惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

博客园 - Franky
N
Netflix TechBlog - Medium
宝玉的分享
宝玉的分享
Google DeepMind News
Google DeepMind News
腾讯CDC
G
Google Developers Blog
Martin Fowler
Martin Fowler
Microsoft Security Blog
Microsoft Security Blog
Recent Announcements
Recent Announcements
爱范儿
爱范儿
Engineering at Meta
Engineering at Meta
Microsoft Azure Blog
Microsoft Azure Blog
A
About on SuperTechFans
aimingoo的专栏
aimingoo的专栏
有赞技术团队
有赞技术团队
Jina AI
Jina AI
人人都是产品经理
人人都是产品经理
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
M
MIT News - Artificial intelligence
罗磊的独立博客
博客园 - 三生石上(FineUI控件)
美团技术团队
WordPress大学
WordPress大学
阮一峰的网络日志
阮一峰的网络日志

math.PR updates on arXiv.org

Visibility in the Boolean Model on Harmonic Manifolds Global estimates on the Brenier map Geodesics and Wandering Exponents in Brochette First-Passage Percolation State-dependent inverse-subordinator time changes of regenerative processes: Excursion structure and multiscale occupation-time limits Randomly twisted transfer operators and singular values statistics Generalized Bessel-Dunkl diffusions An almost sure invariance principle for the Takagi-van der Waerden class functions Central limit theorems for high dimensional lattice polytopes: cosmological polytopes Convergence rate estimates for semigroups and heat kernels associated with resistance forms Second-order Poincaré inequalities and localization on the Poisson space Maximum Probability of Independence in Transitive Matroids On global solutions to the semidiscrete stochastic heat equation The Poisson Tail Conjecture for primes in short intervals A Complete Spectral Analysis of the CEV Operator with Applications to Arbitrage Holographic functions and neural networks From Betting to Empirical Bernstein LIL Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise Pointwise Generalization in Deep Neural Networks Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors Wasserstein bounds for denoising diffusion probabilistic models via the Föllmer process A note on connections between the Föllmer process and the denoising diffusion probabilistic model Simple Approximation and Derivative Free Inference-Time Scaling for Diffusion Models via Sequential Monte Carlo on Path Measures Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations A Fourier perspective on the learning dynamics of neural networks: from sample complexities to mechanistic insights Propagation of Chaos in Contextual Flow Maps Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures $α$-TCAV: A Unified Framework for Testing with Concept Activation Vectors Scaling Laws from Sequential Feature Recovery: A Solvable Hierarchical Model On the Limits of Latent Reuse in Diffusion Models State-of-art minibatches via novel DPP kernels: discretization, wavelets, and rough objectives
AMON: An Open Source Architecture for Online Monitoring, ...
Michael Kallitsis, Stilian Stoev, Shrijita Bhattacharya, George · 2015-09-01 · via math.PR updates on arXiv.org

The Internet, as a global system of interconnected networks, carries an extensive array of information resources and services. Key requirements include good quality-of-service and protection of the infrastructure from nefarious activity (e.g. distributed denial of service--DDoS--attacks). Network monitoring is essential to network engineering, capacity planning and prevention / mitigation of threats. We develop an open source architecture, AMON (All-packet MONitor), for online monitoring and analysis of multi-gigabit network streams. It leverages the high-performance packet monitor PF RING and is readily deployable on commodity hardware. AMON examines all packets, partitions traffic into sub-streams by using rapid hashing and computes certain real-time data products. The resulting data structures provide views of the intensity and connectivity structure of network traffic at the time-scale of routing. The proposed integrated framework includes modules for the identification of heavy-hitters as well as for visualization and statistical detection at the time-of-onset of high impact events such as DDoS. This allows operators to quickly visualize and diagnose attacks, and limit offline and time consuming post-mortem analysis. We demonstrate our system in the context of real-world attack incidents, and validate it against state-of-the-art alternatives. AMON has been deployed and is currently processing 10Gbps+ live Internet traffic at Merit Network. It is extensible and allows the addition of further statistical and filtering modules for real-time forensics.