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@martinseener
martinseener / gist:5247292
Last active Jul 1, 2019
Grok Sophos UTM 9.x Pattern (for logstash) (Simple)
View gist:5247292
filter {
grok {
pattern => ['(?:%{SYSLOGTIMESTAMP:timestamp}|%{TIMESTAMP_ISO8601:timestamp8601}) (?:%{SYSLOGHOST:logsource}) (?:%{YEAR}): (?:%{MONTHNUM}):(?:%{MONTHDAY})-(?:%{HOUR}):(?:%{MINUTE}):(?:%{SECOND}) (?:%{SYSLOGHOST}) (?:%{SYSLOGPROG}): (?<messagebody>(?:id=\"%{INT:utm_id}\" severity=\"%{LOGLEVEL:utm_severity}\" sys=\"%{DATA:utm_sys}\" sub=\"%{DATA:utm_sub}\" name=\"%{DATA:utm_name}\" action=\"%{DATA:utm_action}\" fwrule=\"%{INT:utm_ulogd_fwrule}\" initf=\"%{DATA:utm_ulogd_initf}\" outitf=\"%{DATA:utm_ulogd_outif}\" (?:srcmac=\"%{GREEDYDATA:utm_ulogd_srcmac}\" dstmac=\"%{GREEDYDATA:utm_ulogd_dstmac}\"|srcmac=\"%{GREEDYDATA:utm_ulogd_srcmac}\") srcip=\"%{IP:utm_srcip}\" dstip=\"%{IP:utm_dstip}\" proto=\"%{INT:utm_protocol}\" length=\"%{INT:utm_ulogd_pkglength}\" tos=\"%{DATA:utm_ulogd_tos}\" prec=\"%{DATA:utm_ulogd_prec}\" ttl=\"%{INT:utm_ulogd_ttl}\" srcport=\"%{INT:utm_srcport}\" dstport=\"%{INT:utm_dstport}\" tcpflags=\"%{DATA:utm_ulogd_tcpflags}\"|id=\"%{INT:utm_id}\" severity=\"%{LOGLEVEL:utm
@rxwei
rxwei / ad-manifesto.md
Last active Nov 4, 2019
First-Class Automatic Differentiation in Swift: A Manifesto
@ftrain
ftrain / rhymes.clj
Last active Jan 4, 2021
Annotated rhyming dictionary
View rhymes.clj
;; This is at: https://gist.github.com/8655399
;; So we want a rhyming dictionary in Clojure. Jack Rusher put up
;; this code here:
;;
;; https://gist.github.com/jackrusher/8640437
;;
;; I'm going to study this code and learn as I go.
;;
;; First I put it in a namespace.
@johnhw
johnhw / umap_sparse.py
Last active Dec 1, 2021
1 million prime UMAP layout
View umap_sparse.py
### JHW 2018
import numpy as np
import umap
# This code from the excellent module at:
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module
import random
@syhw
syhw / dnn.py
Last active Dec 22, 2021
A simple deep neural network with or w/o dropout in one file.
View dnn.py
"""
A deep neural network with or w/o dropout in one file.
License: Do What The Fuck You Want to Public License http://www.wtfpl.net/
"""
import numpy, theano, sys, math
from theano import tensor as T
from theano import shared
from theano.tensor.shared_randomstreams import RandomStreams
@adrianhall
adrianhall / AppSyncAPI.yaml
Last active Jan 15, 2022
A CloudFormation template for DynamoDB + Cognito User Pool + AppSync API for the Notes tutorial
View AppSyncAPI.yaml
---
Description: AWS AppSync Notes API
Parameters:
APIName:
Type: String
Description: Name of the API - used to generate unique names for resources
MinLength: 3
MaxLength: 20
AllowedPattern: '^[a-zA-Z][a-zA-Z0-9_]*$'
@markwalkom
markwalkom / logstash.conf
Last active Apr 29, 2022
Reindexing Elasticsearch with Logstash 2.0
View logstash.conf
input {
elasticsearch {
hosts => [ "HOSTNAME_HERE" ]
port => "9200"
index => "INDEXNAME_HERE"
size => 1000
scroll => "5m"
docinfo => true
scan => true
}
@ddevault
ddevault / Makefile
Last active May 5, 2022
Tiny Wayland compositor
View Makefile
WAYLAND_PROTOCOLS=/usr/share/wayland-protocols
# wayland-scanner is a tool which generates C headers and rigging for Wayland
# protocols, which are specified in XML. wlroots requires you to rig these up
# to your build system yourself and provide them in the include path.
xdg-shell-protocol.h:
wayland-scanner server-header \
$(WAYLAND_PROTOCOLS)/stable/xdg-shell/xdg-shell.xml $@
xdg-shell-protocol.c: xdg-shell-protocol.h
@marktheunissen
marktheunissen / pedantically_commented_playbook.yml
Last active Jun 6, 2022 — forked from phred/pedantically_commented_playbook.yml
Insanely complete Ansible playbook, showing off all the options
View pedantically_commented_playbook.yml
This playbook has been removed as it is now very outdated.
@karpathy
karpathy / pg-pong.py
Created May 30, 2016
Training a Neural Network ATARI Pong agent with Policy Gradients from raw pixels
View pg-pong.py
""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym
# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward