As an AI text-based model, I cannot directly execute code or access a real data set. However, I can provide you with an example of how you can develop a machine learning program using a cyber security related dataset in Python. I am assuming you are familiar with Python and have some basic understanding of machine learning concepts.

First, you will need a suitable dataset for your project. You can look for a dataset specific to the cyber security domain (e.g., intrusion detection, malware detection, or spam detection). One popular choice for intrusion detection is the NSL-KDD dataset. It can be downloaded from the following link: https://github.com/defcom17/NSL_KDD

Let’s create a simple machine learning program using the NSL-KDD dataset:

1. Install required libraries:

pip install pandas scikit-learn

2. Save the following code in a Python script (e.g., cyber_sec_ml.py):

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix, accuracy_score

# Load the dataset
def load_dataset(url):
    column_names = [‘duration’, ‘protocol_type’, ‘service’, ‘flag’, ‘src_bytes’,
                    ‘dst_bytes’, ‘land’, ‘wrong_fragment’, ‘urgent’, ‘hot’, ‘num_failed_logins’,
                    ‘logged_in’, ‘num_compromised’, ‘root_shell’, ‘su_attempted’, ‘num_root’,
                    ‘num_file_creations’, ‘num_shells’, ‘num_access_files’, ‘num_outbound_cmds’,
                    ‘is_host_login’, ‘is_guest_login’, ‘count’, ‘srv_count’, ‘serror_rate’,
                    ‘srv_serror_rate’, ‘rerror_rate’, ‘srv_rerror_rate’, ‘same_srv_rate’,
                    ‘diff_srv_rate’, ‘srv_diff_host_rate’, ‘dst_host_count’, ‘dst_host_srv_count’,
                    ‘dst_host_same_srv_rate’, ‘dst_host_diff_srv_rate’, ‘dst_host_same_src_port_rate’,
                    ‘dst_host_srv_diff_host_rate’, ‘dst_host_serror_rate’, ‘dst_host_srv_serror_rate’,
                    ‘dst_host_rerror_rate’, ‘dst_host_srv_rerror_rate’, ‘attack_type’, ‘difficulty_level’]

    dataset = pd.read_csv(url, header=None, names=column_names)
    return dataset

# Encode categorical features
def preprocess_data(dataset):
    categorical_features = [‘protocol_type’, ‘service’, ‘flag’]

    for feature in categorical_features:
        dataset = pd.concat([dataset, pd.get_dummies(dataset[feature], prefix=feature)], axis=1)
        dataset.drop(feature, axis=1, inplace=True)

    return dataset

# Load and preprocess the dataset
url = ‘https://raw.githubusercontent.com/defcom17/NSL_KDD/master/KDDTrain%2B.txt’
dataset = preprocess_data(load_dataset(url))

# Split the dataset into features (X) and target (y) variables
X = dataset.drop([‘attack_type’, ‘difficulty_level’], axis=1)
y = dataset[‘attack_type’]

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Train the model
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)

# Make predictions
y_pred = clf.predict(X_test)

# Evaluate the model
print(‘— Classification Report —’)
print(classification_report(y_test, y_pred))

print(‘— Confusion Matrix —’)
print(confusion_matrix(y_test, y_pred))

print(‘— Accuracy Score —’)
print(accuracy_score(y_test, y_pred))


3. Run the script:

python cyber_sec_ml.py

This script loads the NSL-KDD dataset, preprocesses the data by encoding categorical features, splits the dataset into training and testing sets, trains a RandomForestClassifier, makes predictions, and evaluates the classifier’s performance.

Keep in mind that you may need to explore different machine learning algorithms, tune their parameters, and perform additional preprocessing to improve the performance of your model. Prepare a detailed report on this experiment.