# unsupervised learning

Published articles for unsupervised learning.

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## Process Behaviour Anomaly Detection Using eBPF and Unsupervised-Learning Autoencoders

DevFeed: [Process Behaviour Anomaly Detection Using eBPF and Unsupervised-Learning Autoencoders](<https://devfeed.tech/articles/process-behaviour-anomaly-detection-using-ebpf-and-unsupervised-learning-autoencoders-41266.md>)

Original publisher: [Read original article](<https://www.evilsocket.net/2022/08/15/Process-behaviour-anomaly-detection-using-eBPF-and-unsupervised-learning-Autoencoders/>)

Author: Simone Margaritelli

Published: 2022-08-15T14:06:05Z

Content type: tutorial

Language: en

Sources: [evilsocket](<https://devfeed.tech/sources/evilsocket.md>)

Topics: [eBPF](<https://devfeed.tech/topics/ebpf.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [Linux Kernel](<https://devfeed.tech/topics/linux-kernel.md>), [Learning](<https://devfeed.tech/topics/learning.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [anomaly-detection](<https://devfeed.tech/tags/anomaly-detection.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [autoencoder](<https://devfeed.tech/tags/autoencoder.md>), [bcc](<https://devfeed.tech/tags/bcc.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [deep-neural-networks](<https://devfeed.tech/tags/deep-neural-networks.md>), [defensive-security](<https://devfeed.tech/tags/defensive-security.md>), [dnn](<https://devfeed.tech/tags/dnn.md>), [ebpf](<https://devfeed.tech/tags/ebpf.md>), [github](<https://devfeed.tech/tags/github.md>), [kernel](<https://devfeed.tech/tags/kernel.md>), [kprobe](<https://devfeed.tech/tags/kprobe.md>), [kretprobe](<https://devfeed.tech/tags/kretprobe.md>), [linux](<https://devfeed.tech/tags/linux.md>), [linux-security](<https://devfeed.tech/tags/linux-security.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [process-anomaly-detection](<https://devfeed.tech/tags/process-anomaly-detection.md>), [process-behaviour](<https://devfeed.tech/tags/process-behaviour.md>), [raw-syscalls](<https://devfeed.tech/tags/raw-syscalls.md>), [runtime-protection](<https://devfeed.tech/tags/runtime-protection.md>), [sys-enter](<https://devfeed.tech/tags/sys-enter.md>), [syscall-tracing](<https://devfeed.tech/tags/syscall-tracing.md>), [tensorflow](<https://devfeed.tech/tags/tensorflow.md>), [tracepoint](<https://devfeed.tech/tags/tracepoint.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>)

### AI overview

This tutorial describes using eBPF syscall tracing and an unsupervised autoencoder to detect process behavior anomalies at runtime. It explains an approach that models syscall frequency without requiring an explicit allowlist and discusses potential detection of exploitation, denial-of-service, and other attacks.

### Source excerpt

Hello everybody, I hope you've been enjoying this summer after two years of Covid and lockdowns :D In this post I'm going to describe how

## Deep Probabilistic Modelling with Gaussian Processes #NIPS2017

DevFeed: [Deep Probabilistic Modelling with Gaussian Processes #NIPS2017](<https://devfeed.tech/articles/deep-probabilistic-modelling-with-gaussian-processes-nips2017-40106.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2017-12-04-nips-tutorials-dgp/>)

Published: 2017-12-04T12:00:00Z

Content type: tutorial

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [Tutorial](<https://devfeed.tech/topics/tutorial.md>), [VAE](<https://devfeed.tech/topics/vae.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [NeurIPS](<https://devfeed.tech/topics/neurips.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [bias](<https://devfeed.tech/tags/bias.md>), [conference](<https://devfeed.tech/tags/conference.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [gaussian](<https://devfeed.tech/tags/gaussian.md>), [inference](<https://devfeed.tech/tags/inference.md>), [modelling](<https://devfeed.tech/tags/modelling.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [probabilistic](<https://devfeed.tech/tags/probabilistic.md>), [research](<https://devfeed.tech/tags/research.md>), [supervised-learning](<https://devfeed.tech/tags/supervised-learning.md>), [theory](<https://devfeed.tech/tags/theory.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>), [videos](<https://devfeed.tech/tags/videos.md>)

### AI overview

Lecture notes from a NeurIPS 2017 tutorial introduce deep probabilistic modelling with Gaussian processes, covering probabilistic neural networks, uncertainty, graphical models, and the computational challenge of inference.

### Source excerpt

Lecture notes from Neil Lawrence's NIPS 2017 tutorial on deep probabilistic modelling with Gaussian processes -- from GPs to deep GPs and variational inference.

## k-Means Clustering and Birth Rates

DevFeed: [k-Means Clustering and Birth Rates](<https://devfeed.tech/articles/k-means-clustering-and-birth-rates-40301.md>)

Original publisher: [Read original article](<https://www.jeremykun.com/2013/02/04/k-means-clustering-and-birth-rates/>)

Published: 2013-02-04T17:54:20Z

Content type: tutorial

Language: en

Sources: [Jeremy Kun](<https://devfeed.tech/sources/jeremy-kun.md>)

Topics: [clustering](<https://devfeed.tech/topics/clustering.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [clustering](<https://devfeed.tech/tags/clustering.md>), [heuristic-algorithm](<https://devfeed.tech/tags/heuristic-algorithm.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [mathematica](<https://devfeed.tech/tags/mathematica.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [partition](<https://devfeed.tech/tags/partition.md>), [programming](<https://devfeed.tech/tags/programming.md>), [python](<https://devfeed.tech/tags/python.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>)

### AI overview

This tutorial introduces the clustering problem, formalizes k-means clustering as a partitioning problem over points in a metric space, explains why finding an exact solution is difficult, and describes using a heuristic algorithm instead.

### Source excerpt

A common problem in machine learning is to take some kind of data and break it up into "clumps" that best reflect how the data is structured. A set of points which are all collectively close to each other should be in the same clump. A simple picture will clarify any vagueness in this: cluster-example Here the data consists of points in the plane. There is an obvious clumping of the data into three pieces, and we want a way to automatically determine which points are in which clumps.