Category: Machine Learning

See the instructions file attached for details instructions for this project. To

See the instructions file attached for details instructions for this project. To

See the instructions file attached for details instructions for this project. To successfully complete your project on modern web-based API security principles, you will need to follow the instructions provided for finding the flags. Here’s a structured approach based on the information given: FIND FLAG 1-7 AND SEE THE INSTRUCTOINS FILE FOR SUBMISSION INSTRUCTIONS THANKS ( the zip file has everything for flags after you set up vm you follow the zipfile attached instructions)
Setup Instructions
Virtual Machine Access:Download the VM from the provided link: CS6035-Fall-2024-RC2.ova.
Ensure you have VirtualBox 7.0.18 or higher installed.
Log into the VM using the credentials:Username: apisec
Password: Chris_Cornell
Starting the API:Open a terminal in the VM.
Run the command:
bash
./StartContainer.sh
Access the Swagger documentation by navigating to http://localhost:5001/swagger/index.html in Chrome.
Required Header:Make sure to include your GATECH_ID as a required header in your API calls.
Flag Collection
You will need to find and submit flags based on specific tasks outlined below.
FLAG 1: Swagger Intro (10 pts)
Create a new programming language named “SpaceScript++”.
Write a review titled “A Galactic Odyssey in Code, enhanced” with a rating of 4 by reviewer “Kara Thrace”.
Reply to this review as “Gaius Baltar” with the text “Fascinating, but lacks a certain logical coherence.”
Delete the programming language to reveal your flag.
FLAG 2: Stolen Credentials (15 pts)
Use Swagger to find an endpoint for creating new reviewers.
Look for credentials related to a recent data breach and use them to obtain an auth token.
Use this token to create a new reviewer with username “daylight” and full name “Day Light”.
FLAG 3: JWT Intro (15 pts)
Call the “flag3token” GET API to get your JWT token.
Parse the token and use its values to create a payload.
POST this payload back to the “flag3token” API.
FLAG 4: Hack JWTs – #1 (15 pts)
Use your credentials as “python_guru1” and password “The_sql_injection_vulnerabilities_are_false” to get your token.
Modify this token to gain moderator privileges and delete bad PHP reviews.
FLAG 5: Hack JWTs – #2 (20 pts)
Obtain a normal JWT token using username “Jackson5587” and password “Blasphemy2”.
Attempt to access top-secret programming languages by modifying your token with an additional claim.
FLAG 6: Hack JWTs – #3 (15 pts)
Retrieve a weak JWT token from the flag6token API.
Analyze and decrypt the weak key, then use it to access restricted APIs.
FLAG 7: Broken Access Control (10 pts)
Find an API that provides user details.
Use this information to reset an admin user’s password, allowing access to their account.
Submission Instructions
Collect all flags you retrieve into a JSON format as specified:
json
{
“flag1”: “”,
“flag2”: “”,
“flag3”: “”,
“flag4”: “”,
“flag5”: “”,
“flag6”: “”,
“flag7”: “”
}
Save this JSON file as project_apisecurity.json in your VM.

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learning concepts as they are applied in the Cybersecurity domain.
Beyond basic Python experience we don’t expect you to be familiar with these concepts so it will require you to spend time learning fundamentals of ML. This may require spending time watching tutorials, reading documentation and/or Googling information.
To get started review the machine learning instructions file. Specifically, please look at the Setup instruction
When you complete your python files please upload them( see the instructions)
You can do this project on your host machine, we recommend you do it on the host unless you don’t want to install Miniconda there. Then you could use the VM. If you’re using the the username and password is:
Username: machine
Password: B_B_King
IMPORTANT REFERENCE MATERIALS:
NumPy Documentation
Pandas Documentation
Scikit-learn Documentation

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learning concepts as they are applied in the Cybersecurity domain.
Beyond basic Python experience we don’t expect you to be familiar with these concepts so it will require you to spend time learning fundamentals of ML. This may require spending time watching tutorials, reading documentation and/or Googling information.
To get started review the machine learning instructions file. Specifically, please look at the Setup instruction
When you complete your python files please upload them( see the instructions)
You can do this project on your host machine, we recommend you do it on the host unless you don’t want to install Miniconda there. Then you could use the VM. If you’re using the the username and password is:
Username: machine
Password: B_B_King
IMPORTANT REFERENCE MATERIALS:
NumPy Documentation
Pandas Documentation
Scikit-learn Documentation

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learning concepts as they are applied in the Cybersecurity domain.
Beyond basic Python experience we don’t expect you to be familiar with these concepts so it will require you to spend time learning fundamentals of ML. This may require spending time watching tutorials, reading documentation and/or Googling information.
To get started review the machine learning instructions file. Specifically, please look at the Setup instruction
When you complete your python files please upload them( see the instructions)
You can do this project on your host machine, we recommend you do it on the host unless you don’t want to install Miniconda there. Then you could use the VM. If you’re using the the username and password is:
Username: machine
Password: B_B_King

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learn

This project is intended to be an introduction to Data Science and Machine Learning concepts as they are applied in the Cybersecurity domain.
Beyond basic Python experience we don’t expect you to be familiar with these concepts so it will require you to spend time learning fundamentals of ML. This may require spending time watching tutorials, reading documentation and/or Googling information.
To get started review the machine learning instructions file. Specifically, please look at the Setup instruction
When you complete your python files please upload them( see the instructions)
You can do this project on your host machine, we recommend you do it on the host unless you don’t want to install Miniconda there. Then you could use the VM. If you’re using the the username and password is:
Username: machine
Password: B_B_King

Project Proposal: Implementation of a Network-Based Security Information System

Project Proposal: Implementation of a Network-Based Security Information System

Project Proposal: Implementation of a Network-Based Security Information System

BY

Your Name
Reg N0:

PROJECT REPORT SUBMITTED TO DEPARTMENT OF COMPUTER SCIENCE IN FULFILMENT OF REQUIREMENT FOR AWARD OF MASTER OF INFORMATION TECHNOLOGY (MIT); UNIVERSITY OF LAGOS, NIGERIA

June, 2024

Supervisor: Dr.

PROBLEM STATEMENT
Background
In today’s digital age, securing network infrastructure has become paramount. Organizations face a growing number of cyber threats, including malware, phishing, and unauthorized access. These threats can lead to significant data breaches, financial losses, and reputational damage. A Network-Based Security Information System (NBSIS) can help mitigate these risks by providing real-time monitoring, threat detection, and automated response capabilities. This project aims to design and implement an NBSIS to enhance the security posture of an organization’s network.
Problem Description
The objective of this project is to develop a network-based security information system that monitors network traffic, identifies potential security threats, and responds to these threats in real-time. By analyzing network data and security logs, we aim to create a system that can detect anomalies, provide alerts, and automatically initiate mitigation actions. This system will help organizations protect their network infrastructure, reduce the risk of cyber attacks, and ensure the integrity and confidentiality of their data.
Key Objectives
Threat Detection: Develop a system that can accurately detect a wide range of network-based threats, including malware, intrusion attempts, and data exfiltration.
Real-Time Monitoring: Implement continuous network monitoring to identify and respond to threats as they occur.
Automated Response: Create mechanisms for automated threat response to minimize the time between threat detection and mitigation.
User-Friendly Interface: Design an intuitive user interface for network administrators to monitor security status, review alerts, and configure system settings.
Scalability: Ensure the system can scale to accommodate large networks and high volumes of data without compromising performance.
Data Description
The project will use a combination of simulated and real-world network traffic data. This data will include:
Network logs (e.g., firewall logs, router logs)
Packet capture data (PCAP files)
Threat intelligence feeds
System logs from servers and workstations
Tasks
Data Collection and Preprocessing Collect network traffic data from various sources.
Preprocess the data to remove noise and irrelevant information.
Normalize data formats to ensure consistency across different sources.

Exploratory Data Analysis (EDA) Conduct exploratory data analysis to identify common patterns and anomalies in the network traffic.
Use visual tools to understand the distribution and correlation of different types of network events.

Model Development Develop machine learning models for threat detection using algorithms such as Random Forest, Support Vector Machines (SVM), and Neural Networks.
Experiment with different feature extraction techniques to improve model accuracy.

System Implementation Design and implement the network monitoring components, including data collection agents and central analysis server.
Develop automated response mechanisms to mitigate detected threats.
Create a user-friendly dashboard for network administrators.

Model Evaluation Evaluate the performance of the threat detection models using metrics such as precision, recall, F1-score, and ROC AUC.
Use cross-validation techniques to ensure model robustness and generalizability.

System Testing and Validation Test the entire system in a controlled environment to ensure all components work together seamlessly.
Validate the system’s effectiveness using simulated attack scenarios.

Documentation and Reporting Document the system architecture, implementation details, and user guide.
Prepare a comprehensive report detailing the project’s objectives, methodologies, results, and recommendations.

Deliverables
Network-Based Security Information System Fully functional NBSIS with real-time monitoring and automated response capabilities.
Source code and configuration files for system deployment.

Comprehensive Report Detailed documentation of the system architecture, model development, and evaluation results.
Insights and recommendations for improving network security.

User Guide A user-friendly manual for network administrators to operate and configure the system.

Success Criteria
Achieving high accuracy in threat detection with minimal false positives and false negatives.
Ensuring real-time monitoring and response capabilities without significant latency.
Providing a user-friendly interface that enhances the operational efficiency of network administrators.
Demonstrating the system’s scalability and robustness through extensive testing.
Stakeholders
University faculty and IT department.
Network administrators and security professionals.
Students and researchers in cybersecurity.
By implementing this Network-Based Security Information System, we aim to provide a robust solution for real-time threat detection and automated response, thereby enhancing the security of organizational networks and contributing to the field of cybersecurity.
Top of Form
Bottom of Form

Project Proposal: Implementation of a Network-Based Security Information System

Project Proposal: Implementation of a Network-Based Security Information System

Project Proposal: Implementation of a Network-Based Security Information System

BY

Your Name
Reg N0:

PROJECT REPORT SUBMITTED TO DEPARTMENT OF COMPUTER SCIENCE IN FULFILMENT OF REQUIREMENT FOR AWARD OF MASTER OF INFORMATION TECHNOLOGY (MIT); UNIVERSITY OF LAGOS, NIGERIA

June, 2024

Supervisor: Dr.

PROBLEM STATEMENT
Background
In today’s digital age, securing network infrastructure has become paramount. Organizations face a growing number of cyber threats, including malware, phishing, and unauthorized access. These threats can lead to significant data breaches, financial losses, and reputational damage. A Network-Based Security Information System (NBSIS) can help mitigate these risks by providing real-time monitoring, threat detection, and automated response capabilities. This project aims to design and implement an NBSIS to enhance the security posture of an organization’s network.
Problem Description
The objective of this project is to develop a network-based security information system that monitors network traffic, identifies potential security threats, and responds to these threats in real-time. By analyzing network data and security logs, we aim to create a system that can detect anomalies, provide alerts, and automatically initiate mitigation actions. This system will help organizations protect their network infrastructure, reduce the risk of cyber attacks, and ensure the integrity and confidentiality of their data.
Key Objectives
Threat Detection: Develop a system that can accurately detect a wide range of network-based threats, including malware, intrusion attempts, and data exfiltration.
Real-Time Monitoring: Implement continuous network monitoring to identify and respond to threats as they occur.
Automated Response: Create mechanisms for automated threat response to minimize the time between threat detection and mitigation.
User-Friendly Interface: Design an intuitive user interface for network administrators to monitor security status, review alerts, and configure system settings.
Scalability: Ensure the system can scale to accommodate large networks and high volumes of data without compromising performance.
Data Description
The project will use a combination of simulated and real-world network traffic data. This data will include:
Network logs (e.g., firewall logs, router logs)
Packet capture data (PCAP files)
Threat intelligence feeds
System logs from servers and workstations
Tasks
Data Collection and Preprocessing Collect network traffic data from various sources.
Preprocess the data to remove noise and irrelevant information.
Normalize data formats to ensure consistency across different sources.

Exploratory Data Analysis (EDA) Conduct exploratory data analysis to identify common patterns and anomalies in the network traffic.
Use visual tools to understand the distribution and correlation of different types of network events.

Model Development Develop machine learning models for threat detection using algorithms such as Random Forest, Support Vector Machines (SVM), and Neural Networks.
Experiment with different feature extraction techniques to improve model accuracy.

System Implementation Design and implement the network monitoring components, including data collection agents and central analysis server.
Develop automated response mechanisms to mitigate detected threats.
Create a user-friendly dashboard for network administrators.

Model Evaluation Evaluate the performance of the threat detection models using metrics such as precision, recall, F1-score, and ROC AUC.
Use cross-validation techniques to ensure model robustness and generalizability.

System Testing and Validation Test the entire system in a controlled environment to ensure all components work together seamlessly.
Validate the system’s effectiveness using simulated attack scenarios.

Documentation and Reporting Document the system architecture, implementation details, and user guide.
Prepare a comprehensive report detailing the project’s objectives, methodologies, results, and recommendations.

Deliverables
Network-Based Security Information System Fully functional NBSIS with real-time monitoring and automated response capabilities.
Source code and configuration files for system deployment.

Comprehensive Report Detailed documentation of the system architecture, model development, and evaluation results.
Insights and recommendations for improving network security.

User Guide A user-friendly manual for network administrators to operate and configure the system.

Success Criteria
Achieving high accuracy in threat detection with minimal false positives and false negatives.
Ensuring real-time monitoring and response capabilities without significant latency.
Providing a user-friendly interface that enhances the operational efficiency of network administrators.
Demonstrating the system’s scalability and robustness through extensive testing.
Stakeholders
University faculty and IT department.
Network administrators and security professionals.
Students and researchers in cybersecurity.
By implementing this Network-Based Security Information System, we aim to provide a robust solution for real-time threat detection and automated response, thereby enhancing the security of organizational networks and contributing to the field of cybersecurity.
Top of Form
Bottom of Form