🦑Evaluating and comparing machine learning (ML) and artificial intelligence (AI) algorithms at scale.

- Custom evaluation protocols: Supports different evaluation phases, dataset splits, and metrics, with both public and private leaderboards.
- Remote evaluation: For large-scale challenges requiring special compute capabilities, EvalAI allows organizers to add worker nodes to process submissions.
- Evaluation inside environments: Submissions are evaluated in Docker containers, ensuring a consistent environment.
- CLI support: The evalai-cli tool extends the platform’s capabilities to the command line.
- Portability and scalability: Built on open-source technologies like Docker, Django, Node.js, and PostgreSQL for easy deployment and scalability.
- Faster evaluation: Optimization techniques like dataset chunking and multi-core processing speed up evaluations.

For installation, EvalAI can be set up using Docker with the following steps:

1. Install Docker and Docker Compose.
2. Clone the source code from the repository:

   git clone https://github.com/Cloud-CV/EvalAI.git evalai && cd evalai
   

3. Build and run the Docker containers:

   docker-compose up --build
   

4. Access the platform at http://127.0.0.1:8888.

Default users include:
- SUPERUSER: username: admin, password: password
- HOST USER: username: host, password: password
- PARTICIPANT USER: username: participant, password: password

🦑Legit Win 11 Activator:

@ECHO off
cls

:: Prompt for the product key
set /p productKey="Enter your Windows 11 product key: "

:: Set the product key and install it
cscript slmgr.vbs /ipk %productKey%

:: Activate Windows with the Microsoft servers
cscript slmgr.vbs /ato

:: Check activation status
cscript slmgr.vbs /dli

pause

🦑Perl Hacking

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🦑Reverse Engineering iOS Applications:

> Here's a streamlined approach to obtain the required tools and set up your environment for iOS reverse engineering, as described. The provided GitHub repository is an excellent resource for guidance.

### Key Tools and Instructions

1. iTunnel
- Download: Allows SSH over USB.
- Usage:

     itnl --lport 2222 --iport 22
     ssh -p 2222 root@localhost
     

2. Cydia Impactor
- Download: Lets you sideload iOS apps using a developer certificate.

3. Hopper
- Download: Reverse engineering tool for disassembly, decompilation, and debugging.
- Trial version is sufficient.

4. Cycript
- Download: Enables runtime modification of app behavior via a console.

5. Frida
- Install:

     sudo pip install frida-tools
     

- Purpose: Scripting to change app behavior at runtime.

6. Bettercap
- Download: Perform MITM (Man-in-the-Middle) attacks.

7. class-dump-z
- Download: Dumps ObjC classes (for ObjC apps).

8. momdec
- Download: Decompiles CoreData models.

9. Ghidra
- Download: Alternative reverse engineering tool.

---

### iOS-Specific Tools

#### iOS 10.x
- Clutch
- Use for decrypting iOS applications.

#### iOS 11.x+
- bfinject
- Extract apps using Cycript and Clutch.
- Install dependencies:

    sudo pip install -r requirements.txt --upgrade
    

#### iOS 12.x
- frida-ios-dump
- Decrypt apps and auto-transfer to your computer.

---

### Device Setup

1. On iOS < 11.0
- Jailbreak and install via Cydia:
- Cycript
- Apple File Conduit "2"
- Frida (Source: https://build.frida.re)

- Test SSH:

     itnl --lport 2222 --iport 22
     ssh -p 2222 root@localhost
     

2. On iOS > 11.0
- Create required directories:

     cd / && mkdir jb
     mkdir /jb/bfinject && cd /jb/bfinject
     

- Transfer and extract bfinject:

     scp -P 2222 ~/Downloads/bfinject.tar root@localhost:/jb/bfinject
     tar xvf bfinject.tar
     

3. On iOS 12.x
- Install Frida via Cydia (Source: https://build.frida.re).


For more details and resources, check the GitHub repository: [RE-iOS-Apps](https://github.com/ivRodriguezCA/RE-iOS-Apps).

Curated list of data leaks around the world:

https://github.com/inspektor-dev/awesome-data-leak

🦑Ethical Hacking in 12 Hours:

https://youtu.be/fNzpcB7ODxQ

🦑 Markov Chain Overview and Its Application in Hashcat

What is a Markov Chain?
A Markov chain is a statistical model used to describe a stochastic process. It is characterized by the principle that the probability of transitioning to the next state depends only on the current state and not on the sequence of preceding states. This property is known as the Markov property.

#### Example:
Imagine you're playing a game where each "punch" is randomly determined:
1. The first three outcomes: "scissors," "paper," "rock."
2. For the fourth punch:
- "Rock" has a probability of 0.2,
- "Paper" has 0.3,
- "Scissors" has 0.5.

The outcome of the fourth punch depends only on the probabilities and not on the earlier punches.

---

### Markov Chains in Hashcat
Hashcat utilizes Markov chains to optimize brute-force password cracking in its Mask attack mode (attack mode = 3). Instead of enumerating every possible combination, Hashcat prioritizes password candidates based on their likelihood.

#### How It Works:
- Hashcat computes the Markov chain probability model of common passwords beforehand and stores it in a .hcstat2 file.
- When cracking, the stored probabilities help determine the order in which potential passwords are tested, with more likely combinations appearing first.
- For example, "s" often follows "t," and "q" often follows "u." These probabilities guide the cracking process.

This approach doesn’t reduce the total number of possibilities (keyspace) but improves efficiency by testing likely combinations first.

---

### Markov Chain Parameters in Hashcat
1. `--markov-hcstat2`
Specifies the .hcstat2 file with precomputed probabilities. Default: /usr/share/hashcat/hashcat.hcstat2.

2. `--markov-disable`
Disables Markov chains, reverting to classic brute-force.

3. `--markov-classic`
Uses traditional Markov chains without considering character positions.

4. `--markov-inverse`
Prioritizes less common character combinations, reversing the probability model.

5. `--markov-threshold=<NUM>`
Limits the number of candidates based on probability. For example, a threshold of 2 considers only the two most likely characters for each position.

---

### Per-Position Markov Chains
Hashcat includes a per-position Markov model to improve accuracy by considering the position of each character:
- Example:
- In the first position, "s" is likely followed by "t."
- In the seventh position, "s" might be followed by "e."

This method enables better tailoring of password candidates based on position-specific probabilities.

---

### Practical Demonstration
1. Without Markov chains:

   $ hashcat -a 3 --stdout --markov-disable ?l
   a
   b
   c
   ...
   z
   

2. With Markov chains:

   $ hashcat -a 3 --stdout ?l
   s
   m
   c
   ...
   x
   

3. With `--markov-inverse`:

   $ hashcat -a 3 --markov-inverse --stdout ?l
   x
   u
   q
   ...
   s
   

---

Summary
Hashcat's implementation of Markov chains enhances brute-force attacks by prioritizing password candidates based on likelihood, derived from precomputed statistical models. Parameters like --markov-threshold and --markov-inverse allow further customization, making the cracking process faster without reducing complexity.

By leveraging common user behavior, such as frequent patterns in passwords, Hashcat increases the efficiency of password cracking—a crucial tool for penetration testing and cybersecurity research.