Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, and Research Challenges
This survey provides a novel taxonomy for online anomaly detection in multivariate time series, distinguishing between online training and online inference. It presents an extensive overview and analysis of state-of-the-art model-based online semi- and unsupervised anomaly detection approaches, as well as the most popular benchmark data sets and evaluation metrics used in the literature.
Blockchain Amplification Attack: Exploiting Lax Transaction Validation to Disrupt Ethereum Network
An attacker can exploit modified Ethereum nodes that skip transaction validation to amplify invalid transactions, causing severe network disruption and economic damages.
Explainable Reasoning in 6G Networks: A Proof-of-Concept Study on Optimizing Radio Resource Allocation
The core message of this paper is to introduce a novel graph reinforcement learning (GRL) framework named TANGO that leverages a symbolic subsystem to provide explainable and trustworthy radio resource allocation in 6G networks.
Cooperative Behavior of Large Language Models in the Iterated Prisoner's Dilemma
Large Language Models (LLMs) tend to be more cooperative than typical human players in the Iterated Prisoner's Dilemma, with Llama2 and GPT3.5 exhibiting a stronger propensity for cooperation compared to Llama3.
Deterministic Identification Capacity of Memoryless Channels with Finite Output
The deterministic identification capacity of memoryless channels with finite output exhibits a superlinear scaling in the block length, bounded by the Minkowski dimension of the output probability set.
Enhancing Mobile Cloud Gaming Performance with 5G Standalone Network Telemetry
A telemetry-enhanced cloud gaming platform that leverages fine-grained 5G Standalone Radio Access Network capacity estimates to dynamically adapt video bitrate and frame rate, maximizing quality of experience.
Comprehensive Survey of Techniques for Effective Protocol Fuzzing
This survey provides a comprehensive overview of the unique challenges in protocol fuzzing and the techniques developed by existing works to address them, covering input generation, execution, and bug detection.
Graph Neural Networks: Advancing Intelligent Transportation Systems with Innovative Applications and Emerging Trends
Graph Neural Networks (GNNs) have emerged as a highly promising deep learning approach within the Intelligent Transportation Systems (ITS) domain, demonstrating excellent performance across various applications such as traffic forecasting, vehicle control, traffic signal control, transportation safety, demand prediction, and parking management.
개인 신경망 추론을 위한 합성곱 및 부트스트래핑의 공동 최적화: NeuJeans
NeuJeans는 FHE 기반 심층 합성곱 신경망 추론을 위한 알고리즘적 발전을 제안합니다. CinS 인코딩 방법과 최적화된 합성곱 알고리즘을 통해 FHE 기반 추론의 계산 비용을 크게 줄였습니다.
Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems
Magmaw, a novel wireless attack framework, can generate universal adversarial perturbations to subvert multimodal machine learning-based wireless communication systems and their downstream applications, even in the presence of strong defense mechanisms.
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