GitBook: [#3165] No subject

Author: carlospolop

6881-udp-pentesting-bittorrent.md

@@ -1,4 +1,4 @@
-# 6881/udp - Pentesting BitTorrent
+
 
 <details>
 
@@ -16,6 +16,9 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
+
+
+
 <details>
 
 <summary><strong>Support HackTricks and get benefits!</strong></summary>
@@ -31,3 +34,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

a.i.-exploiting/bra.i.nsmasher-presentation/README.md

@@ -1,4 +1,4 @@
-# BRA.I.NSMASHER Presentation
+
 
 <details>
 
@@ -16,7 +16,8 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
-## Presentation
+
+# Presentation
 
 **BrainSmasher** is a platform made with the purpose of aiding **pentesters, researcher, students, A.I. Cybersecurity engineers** to practice and learn all the techniques for **exploiting commercial A.I.** applications, by working on specifically crafted labs that reproduce several systems, like face recognition, speech recognition, ensemble image classification, autonomous drive, malware evasion, chatbot, data poisoning etc...
 
@@ -36,7 +37,7 @@ _A big thanks to Hacktricks and Carlos Polop for giving us this opportunity_
 
 > _Walter Miele from BrA.I.nsmasher_
 
-## Registry Challenge
+# Registry Challenge
 
 In order to register in [**BrA.I.Smasher** ](https://beta.brainsmasher.eu)you need to solve an easy challenge ([**here**](https://beta.brainsmasher.eu/registrationChallenge)).\
 Just think how you can confuse a neuronal network while not confusing the other one knowing that one detects better the panda while the other one is worse...
@@ -47,12 +48,13 @@ However, if at some point you **don't know how to solve** the challenge, or **ev
 
 I have to tell you that there are **easier ways** to pass the challenge, but this **solution** is **awesome** as you will learn how to pass the challenge performing an **Adversarial Image performing a Fast Gradient Signed Method (FGSM) attack for images.**
 
-## More Tutorials
+# More Tutorials
 
 {% content-ref url="basic-captcha-breaker.md" %}
 [basic-captcha-breaker.md](basic-captcha-breaker.md)
 {% endcontent-ref %}
 
+
 <details>
 
 <summary><strong>Support HackTricks and get benefits!</strong></summary>
@@ -68,3 +70,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

a.i.-exploiting/bra.i.nsmasher-presentation/basic-bruteforcer.md

@@ -1,4 +1,4 @@
-# Basic Bruteforcer
+
 
 <details>
 
@@ -16,7 +16,8 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
-## BRUTEFORCER IMAGE CORRUPTION SCRIPT
+
+# BRUTEFORCER IMAGE CORRUPTION SCRIPT
 
 The purpose here is to introduce the user to some basic concepts about **A.I. apps exploiting**, via some easy to follow scripts, which represents the core for writing useful tools.\<br>\
 In this example (which can be used to solve the easy labs of BrainSmasher) by recalling also what is written in the solution for the introduction challenge, we will provide a simple yet useful way, in order to iteratively produce some corrupted images, to bruteforce the face recon easy labs (and thus also real applications that relies on the same principles)
@@ -29,6 +30,7 @@ Try it on our labs [**BrA.I.Smasher Website**](https://beta.brainsmasher.eu)
 
 Enjoy and stay safe!
 
+
 <details>
 
 <summary><strong>Support HackTricks and get benefits!</strong></summary>
@@ -44,3 +46,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

a.i.-exploiting/bra.i.nsmasher-presentation/basic-captcha-breaker.md

@@ -1,4 +1,4 @@
-# Basic Captcha Breaker
+
 
 <details>
 
@@ -16,10 +16,13 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
-In this tutorial **a basic captcha is going to be broken**.\
+
+In this tutorial **a basic captcha is going to be broken**.  
 A **NN is going to be trained** using several **images** that represents **letters** and then this NN is going to be used to **automatically identify the letters inside a captcha image**.
 
-Check the awesome guided tutorial provided by [**BrA.In Smasher**](https://beta.brainsmasher.eu) in this [**google collab page**](https://colab.research.google.com/drive/1uiQJpqEj5V2\_ijoumSd2noaDJuniTlKq?usp=sharing).
+Check the awesome guided tutorial provided by [**BrA.In Smasher**](https://beta.brainsmasher.eu/) in this [**google collab page**](https://colab.research.google.com/drive/1uiQJpqEj5V2_ijoumSd2noaDJuniTlKq?usp=sharing).
+
+
 
 <details>
 
@@ -36,3 +39,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

a.i.-exploiting/bra.i.nsmasher-presentation/bim-bruteforcer.md

@@ -1,4 +1,4 @@
-# BIM Bruteforcer
+
 
 <details>
 
@@ -16,9 +16,10 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
-## BRUTEFORCER CORE SCRIPT WITH BIM ATTACK
 
-This time we introduce a new type of gradient based attack, in order to brute force an image classification app (can be shaped and used for any input of course), the BIM, or Basic Iteration Method.
+# BRUTEFORCER CORE SCRIPT WITH BIM ATTACK
+
+This time we introduce a new type of gradient based attack, in order to brute force an image classification app \(can be shaped and used for any input of course\), the BIM, or Basic Iteration Method.
 
 It’s recommended to see at least the explanation in the [**introduction challenge colab Notebook**](https://colab.research.google.com/drive/1lDh0oZ3TR-z87WjogdegZCdtsUuDADcR)
 
@@ -30,6 +31,8 @@ As usual we will provide only the A.I. attack core part, it’s up to you to com
 Remember, in those kind of scenarios, in order to mime real-based attack applications, we don’t have the exact model to fool or the image target in which we would like to transform our image. That’s why, in order to overcome this issue, we must blend our core script, with a bruteforcer logic, accordingly to the application responses we want to fool.
 {% endhint %}
 
+
+
 <details>
 
 <summary><strong>Support HackTricks and get benefits!</strong></summary>
@@ -45,3 +48,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

a.i.-exploiting/bra.i.nsmasher-presentation/hybrid-malware-classifier-part-1.md

@@ -1,4 +1,4 @@
-# Hybrid Malware Classifier Part 1
+
 
 <details>
 
@@ -16,9 +16,10 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
-## A.I. HYBRID MALWARE CLASSIFIER
 
-### INTERMEDIATE PYTHON SKILL, INTERMEDIATE MACHINE LEARNING SKILLS (Part 1)
+# A.I. HYBRID MALWARE CLASSIFIER
+
+## INTERMEDIATE PYTHON SKILL, INTERMEDIATE MACHINE LEARNING SKILLS \(Part 1\)
 
 In this series of notebook we are going to build an **hybrid malware classifier.**
 
@@ -34,7 +35,9 @@ For the **Fourth Part** For the Fourth Part we will add some tactics to add robu
 
 There are also many available datasets for Static and/ or Dynamic Malware analysis on several sites for this type of classification, like Ember, VirusShare, Sorel-20M, but i strongly encourage that you build one or your own.
 
-Here’s the link to our [**colab notebook**](https://colab.research.google.com/drive/1nNZLMogXF-iq-\_78IvGTd-c89\_C82AB8#scrollTo=lUHLMl8Pusrn) enjoy and stay safe :)
+Here’s the link to our [**colab notebook**](https://colab.research.google.com/drive/1nNZLMogXF-iq-_78IvGTd-c89_C82AB8#scrollTo=lUHLMl8Pusrn) enjoy and stay safe :\)
+
+
 
 <details>
 
@@ -51,3 +54,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

a.i.-exploiting/bra.i.nsmasher-presentation/ml-basics/README.md

@@ -1,4 +1,4 @@
-# ML Basics
+
 
 <details>
 
@@ -16,6 +16,9 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
+
+
+
 <details>
 
 <summary><strong>Support HackTricks and get benefits!</strong></summary>
@@ -31,3 +34,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

a.i.-exploiting/bra.i.nsmasher-presentation/ml-basics/feature-engineering.md

@@ -1,4 +1,4 @@
-# Feature Engineering
+
 
 <details>
 
@@ -16,21 +16,22 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
-## Basic types of possible data
+
+# Basic types of possible data
 
 Data can be **continuous** (**infinity** values) or **categorical** (nominal) where the amount of possible values are **limited**.
 
-### Categorical types
+## Categorical types
 
-#### Binary
+### Binary
 
 Just **2 possible values**: 1 or 0. In case in a dataset the values are in string format (e.g. "True" and "False") you assign numbers to those values with:
 
 ```python
 dataset["column2"] = dataset.column2.map({"T": 1, "F": 0})
 ```
 
-#### **Ordinal**
+### **Ordinal**
 
 The **values follows an order**, like in: 1st place, 2nd place... If the categories are strings (like: "starter", "amateur", "professional", "expert") you can map them to numbers as we saw in the binary case.
 
@@ -49,7 +50,7 @@ possible_values_mapping = {value:idx for idx,value in enumerate(possible_values_
 dataset['column2'] = dataset.column2.map(possible_values_mapping)
 ```
 
-#### **Cyclical**
+### **Cyclical**
 
 Looks **like ordinal value** because there is an order, but it doesn't mean one is bigger than the other. Also the **distance between them depends on the direction** you are counting. Example: The days of the week, Sunday isn't "bigger" than Monday.
 
@@ -60,7 +61,7 @@ column2_dummies = pd.get_dummies(dataset.column2, drop_first=True)
 dataset_joined = pd.concat([dataset[['column2']], column2_dummies], axis=1)
 ```
 
-#### **Dates**
+### **Dates**
 
 Date are **continuous** **variables**. Can be seen as **cyclical** (because they repeat) **or** as **ordinal** variables (because a time is bigger than a previous one).
 
@@ -94,7 +95,7 @@ dataset['weekday'] = dataset.transaction_date.dt.weekday
 dataset['day_name'] = dataset.transaction_date.apply(lambda x: x.day_name())
 ```
 
-#### Multi-category/nominal
+### Multi-category/nominal
 
 **More than 2 categories** with no related order. Use `dataset.describe(include='all')` to get information about the categories of each feature.
 
@@ -107,7 +108,7 @@ You can get a **multi-category column one-hot encoded** with `pd.get_dummies(dat
 
 You can get a **multi-category column dummie encoded** with `pd.get_dummies(dataset.column1, drop_first=True)`. This will transform all the classes in binary features, so this will create **one new column per possible class minus one** as the **last 2 columns will be reflect as "1" or "0" in the last binary column created**. This will avoid perfect multicollinearity, reducing the relations between columns.
 
-## Collinear/Multicollinearity
+# Collinear/Multicollinearity
 
 Collinear appears when **2 features are related to each other**. Multicollineratity appears when those are more than 2.
 
@@ -125,7 +126,7 @@ X = add_constant(onehot_encoded) # Add previously one-hot encoded data
 print(pd.Series([variance_inflation_factor(X.values,i) for i in range(X.shape[1])], index=X.columns))
 ```
 
-## Categorical Imbalance
+# Categorical Imbalance
 
 This occurs when there is **not the same amount of each category** in the training data.
 
@@ -174,7 +175,7 @@ You can use the argument **`sampling_strategy`** to indicate the **percentage**
 Undersamplig or Oversampling aren't perfect if you get statistics (with `.describe()`) of the over/under-sampled data and compare them to the original you will see **that they changed.** Therefore oversampling and undersampling are modifying the training data.
 {% endhint %}
 
-### SMOTE oversampling
+## SMOTE oversampling
 
 **SMOTE** is usually a **more trustable way to oversample the data**.
 
@@ -189,13 +190,13 @@ dataset['target_column'] = y_smote
 print(y_smote.value_counts()) #Confirm data isn't imbalanced anymore
 ```
 
-## Rarely Occurring Categories
+# Rarely Occurring Categories
 
 Imagine a dataset where one of the target classes **occur very little times**.
 
 This is like the category imbalance from the previous section, but the rarely occurring category is occurring even less than "minority class" in that case. The **raw** **oversampling** and **undersampling** methods could be also used here, but generally those techniques **won't give really good results**.
 
-### Weights
+## Weights
 
 In some algorithms it's possible to **modify the weights of the targeted data** so some of them get by default more importance when generating the model.
 
@@ -206,13 +207,13 @@ model = LogisticRegression(class_weight=weights)
 
 You can **mix the weights with over/under-sampling techniques** to try to improve the results.
 
-### PCA - Principal Component Analysis
+## PCA - Principal Component Analysis
 
 Is a method that helps to reduce the dimensionality of the data. It's going to **combine different features** to **reduce the amount** of them generating **more useful features** (_less computation is needed_).
 
 The resulting features aren't understandable by humans, so it also **anonymize the data**.
 
-## Incongruent Label Categories
+# Incongruent Label Categories
 
 Data might have mistakes for unsuccessful transformations or just because human error when writing the data.
 
@@ -222,7 +223,7 @@ You can clean this issues by lowercasing everything and mapping misspelled label
 
 It's very important to check that **all the data that you have contains is correctly labeled**, because for example, one misspelling error in the data, when dummie encoding the classes, will generate a new column in the final features with **bad consequences for the final model**. This example can be detected very easily by one-hot encoding a column and checking the names of the columns created.
 
-## Missing Data
+# Missing Data
 
 Some data of the study may be missing.
 
@@ -290,7 +291,7 @@ dataset.iloc[10:20] # Get some indexes that contained empty data before
 
 To fill categorical data first of all you need to think if there is any reason why the values are missing. If it's by **choice of the users** (they didn't want to give the data) maybe yo can **create a new category** indicating that. If it's because of human error you can **remove the rows** or the **feature** (check the steps mentioned before) or **fill it with the mode, the most used category** (not recommended).
 
-## Combining Features
+# Combining Features
 
 If you find **two features** that are **correlated** between them, usually you should **drop** one of them (the one that is less correlated with the target), but you could also try to **combine them and create a new feature**.
 
@@ -307,6 +308,7 @@ X = add_constant(dataset[['column1', 'column2', 'target']])
 pd.Series([variance_inflation_factor(X.values, i) for i in range(X.shape[1])], index=X.columns)
 ```
 
+
 <details>
 
 <summary><strong>Support HackTricks and get benefits!</strong></summary>
@@ -322,3 +324,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+

backdoors/empire.md

@@ -1,4 +1,4 @@
-# Empire
+
 
 <details>
 
@@ -16,6 +16,9 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 
 </details>
 
+
+
+
 <details>
 
 <summary><strong>Support HackTricks and get benefits!</strong></summary>
@@ -31,3 +34,5 @@ Get the [**official PEASS & HackTricks swag**](https://peass.creator-spring.com)
 **Share your hacking tricks submitting PRs to the** [**hacktricks github repo**](https://github.com/carlospolop/hacktricks)**.**
 
 </details>
+
+