation of privacy concerns.
arXiv Machine Learning
Sep 18
ZeroHAT: Behavior-Conditioned Zero-Shot Human Activity Trace Generation
ZeroHAT is a new framework for generating synthetic human activity traces (HATs) in a target region without any real data from that region. It transfers behavioral patterns learned from real HATs in source regions and adapts them using publicly available contextual information about the target region. The system includes a consistency-aware intent extractor, a cross-region behavioral cloning module, and a behavior-conditioned activity realization module, and it outperforms the strongest baseline by 4.5–6.4× in downstream utility and improves fidelity by 15.6–40.8% across ten cities.
arXiv AI
Jul 16
AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders
arXiv:2607. 13123v1 Announce Type: cross Abstract: Cybersecurity is the practice of protecting systems, networks, and data from digital attacks.
arXiv Machine Learning
Jul 22
Beyond Content: Behavioral Policies Reveal Actors in Information Operations
arXiv:2602. 02838v2 Announce Type: replace-cross Abstract: The detection of online influence operations -- coordinated campaigns by malicious actors to spread narratives -- has traditionally depended on content analysis or network features.