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import logging
import cStringIO
import csv

DEBUG = False

def data2csv(data):
    si = cStringIO.StringIO()
    cw = csv.writer(si, delimiter='\t',lineterminator="\n")
    for row in data:

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## FOR UBUNTU
Dependencies install

1. apt-get install nginx-extras
2. apt-get install lua-zlib

lua file decompress request body
--------------------------------

see  https://gist.github.com/iammehrabalam/30f5402bbcdad139c9eafd3a6f47ce6c

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Running Ubuntu 16.04.1 armhf on Qemu

This is a writeup about how to install Ubuntu 16.04.1 Xenial Xerus for the 32-bit hard-float ARMv7 (armhf) architecture on a Qemu VM via Ubuntu netboot.

The setup will create a Ubuntu VM with LPAE extensions (generic-lpae) enabled. However, this writeup should also work for non-LPAE (generic) kernels.

The performance of the resulting VM is quite good, and it allows VMs with >1G ram (compared to 256M on versatilepb and 1G on versatile-a9/versatile-a15). It also supports virtio disks whereas versatile-a9/versatile-a15 only support SD cards via the -sd argument.

Get netboot files

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# If you're looking into the C10M problem (10 million concurrent connections)
# you might want to play with DPDK (Originally proprietry Intel, now open source)
#
# C10M: http://c10m.robertgraham.com/
# DPDK: http://dpdk.org/
#
# This is a quick summary how to install dpdk on ubuntu
# running inside virtualbox on a mac

# On my Mac:

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#
# This will ratelimit requests to 10/s
# It will allow up to 128 requests to connect, make their request, and then delay the response
# On the client side, this will appear to be "hung", until one of the 10 slots opens up, at
# which point it will proxy your request
#
# If more than 128 requests are queued, any after that will immediately be returned with an
# error
#
#

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# ------Instructions---------
# Install (and configure) subfinder, assetfinder, and httprobe
# go get -v github.com/projectdiscovery/subfinder/cmd/subfinder && go get -v github.com/tomnomnom/httprobe && go get -v github.com/tomnomnom/assetfinder
# cat firefox.sh >> ~/.bashrc 
# source ~/.bashrc 
# Usage - subf_ff target.tld
#         asset_ff target.tld

subf_ff () {
subfinder -d $1 -silent -t 100 | httprobe -c 50 | sort -u | while read line; do firefox $line; sleep 10; done

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# One of the fastest ways to insert bulk data into Postgres (at least, aside from COPY) is using the psycopg2 extras function execute_values.
# However, this doesn't return an accurate row count value - instead, it just returns the row count for the last page inserted. 
# This wraps the execute_values function with its own pagination to return an accurate count of rows inserted. 
# Performance is approximately equivalent to underlying execute_values function - within 5-10% or so in my brief tests.

import psycopg2
import psycopg2.extras
import math

db_connection_string = "dbname=EDITME host=EDITME"

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Get PDF export of Grafana dashboard

Note

As currently described, this approach does not work. The reason for that is that creating a snapshot via the API does not actually populate any data, as described in e.g. https://community.grafana.com/t/snapshot-using-http-api-does-nothing/. An alternative approach which does work described in https://gist.github.com/svet-b/1ad0656cd3ce0e1a633e16eb20f66425.

Prerequisites

Packages:

• The jq JSON processing command line tool, which is available for most distributions (https://stedolan.github.io/jq/, sudo apt install jq)
• NodeJS, and the puppeteer package (npm install puppeteer), which is used to run headless Chrome

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from java.awt import Font
from javax.swing import JScrollPane, JTextPane
from javax.swing.text import SimpleAttributeSet

from burp import IBurpExtender, IExtensionStateListener, IHttpListener, ITab

import base64
import traceback

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from burp import IScanIssue

class CustomIssue(IScanIssue):
    def __init__(self, BasePair, Confidence='Certain', IssueBackground=None, IssueDetail=None, IssueName='Python Scripter generated issue', RemediationBackground=None, RemediationDetail=None, Severity='High'):
        self.HttpMessages=[BasePair] # list of HTTP Messages
        self.HttpService=BasePair.getHttpService() # HTTP Service
        self.Url=BasePair.getUrl() # Java URL
        self.Confidence = Confidence # "Certain", "Firm" or "Tentative"
        self.IssueBackground = IssueBackground # String or None
        self.IssueDetail = IssueDetail # String or None