I work in Cyber Security. And if I’m being really honest, it’s not very interesting. There are plenty of tools, open-source or proprietary, that can automate many of the tedious tasks within a security role, but I always find myself more fascinated by how the tool works than by how much it’s helping me.
Cyber Security, Incident Response, Digital Forensics…They’re all very destructive by nature. But I’m a creative. How can a creative add value to an intrinsically destructive field? And how can I make my job more interesting by mixing it with my interests?
By creating tools.
This post is a walkthrough of a personal project I completed, which is a small step towards the creation of my own tool.
Turning thousands of lines of pcap data into interactive network visualisations.
Complete code available on my github repository.
Image by Author
Too often an Incident Responder asks for a map of the network, only to discover it’s more than 2 years old and not much more than a ghost of the current network topology.
So we try to create the ‘ground truth’ of what the network actually looks like. Various tools can be used for this, netflow, nmap, tracert. This example will focus on network packet captures, but a bit of Python applied in the right doses could adapt this project to any network mapping command line tool output.
Wireshark is a great tool, but as the size of the packet capture increases, the performance drops as it tries to parse the potential hundreds of thousands of lines of traffic data.
So I began by exporting my pcap data to a csv. Which python loves.
#data-science #dash #python #cybersecurity #data-visualization
Computers began to be used as mass market electronic devices in 1970s which is popularly known as personal computer era. They were intended for individual use much like any other electronic device like a calculator, although with much advanced functionalities. Basically, you bought a computer. You bought the software. You started using it for your individual purposes. Eventually, there was a need to inter-connect computers so that they can communicate to each other (e.g email, instant messaging) and with other devices (e.g printers). This gave rise to Computer Networks of personal computers.
A computer network represents an interconnection of multiple computers and/or other devices to facilitate communication between them.
This “communication”, if you think, is all about _ transfer of data_ between the devices, whether it’s an email, a chat message, a video call or sharing of file. Networking is all about communication which basically is nothing but “transfer of data”
Computer Networks are generally categorized by their size and physical capacity. e.g A LAN (Local Area Network) is a network that connects computers and devices in a limited geographical area such as home or office while a WAN (Wide Area Network) which connects computers and devices in a large geographical area such a city or country.
#computer-networking #internet #programming #computer-science #basic-concept #neural networks
Talking about inspiration in the networking industry, nothing more than Autonomous Driving Network (ADN). You may hear about this and wondering what this is about, and does it have anything to do with autonomous driving vehicles? Your guess is right; the ADN concept is derived from or inspired by the rapid development of the autonomous driving car in recent years.
Driverless Car of the Future, the advertisement for “America’s Electric Light and Power Companies,” Saturday Evening Post, the 1950s.
The vision of autonomous driving has been around for more than 70 years. But engineers continuously make attempts to achieve the idea without too much success. The concept stayed as a fiction for a long time. In 2004, the US Defense Advanced Research Projects Administration (DARPA) organized the Grand Challenge for autonomous vehicles for teams to compete for the grand prize of $1 million. I remembered watching TV and saw those competing vehicles, behaved like driven by drunk man, had a really tough time to drive by itself. I thought that autonomous driving vision would still have a long way to go. To my surprise, the next year, 2005, Stanford University’s vehicles autonomously drove 131 miles in California’s Mojave desert without a scratch and took the $1 million Grand Challenge prize. How was that possible? Later I learned that the secret ingredient to make this possible was using the latest ML (Machine Learning) enabled AI (Artificial Intelligent ) technology.
Since then, AI technologies advanced rapidly and been implemented in all verticals. Around the 2016 time frame, the concept of Autonomous Driving Network started to emerge by combining AI and network to achieve network operational autonomy. The automation concept is nothing new in the networking industry; network operations are continually being automated here and there. But this time, ADN is beyond automating mundane tasks; it reaches a whole new level. With the help of AI technologies and other critical ingredients advancement like SDN (Software Defined Network), autonomous networking has a great chance from a vision to future reality.
In this article, we will examine some critical components of the ADN, current landscape, and factors that are important for ADN to be a success.
At the current stage, there are different terminologies to describe ADN vision by various organizations.
Even though slightly different terminologies, the industry is moving towards some common terms and consensus called autonomous networks, e.g. TMF, ETSI, ITU-T, GSMA. The core vision includes business and network aspects. The autonomous network delivers the “hyper-loop” from business requirements all the way to network and device layers.
On the network layer, it contains the below critical aspects:
On top of those, these capabilities need to be across multiple services, multiple domains, and the entire lifecycle(TMF, 2019).
No doubt, this is the most ambitious goal that the networking industry has ever aimed at. It has been described as the “end-state” and“ultimate goal” of networking evolution. This is not just a vision on PPT, the networking industry already on the move toward the goal.
David Wang, Huawei’s Executive Director of the Board and President of Products & Solutions, said in his 2018 Ultra-Broadband Forum(UBBF) keynote speech. (David W. 2018):
“In a fully connected and intelligent era, autonomous driving is becoming a reality. Industries like automotive, aerospace, and manufacturing are modernizing and renewing themselves by introducing autonomous technologies. However, the telecom sector is facing a major structural problem: Networks are growing year by year, but OPEX is growing faster than revenue. What’s more, it takes 100 times more effort for telecom operators to maintain their networks than OTT players. Therefore, it’s imperative that telecom operators build autonomous driving networks.”
Juniper CEO Rami Rahim said in his keynote at the company’s virtual AI event: (CRN, 2020)
“The goal now is a self-driving network. The call to action is to embrace the change. We can all benefit from putting more time into higher-layer activities, like keeping distributors out of the business. The future, I truly believe, is about getting the network out of the way. It is time for the infrastructure to take a back seat to the self-driving network.”
If you asked me this question 15 years ago, my answer would be “no chance” as I could not imagine an autonomous driving vehicle was possible then. But now, the vision is not far-fetch anymore not only because of ML/AI technology rapid advancement but other key building blocks are made significant progress, just name a few key building blocks:
#network-automation #autonomous-network #ai-in-network #self-driving-network #neural-networks
Visual Analytics is the scientific visualization to emerge an idea to present data in such a way so that it could be easily determined by anyone.
It gives an idea to the human mind to directly interact with interactive visuals which could help in making decisions easy and fast.
Visual Analytics basically breaks the complex data in a simple way.
The human brain is fast and is built to process things faster. So Data visualization provides its way to make things easy for students, researchers, mathematicians, scientists e
#blogs #data visualization #business analytics #data visualization techniques #visual analytics #visualizing ml models
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