Applying Machine Learning to Detect Threats in Email and Network Systems
Budget: €250 – €750 EUR
Project Title: Master’s Thesis on Applying Machine Learning to Detect Threats in Email and Network Systems
Description:
I’m looking for someone to help write my master’s thesis with the following specifications:
• Topic: Applying Machine Learning to Detect Threats in Email and Network Systems.
• Length: Maximum 80 pages.
Objective:
The project aims to explore the use of Large Language Models (LLMs) for detecting security threats in emails and network logs. Since LLMs are proficient at text understanding, they should assist in identifying phishing attempts, suspicious behavior, or other anomalies related to cybersecurity.
Requirements:
1. Introduction to Threat Detection:
• Explanation of email and network threats, such as phishing, malware, and other cyber-attacks.
• Overview of machine learning applications in cybersecurity.
2. Machine Learning Models:
• Focus on using LLMs for detecting patterns and threats in textual data.
• Discuss other ML techniques that could complement LLMs in cybersecurity (optional).
3. Evaluation and Metrics:
• Implement a baseline weighted confusion matrix to evaluate model performance.
• This will prioritize critical errors, such as missing real threats, which are more important in security-focused evaluations.
4. Implementation:
• The code for threat detection should be written in Python or C++.
• A detailed explanation of how the model analyzes emails and network logs for potential threats.
5. Conclusion:
• Discuss potential improvements, challenges, and future research areas in ML-based threat detection.
Skills Required:
• Expertise in Machine Learning (particularly with LLMs).
• Familiarity with cybersecurity and threat detection systems.
• Proficiency in Python or C++ for coding implementations.
Description:
I’m looking for someone to help write my master’s thesis with the following specifications:
• Topic: Applying Machine Learning to Detect Threats in Email and Network Systems.
• Length: Maximum 80 pages.
Objective:
The project aims to explore the use of Large Language Models (LLMs) for detecting security threats in emails and network logs. Since LLMs are proficient at text understanding, they should assist in identifying phishing attempts, suspicious behavior, or other anomalies related to cybersecurity.
Requirements:
1. Introduction to Threat Detection:
• Explanation of email and network threats, such as phishing, malware, and other cyber-attacks.
• Overview of machine learning applications in cybersecurity.
2. Machine Learning Models:
• Focus on using LLMs for detecting patterns and threats in textual data.
• Discuss other ML techniques that could complement LLMs in cybersecurity (optional).
3. Evaluation and Metrics:
• Implement a baseline weighted confusion matrix to evaluate model performance.
• This will prioritize critical errors, such as missing real threats, which are more important in security-focused evaluations.
4. Implementation:
• The code for threat detection should be written in Python or C++.
• A detailed explanation of how the model analyzes emails and network logs for potential threats.
5. Conclusion:
• Discuss potential improvements, challenges, and future research areas in ML-based threat detection.
Skills Required:
• Expertise in Machine Learning (particularly with LLMs).
• Familiarity with cybersecurity and threat detection systems.
• Proficiency in Python or C++ for coding implementations.