A graph convolutional network (GCN)-based approach is developed for anomaly detection in power dispatch data networks, addressing the challenges of high-dimensional, non-Euclidean, and dynamic grid environments. The method begins with the construction of a graph that encodes both physical connectivity and operational i...
The proposed ARIMA-LSTM hybrid framework offers a robust performance and solution characterized by interpretability, scalability, and accuracy that make it an ideal system for real-time, high-stakes anomaly detection in dynamic streaming environments.
Computer network security is a critical concern in the era of increasing cyber threats. Traditional intrusion detection systems rely on handcrafted rules and statistical features that fail to capture the complex topological relationships inherent in network traffic. This paper proposes a graph neural network (GNN)-base...
A graph-based anomaly detection model tailored for power generation systems using multivariate time-series data that incorporates both spatial and temporal dependencies is presented, demonstrating the advantages of incorporating both spatial and temporal dependencies.
Modern vehicles rely on Controller Area Network (CAN) communication, where detecting anomalies in multivariate sensor data is vital for safety and reliability. To capture diverse anomalies in multivariate time series, models must learn both temporal and spatial dependencies. Graph neural network (GNN)-based approaches...
When edge nodes undertake the task of near-source traffic monitoring in the Internet of Things (iot), limited computing resources and the difference of abnormal behavior time span are easy to cause local mutation and insufficient information representation of continuous attack. To solve this problem, this paper constru...
A cross-section of the MIT community convened discussions on topics ranging from data centers and super pollutants to agriculture and education.
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
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