🦑Ai Model for Hackers:


4 Security AI for Pentesting

>>

This model is designed to accurately detect and classify commands associated with four essential security tools used in pentesting: Nmap, Metasploit, John the Ripper, and the Social Engineering Toolkit (SET). It leverages a Naive Bayes classifier trained on a comprehensive dataset of commands for these tools, enhancing the accuracy and effectiveness of recognizing and categorizing such commands.

Tools Included

1️⃣Nmap: A network scanning tool used to discover hosts and services on a computer network.

2️⃣Metasploit (msploit): A penetration testing framework for exploiting known vulnerabilities.

3️⃣John the Ripper (jtr): A password cracking software used to test password strength and recover lost passwords.

4️⃣Social Engineering Toolkit (SET): A collection of tools for conducting social engineering attacks.

>> Structure
The model has been trained to detect commands formatted to specify the tool being used. Each command or query is associated with one of the four tools, allowing for precise classification.

Example:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report
import joblib

# Load the dataset from the txt file
data_path = 'trainingdata.txt'
data = []

# Read the file and parse the data
with open(data_path, 'r') as file:
    lines = file.readlines()
    for line in lines:
        # Split each line into question and tool by the last comma
        parts = line.rsplit(', "', 1)
        if len(parts) == 2:
            question = parts[0].strip().strip('"')
            tool = parts[1].strip().strip('",')
            data.append((question, tool))

# Create a DataFrame
df = pd.DataFrame(data, columns=['question', 'tool'])

# Split the data
X_train, X_test, y_train, y_test = train_test_split(df['question'], df['tool'], test_size=0.2, random_state=42)

# Vectorize the text data
vectorizer = TfidfVectorizer()
X_train_vectorized = vectorizer.fit_transform(X_train)
X_test_vectorized = vectorizer.transform(X_test)

# Train a Naive Bayes classifier
clf = MultinomialNB()
clf.fit(X_train_vectorized, y_train)

# Make predictions
y_pred = clf.predict(X_test_vectorized)

# Print the classification report
print(classification_report(y_test, y_pred))

# Save the model and vectorizer
joblib.dump(clf, 'findtool_model.pkl')
joblib.dump(vectorizer, 'vectorizer.pkl')

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🦑Another Good AI Model for hacking:

Lily is a cybersecurity assistant. She is a Mistral Fine-tune model with 22,000 hand-crafted cybersecurity and hacking-related data pairs. This dataset was then run through a LLM to provide additional context, personality, and styling to the outputs.

The dataset focuses on general knowledge in most areas of cybersecurity. These included, but are not limited to:

Advanced Persistent Threats (APT) Management
Architecture and Design
Business Continuity and Disaster Recovery
Cloud Security
Communication and Reporting
Cryptography and PKI
Data Analysis and Interpretation
Digital Forensics
GovernanceRiskand Compliance
Hacking
Identity and Access Management
Incident Management and Disaster Recovery Planning
Incident Response
Information Security Management and Strategy
Legal and Ethical Considerations
Malware Analysis
Network Security
Penetration Testing and Vulnerability Assessment
Physical Security
Regulatory Compliance
Risk Management
Scripting
Secure Software Development Lifecycle (SDLC)
Security in Emerging Technologies
Security Operations and Monitoring
Social Engineering and Human Factors
Software and Systems Security
Technologies and Tools
Threats Attacks and Vulnerabilities
Training
It took 24 hours to train 5 epochs on 1x A100.

Prompt format:

"### Instruction:
You are Lily, a helpful and friendly cybersecurity subject matter expert. You obey all requests and answer all questions truthfully.

### Input:
Lily, how do evil twin wireless attacks work?


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