Wednesday, May 31, 2023

Blockchain Forensics R&D With Python - Tracking Attackers, Etherscan API Usage, Python Coding, Signature Lookups And Decompiling Bytecode

 Added a new playlist over the last few weeks, its up to about 8 videos now which starts out writing code to monitor attackers address changes on the blockchain followed by using EtherScan API's to track some movements and make sense of things with a few other learning points along the way...   

There is one pre-requisite video however which is where this series came from which is another video I created where we took apart a smart contract from a YouTube scam to find the attackers malicious code, so review that first below: 

Twitter: @ficti0n
Web: http://cclabs.io/


If you learn something from this blog/video consider sharing it on your social media or retweeting my posts.


Here is the Pre-Requisite Video to this series Which does a smart contract audit of a malicious front running bot: 



Here is the new playlist series which digs into tracking this attacker: 


Link to full Playlist since blogger is not letting me embed the playlist: 
https://www.youtube.com/playlist?list=PLCwnLq3tOElrUdIg4LgdhPhCKAiy7NZYA




Intro to the playlist: 


 

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Posted by K. Lianne at 8:44 PM | | Comments

FOOTPRITING AND INFORMATION GATHERING USED IN HACKING

WHAT IS FOOTPRITING AND INFORMATION GATHERING IN HACKING?

 Footpriting is the technique used for gathering information about computer systems and the entities they belongs too. 
To get this information, a hacker might use various tools and technologies.

Basically it is the first step where hacker gather as much information as possible to find the way for cracking the whole system or target or atleast decide what types of attacks will be more suitable for the target.

 Footpriting can be both passive and active.

 Reviewing a company's website is an example of passive footprinting, 
whereas attempting to gain access to sensititve information through social engineering is an example of active information gathering.

 During this phase hacking, a hacker can collect the following information>- Domain name
-IP Addresses
-Namespaces
-Employee information 
-Phone numbers
-E-mails 
Job information

 Tip-You can use http://www.whois.com/ website to get detailed information about a domain name information including its owner,its registrar, date of registration, expiry, name servers owner's contact information etc.

 Use of  Footprinting & Information Gathering in People Searching-
Now a days its very easy to find anyone with his/her full name in social media sites like Facebook, Instragram,Twitter,Linkdedin to gather information about date of birth,birthplace, real photos, education detail, hobbies, relationship status etc.

 There are several sites like PIPL,PeekYou, Transport Sites such as mptransport,uptransport etc and Job placement Sites such as Shine.com,Naukari.com , Monster.com etc which are very useful for hacker to collect information about anyone.  
Hacker collect the information about you from your Resume which you uploaded on job placement site for seeking a job as well as  hacker collect the information from your vehicle number also from transport sites to know about the owner of vehicle, adderess etc then after they make plan how to attack on victim to earn money after know about him/her from collecting information.


INFORMATION GATHERING-It is the process of collecting the information from different places about any individual company,organization, server, ip address or person.
Most of the hacker spend his time in this process.

Information gathering plays a vital role for both investigating and attacking purposes.This is one of the best way to collect victim data and find the vulnerability and loopholes to get unauthorized modifications,deletion and unauthorized access.



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Posted by K. Lianne at 12:16 PM | | Comments

5 Free Online Courses To Learn Artificial Intelligence

We are living in the era of fourth industrial revolution(4IR), where Artificial intelligence has a significant role to play. This 4IR technology embedded within societies and even into the human body. From Computer enthusiasts to common people, everyone should be aware and learn this breakthrough technology.
We think about gigantic Robots from Transformers when we hear about Artificial Intelligence(AI) which is a fiction in the past but a fact today, capable of transforming the whole tech world. The field of AI consists of more than Robots such as personal assistants, self-driving cars, apprenticeship learning, behavior cloning and so on. To learn about this advanced technology, thanks to the online learning resources which offers great content to get started with artificial intelligence.

Here are the 5 free e-learning courses on Artificial Intelligence

1. UC Berkeley CS188 Intro to AI

Get started with UC Berkeley AI course, this course is absolutely for beginners who are unaware of Artificial intelligence. It doesn't need any prior computer knowledge to know about AI. UC Berkeley allows anyone to learn this course for free. This course is systematically presented and consists of the following:
  • Course Schedule
  • Complete sets of Lecture Slides and Videos
  • Interface for Electronic Homework Assignments
  • Section Handouts
  • Specs for the Pacman Projects
  • Source files and PDFs of past Berkeley CS188 exams
  • Form to apply for edX hosted autograders for homework and projects (and more)
  • Contact information
Aside from this, you can also browse the following courses as well from UC Berkeley that are part of AI course:
  • Machine Learning: CS189, Stat154
  • Intro to Data Science: CS194-16
  • Probability: EE126, Stat134
  • Optimization: EE127
  • Cognitive Modeling: CogSci131
  • Machine Learning Theory: CS281A, CS281B
  • Vision: CS280
  • Robotics: CS287
  • Natural Language Processing: CS288

2. Artificial Intelligence: Principles and Techniques

This course is offered by Stanford with great content that includes topics, videos, assignments, projects, and exams. The whole course mainly focuses on the complex real-world problems and try to find similarity between web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling. Here you will learn the foundational principles of AI and implement some the AI systems. The goal of this course is to help you tackle the real-world situations with the help of AI tools. So, it is the best for the beginner to get started with AI.

3. Learn with GOOGLE AI

Who will dislike the course from Google? absolutely no one. This company is one of the early adopters of AI has a lot to offer to learners. Learn with Google AI is an education platform for people at all experience levels, it is free to access and browse content. The education resources provided by Google is from the machine learning experts of the company. These resources are the collections of lessons, tutorials, and Hands-on exercises that help you start learning, building, and problem-solving.

4. MIT 6.S094: Deep Learning for Self-Driving Cars

This course gives the practical overview of Deep Learning and AI. It is the course for beginners, also for the people who are getting started with Machine Learning. The course also offers a lot of benefits to the experienced and advanced researchers in the field deep learning. This MIT's course takes people into the journey of Deep Learning with the applied theme of building Self-Driving cars. However, the course also offers slides and videos to engage the learners.

5. Fundamentals of Deep Learning for Computer Vision

This course is offered by Nvidia and Nvidia Deep learning Institute. Computer Vision is one of the disciplines of AI that acquire, analyze, process, and understand images. The course is completely free and everyone who is enthusiast about AI can access and learn the course. It is a hands-on course that able to provide basics of deep learning and deployment of neural networks. With this. you will also learn the following:
  • Identify the ingredients required to start a Deep Learning project.
  • Train a deep neural network to correctly classify images it has never seen before.
  • Deploy deep neural networks into applications.
  • Identify techniques for improving the performance of deep learning applications.
  • Assess the types of problems that are candidates for deep learning.
  • Modify neural networks to change their behavior.
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Posted by K. Lianne at 3:32 AM | | Comments

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