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5 more programs were created to practice python.
Programs to make functions and Maching Learning in python.
This is a basic example of how you can add and delete rules in IPTables using Python. Note that these commands will need superuser permissions to run, so you may need to run your Python script with administrator privileges. This is a basic example of how you can add and delete rules in IPTables using Python. Note that these commands will need superuser permissions to run, so you may need to run your Python script with administrator privileges.
This script will add a rule to the IPTables firewall to allow SSH (Secure Shell) traffic on port 22.
This script will set a custom rule on the IPTables firewall that allows or blocks traffic from a specific source IP address to a specific destination IP address and port.
In this example, we will use a data set of Iris flowers to create a flower species classifier based on their characteristics.
RSA key securely and then serialize it into PEM format before loading it.
Random forest for encryption
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Everything updated correctly. |
This code assumes that you have a CSV file called "vulnerabilities_data.csv" that contains the data for vulnerabilities in the security system, where each row represents an instance of data and the "vulnerable" column indicates whether the system is vulnerable or not.
In this example, security event logs are used to detect system anomalies. Relevant features are selected from the data and normalized before training the model. The Isolation Forest algorithm is used, which is effective in detecting anomalies in large and multidimensional data sets. The model predictions are evaluated using classification metrics, such as precision, recall, and F1-score.
In this example, we first load data from the NSL-KDD dataset, which contains network activity logs labeled as benign or malicious. Then, we perform the necessary data preprocessing, such as removing non-relevant columns and encoding categorical variables. After splitting the data into training and test sets, we normalize the features so that they have a mean of zero and a standard deviation of one. Then, we use a neural network classifier (MLPClassifier) to train the model on the training set. Finally, we make predictions on the test set and evaluate the performance of the model using classification metrics, such as precision, recall, and F1-score.
In this example, we first load data from the CICIDS 2017 dataset, which contains labeled network traffic logs. Then, we perform the necessary data preprocessing, such as removing non-relevant columns and encoding categorical variables. After normalizing the data, we use the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm to detect anomalies in the data set. DBSCAN is an unsupervised clustering algorithm that can identify high-density regions in the feature space, making it suitable for anomaly detection in network traffic data sets. Finally, we assign anomaly (-1) and non-anomaly (1) labels to the data and evaluate the model performance using classification metrics. This advanced approach allows us to detect denial of service attacks in a network environment using unsupervised learning techniques.
In this example, we first load the data from the email dataset, where each email is labeled as phishing or legitimate. We then preprocess the data by tokenizing the text in the emails and converting them into numerical sequences. We build a CNN model using embedding layers to convert words into dense vectors, convolutional layers to extract important features from the text, and dense layers for final classification. We train the model on the training data and evaluate it on the test set. This advanced approach uses deep learning and natural language processing to detect phishing attacks in emails, leveraging the capabilities of convolutional neural networks to learn complex patterns in text data.
A file computer (Tells you what order your files should be in).
Automatic word corrector. You can fix a file by entering the specific file path (including the file obviously).
Project to have a defender of a file.
Implement a textual adventure game where players make decisions that affect the course of the story. You can include ASCII graphics to enhance the viewing experience.
A tool that captures and analyzes network packets using Scapy.
A Twitter Bot to publish RT and Sputnik News.
A tool to simulate brute force attacks and evaluate password security. (Any less educational use is the responsibility of the respective user executing it).
ASCII Art Generator: Convert images into ASCII art.
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