NETLAB+ is a remote access solution that lets students complete labs anywhere, and anytime. Here are the steps for logging in and using Netlab for the first time.

Logging Into Netlab

Login to Netlab: https://ccsenetlab2.kennesaw.edu/

Enter your Username and Temp Password and click Login

Change your password and click Submit

Leave E-mail Address blank and click Submit

Click Submit on Date and Time Settings if they are correct

Click Understood and you are ready to use Netlab

Scheduling a Lab Reservation

Once logged in, you can click on “New Lab Reservation”, then “Schedule Lab for Myself”

If you are enrolled in IT1738, you will see a list of courses. Please take them in order.

If you are enrolled in a single course, your screen will be similar to below.

A list of labs will appear. Click the dropdown arrow for the lab you would like then click Schedule Lab

Select the date, POD, and time you would like to complete the lab

If everything looks correct, click Submit

Click OK

When it is time to complete the lab it will say Enter Lab where the red box is

Completing a Lab

Once logged in and your lab is scheduled, you can click on Enter Lab if it is your scheduled time

When you enter the lab you will see the menu bar below

Content is where you will find the step-by-step instructions to complete the lab. Every tool or resource you need will be provided for you with the Content section.

Installing Kali Tools on ANY Version of Linux

I am a huge fan of open source code, Kali, and Raspberry Pi’s. Unfortunately, there are quite a few tools on Kali that no longer work. It’s a huge pain to install the synaptic package manager and go through and uninstall the broken packages. It’s even more of a pain to run sudo apt-get remove “package” for each package.

My goals were to:

1. Install a lightweight version of Linux on Raspberry Pi
2. Install ONLY the Kali Linux tools that I wanted on my Raspberry Pi (Linux Mint 19 & Ubuntu 18)
3. Conserve resources (sdcard space, memory, cpu processing)

I can across Katoolin a few years ago and it did not work on my 32-bit version of Linux Mint 17. I wiped my laptop recently and installed Linux Mint 19 64-bit. I absolutely love it! It had just about everything I needed on first boot. I also have a separate partition with Kali Linux on the same laptop. If I wanted to use Kali, I would have to reboot. If I needed to actually do some work, I would reboot and switch back to Linux Mint. Enough about my set up! lol

I followed these steps with the exception of adding the sudo command in front of each one and voila. I can add just the Kali tools that I need and do not have to reboot to use them. Enjoy!

Short Bytes: Many Ubuntu Linux users are willing to use the tools of Kali Linux but they don’t want to install another Linux distro for this purpose. To solve this issue, you can use Katoolin script and install all Kali Linux tools on Ubuntu and other Debian derivatives.

Kali Linux is one of the most popular Linux-based distributions being used by ethical hackers. This Debian-derived Linux derivative is specially crafted for penetration testing and digital forensics. However, if you want to use a Linux distribution for daily use, it’s not recommended.

But, what if you want to try out Kali Linux tools on other Linux distros like Ubuntu? You’ll be glad to know that all the tools of this free Linux distribution could be installed on your regular Ubuntu installation.

Here, in this article, I’ll show you how to install Kali Linux tools on Ubuntu using a simple script called Katoolin.

Things to know before installing Kali Linux tools on Ubuntu:

To guide you through this tutorial, I’ll be using an installation of Ubuntu 16.04 LTS on my MacBook. This tutorial will also work fine on other versions of Ubuntu like Ubuntu 15.10 and Ubuntu 14.04. It could also be used to install Kali Linux tools on other Debian-based Linux distros.

Note that with the help of Katoolin script, we’ll be able to add Kali repos to Ubuntu Linux, remove them, and install all the tools of Kali Linux.

How To Install All Kali Linux Tools On Ubuntu?

Using few simple commands, we can easily install Kali Linux tools on our Ubuntu machine.

To install Katoolin, first you need to install git with the following command:

apt-get install git

Now, download Katooni and copy the executable to proper directory using following commands:

git clone https://github.com/LionSec/katoolin.git  && cp katoolin/katoolin.py /usr/bin/katoolin

The next step is to make Katoolin executable by running the following command:

chmod +x  /usr/bin/katoolin

Now, you just need to enter following command to run Katoolin on your Ubuntu installation:

katoolin

As soon as you enter this command, the following easy-to-navigate menu will appear with 5 options to add Kali Linux repos and update, categories etc.

Using Katoolin in Ubuntu to install Kali Linux tools:

Using Katoolin is very simple and everything is right in front of you. To add Kali Linux repos, select 1 from the menu and press enter.  To update them, select option 2:

To delete Kali Linux repos that you added above, you just need to select option 3:

Now, you need to go back to the main menu and install Kali Linux tools in Ubuntu. To do so, simply type back and press enter. To choose a category of Kali Linux tool, yo need to select option 2 and press enter. 

Here, I chose option 1 i.e. Information Gathering and pressed enter:

This will present a list of different categories and you can select any one of these. You can also choose all of them at once using 0 option. Here, I chose to install Wireshark by choosing option 52:

This way you can choose to install Kali Linux tools on Ubuntu and start hacking. Did you find this tutorial helpful? Share your views in the comments below.

Kali Linux is great if you know exactly what you are doing. If something breaks, no problem. You fix it and keep moving. Unfortunately, as a Professor, all it takes is a [sudo apt-get install ‘bad package’] to have 30-70 students sending you emails. So I started looking for an alternative that was a little more user-friendly, secure and worked on a Raspberry Pi. I will discuss in another post why I use Raspberry Pi’s for everything.

Introduction

PiNet is a system that was developed by Andrew Mulholland, a Computer Science student at Queens University, Belfast. It was designed for schools/ organizations to setup and manage Raspberry Pi networks. The software/documentation is completely free and open source and has been built with guidance from educators across the world. A list of all those that have helped make this project possible can be found here.

Hardware Needed (Server Side)

• 1x Server (Physical or Virtual)
• 1x Router (For Internet. Not Required)
• 1x Network Switch (Gigabit Preferred)

Equipment Needed (Client Side)

xx represents the number for your environment

• xx Raspberry Pi3’s
• xx 8gb Micro SD Cards
• xx CAT5 or CAT5e network cables (length depends on the distance from the switch)
• xx Monitor, Mouse, and Keyboard

Server

Standard Setup (Up to 50 Nodes)

• Dual-core 2GHz processor (Intel Core I3 or greater)
• 4GB of RAM
• 40GB of hard disk space
• A gigabit ethernet port (100mbit/s is not officially supported)

Hosted Setup (50+ Nodes)

• Dual-core 2GHz processor (Intel Core I3 or greater)
• 8GB of RAM (4GB allocated to PiNet/Ubuntu)
• 40GB of SSD space (free to be used by PiNet/Ubuntu)
• A gigabit ethernet port (100mbit/s is not officially supported)

Router

A router is needed for DHCP and routing out to the internet if needed. A router with 100mb ports will work, however, 1000mb is preferred.

Switch

Networks are made up of network switches. They typically have 8, 16, 24 or 48 ports and will operate at the speeds above. With some switches, all the ports run at the same speed. You will need a switch with xx plus 1 ports for the number of nodes.  For example, if you have 8 nodes you will need a 16 port switch as a port is needed for the server making it a total of 9 ports in use.

Setting Up The Environment

These instructions are designed to be used by the consultants at The Onyx IT Group and are not a step by step guide. This is a high-level reference guide that has been tested as of 10/27/2018.

1. Install the latest version of Ubuntu Desktop either on a dedicated PC or in a VM
   1. We installed Ubuntu 18.04.1 LTS as a VM on VMWare ESXi 6 on a Dell R620
2. Use the server requirements under the “hardware required section” for memory and hard drive size
3. Login in and change the hostname and assign a static IP to your ethernet adapter
4. Run the command  sudo wget --content-disposition http://links.pinet.org.uk/jessie-stable-pinet
5. When the previous command completes, run sudo bash pinet and follow the prompts on the screen (We are running in <Development> mode)
6. Once the PiNet install is complete, format an 8gb sdcard
7. Copy /Home/piBoot to the sdcard
8. Eject sdcard, place it in a Raspberry Pi and insert the power cable

In this tutorial, we will install and configure a Hadoop cluster using Raspberry Pi’s. Our cluster will consists of four nodes (one master and three slaves). Since Raspberries have a low price and Hadoop is open source, together they offer a great resource to learn Hadoop. Make sure you have at least 8 hours set aside to complete this project or split it up into manageable sections. 

Items in blue are code to be copied. Items in red are to be changed for your environment. Items in purple are for slave nodes only.

Materials

• 4x Raspberry PI 3 Model (4 Core 1Ghz CPU, 1GB RAM)
• 4x 16gb MicroSDHC cards (Sandisk UHS-I 10x)
• 4x micro USB to USB for power
• 4x CAT5e network cables (3feet if possible)
• 1x Powered USB Hub
• 1x 5 or more port switch
• 1x Dogbone case for 4x Raspberry Pi’s (Any case will do)
• Rasbpian Jessie Lite (Linux 4.1.17-v7+)
• Oracle Java java version 1.8.0_65
• Hadoop 2.7.2 (ARM native compiled from source)
  
  

For my setup, I ordered the dogbone case, usb hub, microUSB to USB cables, and ethernet cables from Amazon. Since Microcenter is local, I drove over and purchased 4 Raspberry Pi 3’s for $29 each and 4 16gb MicroSDHC cards for $7.99 each. I already owned a 24 port non-managed switch and a 48 port Cisco switch. I also had a spare monitor, keyboard, and HDMI to DVI cable. Once I setup the Raspberry Pi’s using the monitor, I would ssh into each one from a remote computer. The total cost of this project was under $200.

Install Raspbian

Download Raspbian Jessie Lite: https://www.raspberrypi.org/downloads/raspbian/

Write to SD card (use any tool of choice) for windows I use: https://sourceforge.net/projects/win32diskimager/

Plugin in SD card and fire up your PI.

For initial configuration (raspi-config)

• Expand filesystem
• Memory Split Choose 16MB to give as much RAM as possible for Hadoop
• Change the hostname (rpi0)
• Turn on SSH
• Change the root password

Reboot

Basic Pre-installation Configuration

Change Password, Update, and Install Java, rsync, and ant

passwd

sudo apt-get update && sudo apt-get install oracle-java8-jdk && sudo apt-get install rsync && sudo apt-get install ant 

Verify that it was installed correctly:

java –version

Run update-alternatives, ensure jdk-8-oracle-*** is selected if you have Java 7

sudo update-alternatives --config java

Setup Connectivity

sudo nano /etc/hosts

10.0.0.100 rpi0
10.0.0.101 rpi1
10.0.0.102 rpi2
10.0.0.103 rpi3

sudo nano /etc/dhcpcd.conf

At the bottom of the file add:

static ip_address=interface eth0
static ip_address=10.0.0.100/24
# static ip_address=10.0.0.101/24
# static ip_address=10.0.0.102/24
# static ip_address=10.0.0.103/24
static routers=10.0.0.1
static domain_name_servers=10.0.0.1

Restart & SSH to new IP

sudo shutdown -r

Login locally or remote

ssh pi@10.0.0.100

Configure Hadoop User

sudo addgroup hadoop

sudo adduser --ingroup hadoop hduser

sudo adduser hduser sudo

logout

Log in as the newly created hduser. From here forward, everything will be executed as hduser. Create SSH pairs keys with blank password. This will enable nodes to communicate with each other in the cluster.

mkdir ~/.ssh

ssh-keygen -t rsa -P ""

cat ~/.ssh/id_rsa.pub > ~/.ssh/authorized_keys

SSH to your node and answer yes when prompted to trust certificate key – otherwise Hadoop will fail to login later.

ssh rpi0

exit

Install Hadoop 2.7.3 for Raspberry PI (ARM)

Ensure you have logged out as hduser and logged in as pi user. (for sudo command below to work properly, as we did above)

Install Hadoop

cd ~/

sudo mkdir /opt (directory may already exist)

cd /opt/span>

sudo wget http://www-us.apache.org/dist/hadoop/common/stable/hadoop-2.7.3.tar.gz

sudo tar xvzf hadoop-2.7.3.tar.gz

sudo mv hadoop-2.7.3 hadoop

sudo chown -R hduser:hadoop hadoop

Give access to hduser

sudo chown -R hduser:hadoop /opt/hadoop/

Add environment variables

sudo nano ~/.bashrc

Add the following to the bottom of the file

# — HADOOP ENVIRONMENT VARIABLES START — #
export JAVA_HOME=$(readlink -f /usr/bin/java | sed “s:bin/java::”)
export HADOOP_HOME=/opt/hadoop
export PATH=$PATH:$HADOOP_HOME/bin
export PATH=$PATH:$HADOOP_HOME/sbin
export HADOOP_MAPRED_HOME=$HADOOP_HOME
export HADOOP_COMMON_HOME=$HADOOP_HOME
export HADOOP_HDFS_HOME=$HADOOP_HOME
export YARN_HOME=$HADOOP_HOME
export HADOOP_COMMON_LIB_NATIVE_DIR=$HADOOP_HOME/lib/native
export HADOOP_OPTS=”-XX:-PrintWarnings -Djava.library.path=$HADOOP_HOME/lib”
# — HADOOP ENVIRONMENT VARIABLES END — #

Check to see if the Hadoop variable has been set

source ~/.bashrc

hadoop version

Setting up the Hadoop Daemon configurations

Add environment variable to hadoop-env.sh

cd /opt/hadoop/etc/hadoop
 

sudo nano hadoop-env.sh

Add this:

export JAVA_HOME=$(readlink -f /usr/bin/java | sed "s:bin/java::")
export HADOOP_HEAPSIZE=250

Edit Config Files (Only change what’s in red to match your hostname)

cd opt/hadoop/etc/hadoop

sudo nano core-site.xml

<configuration>

<property>
<name>hadoop.tmp.dir</name>
<value>/opt/hdfs/tmp</value>
</property>

<property>
<name>fs.default.name</name>
<value>hdfs://rpi0:9000/</value>
</property>

</configuration>

sudo nano hdfs-site.xml

<configuration>

<property>
<name>dfs.replication</name>
<value>3</value>
</property>

<property>
<name>dfs.webhdfs.enable</name>
<value>true</value>
</property>

<property>
<name>dfs.blocksize</name>
<value>134217728</value>
</property>

</configuration>

sudo nano yarn-site.xml

<configuration>

<property>
<name>yarn.resourcemanager.resource-tracker.address</name>
<value>rpi0:8025</value>
</property>

<property>
<name>yarn.resourcemanager.scheduler.address</name>
<value>rpi0:8035</value>
</property>

<property>
<name>yarn.resourcemanager.address</name>
<value>rpi0:8050</value>
</property>

<property>
<name>yarn.nodemanager.aux-services</name>
<value>mapreduce_shuffle</value>
</property>

<property>
<name>yarn.nodemanager.resource.cpu-vcores</name>
<value>4</value>
</property>

<property>
<name>yarn.nodemanager.resource.memory-mb</name>
<value>768</value>
</property>

<property>
<name>yarn.scheduler.minimum-allocation-mb</name>
<value>64</value>
</property>

<property>
<name>yarn.scheduler.maximum-allocation-mb</name>
<value>256</value>
</property>

<property>
<name>yarn.scheduler.minimum-allocation-vcores</name>
<value>1</value>
</property>

<property>
<name>yarn.scheduler.maximum-allocation-vcores</name>
<value>4</value>
</property>

<property>
<name>yarn.nodemanager.vmem-check-enabled</name>
<value>true</value>
</property>

<property>
<name>yarn.nodemanager.pmem-check-enabled</name>
<value>true</value>
</property>

<property>
<name>yarn.nodemanager.vmem-pmem-ratio</name>
<value>2.1</value>
</property>

</configuration>

sudo nano mapred-site.xml.template

Save As: mapred-site.xml

<configuration>

<property>
<name>mapredreduce.job.tracker</name>
<value>rpi00:9001</value>
</property>

<property>
<name>mapred.framework.name</name>
<value>yarn</value>
</property>

<property>
<name>mapreduce.map.memory.mb</name>
<value>256</value>
</property>

<property>
<name>mapreduce.map.java.opts</name>
<value>-Xmx204m</value>
</property>

<property>
<name>mapreduce.map.cpu.vcores</name>
<value>2</value>
</property>

<property>
<name>mapreduce.reduce.memory.mb</name>
<value>128</value>
</property>

<property>
<name>mapreduce.reduce.java.opts</name>
<value>-Xmx102m</value>
</property>

<property>
<name>mapreduce.reduce.cpu.vcores</name>
<value>2</value>
</property>

<property>
<name>yarn.app.mapreduce.am.resource.mb</name>
<value>128</value>
</property>

<property>
<name>yarn.app.mapreduce.am.command-opts</name>
<value>-Xmx102m</value>
</property>

<property>
<name>yarn.app.mapreduce.am.resource.cpu-vcores</name>
<value>1</value>
</property>

<property>
<name>mapreduce.job.maps</name>
<value>4</value>
</property>

<property>
<name>mapreduce.job.reduces</name>
<value>4</value>
</property>

</configuration>

Hadoop Data File System (HDFS)

As part of the Hadoop installation HDFS is automatically added. A tmp folder needs to be created within HDFS to keep the temporary test data.

sudo mkdir /opt/hdfs/tmp

sudo chown hduser:hadoop /opt/hdfs/tmp

sudo chmod 750 /opt/hdfs/tmp

Add the slaves file

sudo nano /opt/hadoop/etc/hadoop/slaves

add :

rpi1
rpi2
rpi3

Copy SD Card

Use Win32imager or any other software of your choice to clone the sd-card. After clone ensure to configure each node with new hostname and ip-address.

Update Datanodes one at a time

Leave rpi0 off until all datanodes are setup

Plugin in SD card in (rpi1, then rpi2, then rpi3) and fire up your Pi’s.

Login in as pi

sudo raspi-config

Change the hostname (rpi1, rpi2, rpi3)

Change the IP Address

sudo nano /etc/dhcpcd.conf

Comment out the IP for rpi0 and uncomment the IP for rpi1. Repeat on rpi2, rpi3

interface eth0
#static ip_address=10.0.0.100/24
static ip_address=10.0.0.101/24
# static ip_address=10.0.0.102/24
# static ip_address=10.0.0.103/24
static routers=10.0.0.1
static domain_name_servers=10.0.0.1

Reboot – This concludes the setup of the slaves

Add the master file to the master rpi0 only

sudo nano /opt/hadoop/etc/hadoop/masters

add :

rpi0

Logon with ssh to all nodes from namenode (rpi1). Enter Yes when you get the “Host key verification failed message”. This is important in order for namenode to be able to communicate with the other nodes without entering a password.

Format the namenode (rpi0) and start the services

hdfs namenode -format

That will format the master as a proper namenode. Finally, start the services. Previously, Hadoop used the start-all.sh script for this, but it has been deprecated and the recommended method now is to use the start-<service>.sh scripts individually. In each node, go to /opt/hadoop/etc/hadoop/ and execute the following:

start-dfs.sh

start-yarn.sh

Verify the cluster

There are several ways to confirm that everything is running properly, for example, you can point your browser to http://rpi0:50070 (the http address configured earlier) or to http://rpi0:8088. There you can see status reports of the cluster.

Alternatively, you can also check the Java processes in the nodes. In the master node you should see at least 3 processes: NameNode, SecondaryNameNode and ResourceManager. In the slaves you should see only two: NodeManager and DataNode. My favorite way is to use the hdfs report command. This is the output report of my configuration:

hdfs dfsadmin -report

Configured Capacity: 182240477184 (169.72 GB)
Present Capacity: 153073315840 (142.56 GB)
DFS Remaining: 153073020928 (142.56 GB)
DFS Used: 294912 (288 KB)
DFS Used%: 0.00%
Under replicated blocks: 0
Blocks with corrupt replicas: 0
Missing blocks: 0

————————————————-

Live datanodes (4):

These strings copy the basic configuration to from the master to the slave nodes. You only need to do this if you make changes on the master to any of the xml files and want to replicate those changes to the slave nodes.

sudo rsync -avxP --rsync-path='/usr/bin/sudo /usr/bin/rsync' /opt/hadoop/etc/hadoop pi@rasp1:/opt/hadoop/etc/hadoop

sudo rsync -avxP --rsync-path='/usr/bin/sudo /usr/bin/rsync' /opt/hadoop/etc/hadoop pi@rasp1:/opt/hadoop/etc/hadoop

sudo rsync -avxP --rsync-path='/usr/bin/sudo /usr/bin/rsync' /opt/hadoop/etc/hadoop pi@rasp1:/opt/hadoop/etc/hadoop

References

Verduco, A. (2016, March 23). Building a Hadoop cluster with Raspberry Pi – developerWorks Recipes. Retrieved April 26, 2017, from https://developer.ibm.com/recipes/tutorials/building-a-hadoop-cluster-with-raspberry-pi/#r_overview

Widriksson, J. (2016). Raspberry PI 2 Hadoop 2 Cluster – Jonas Widriksson. Retrieved April 26, 2017, from http://www.widriksson.com/raspberry-pi-2-hadoop-2-cluster/

My Personal Thoughts of Kali Linux Full on a Raspberry Pi 3 B+ v1.2

Remember in the beginning when I said you could install Kali on an 8gb or 16gb sdcard? This is what I was referencing. If you plan to install all of the Kali Lunix tools or the “Full” version, you will need a 16gb sdcard. I will warn you. I have had terrible experiences doing this with Kali 2018.3 which is designed on Debian. Kali just released 2018.4 in October 2018.

The CPU lags to the point where you spend a great deal of time waiting for your mouse to come back. There is no around it. You can still use the 16gb sdcard, just download the specific Kali tools and dependencies that you are looking for. If you do not know what I am referencing, you will soon enough.

In case you are interested, this is what the full Kali menu looks like.

Install Full Version of Kali

Login using the username ‘root ‘ and the new password you created.

Install Kali Linux Full, and as stated earlier,  based on your internet speed, this could take an additional 4-5 hours.

root@kali:~# sudo apt-get update && sudo apt-get install kali-linux-full

Kali Linux on Raspberry Pi3

Materials Needed

• 1x Raspberry Pi 2/3
• 1x 8gb or 16gb Class 10 MicroSDHC card
• 1x MicroSDHC Reader/Writer
• 1x USB Raspberry Pi 3 Power Supply 5V 2.5
• Etcher
• Keyboard, Mouse, Monitor, and Internet Access

High Level Overview

1. Download kali linux for the Raspberry Pi
2. Download Etcher
3. Write the image to the MicroSDHC card
4. Boot the Raspberry Pi and Login as root/toor
5. Change the password
6. Change the hostname
7. Update and Upgrade
8. Reboot/Test/Patch if needed/Reboot
9. Test Kali

Step-by-step Instructions

Disclaimer: Please type all commands below when directed. Copy and paste the code at your own risk. The copied code could contain spaces and/or other HTML characters not suitable for python or bash scripting.