Here are a few potential PhD topics in cyber-security or machine learning:

These are just a few examples of potential PhD topics in cyber-security or machine learning. It's important to choose a topic that aligns with your interests and expertise, and that has the potential to make a significant contribution to the field.

Ahshanul Haque Thank you so much, its has helped me a lot on deciding what to do, I am mostly interested in topic 5 you suggested , hoping to apply it for mobile health applications. Please provide me any resources you got relevant to that topic.

1. Exploring Machine Learning Techniques for Detecting and Responding to Cybersecurity Incidents. 2. Investigating the Use of Deep Learning for Enhancing Cybersecurity. 3. An Analysis of the Impact of Machine Learning on Cybersecurity Infrastructure. 4. Investigating the Use of Automation in Cybersecurity. 5. Evaluation of Machine Learning Algorithms for Intrusion Detection. 6. Exploring Machine Learning Methods for Network Security. 7. An Analysis of Machine Learning in Malware Detection. 8. Investigating the Use of Machine Learning for Intrusion Detection. 9. Applications of Machine Learning in Detecting and Mitigating Cyberattacks. 10. Investigating the Use of Machine Learning for Enhancing Cyber security.

1. Analyzing the Effect of Malware on Machine Learning Models

2. Exploring the Impact of Adversarial Examples on Cybersecurity

3. Investigating the Use of Machine Learning for Intrusion Detection

4. Developing Techniques for Automated Credential Stuffing Detection

5. Investigating the Use of Machine Learning for Automated Vulnerability Detection

6. Studying the Impact of AI-Assisted Cyberattacks on Cybersecurity

7. Developing Machine Learning Techniques for Automated Malware Detection

8. Exploring the Use of Machine Learning for Automated Exploit Detection

9. Analyzing the Effectiveness of Machine Learning for Cyber Forensics

10. Investigating the Use of Machine Learning for Automated Phishing Detection

Hi, Nipuna Sankalpa You can see the papers I contributed in Malware detection. Hope it helps.

Preprint Android Malware Detection using Machine learning: A Review

Chapter Android Malware Detection in Large Dataset: Smart Approach

Let me know if you need help in these areas.

Wali Mohd Dar Thank you so much, it gave me an great idea about what I should look into

I would suggest that research needs to be done on techniques of corruption of Generative AI by the introduction of malware that presents biased data to change the results incorporated by the AI "learning algorithms" in a malicious way, and so are able to bias the "training data" incorporated into generation of extensions to the analysis routines. It is concerning to me that most of the recent work I have seen seems to be concentrating on the "short term" result, and not very much is looking for "longer term" (i.e. the focus of Malware detection using Machine learning is on immediate detection and seems to be lacking consideration of an attack based on gradual corruption introduced over longer and even potentially asynchronous time periods)

AI/ML - development of user stories/use cases that advance the automation of cyber security operations through AI/ML technology, for e.g., threat intelligence (TI) parsing, SIEM configuration and operation, triage of security events for false positive indicators.

Traditionally, this should be discussed and agreed upon with your advisor, so I will just give you a general advice:

There is no reason for you to select security OR AI. There is some emerging research in the intersection of the two topics. For a list of recent research and suggest topics you can look at the following CfP for a HICSS 2024 mini-track on Information Security in the era of Artificial General Intelligence (AGI)

https://hicss.hawaii.edu/tracks-57/decision-analytics-and-service-science/#information-security-in-the-era-of-artificial-general-intelligence-agi-minitrack

Here are some Ph.D. topic suggestions for cybersecurity, considering app security, context-aware systems, and small devices including embedded control systems, IoT applications, and sensor networks:

Alternatively, here are some Ph.D. topic suggestions related to AI and Machine Learning in cybersecurity:

1. Adversarial Machine Learning: Developing Robust and Resilient Algorithms against Adversarial Attacks"

2. "Privacy-Preserving Machine Learning Techniques: Novel Approaches for Secure Data Sharing and Collaborative Learning"

3. "Advanced Techniques for Network Security: Analyzing Cyber Threats and Developing Effective Defense Strategies"

4. "AI-Enabled Cybersecurity: Developing Automated and Intelligent Security Solutions to Combat Emerging Threats"

5. "Deep Learning for Malware Detection and Analysis: Developing Advanced Algorithms for Early Detection and Response"

6. "Secure and Private Federated Learning: Designing Robust and Efficient Distributed Learning Algorithms"

7. "Machine Learning for Cyber Forensics: Developing Advanced Techniques for Digital Evidence Analysis and Attribution"

8. "Blockchain and Cybersecurity: Developing Secure and Efficient Blockchain-Based Systems for Data Protection and Authentication"

9. "Secure and Explainable AI: Developing Transparent and Trustworthy Machine Learning Models for Critical Applications"

10. "Next-Generation Cybersecurity: Integrating Machine Learning, Big Data Analytics, and IoT Technologies for Advanced Threat Detection and Mitigation."

Each of these topics offers a rich avenue for exploration and innovation in the intersection of cybersecurity, machine learning, and AI. They are particularly relevant given the growing complexity and interconnectedness of modern digital systems. For materials and resources, academic journals, conferences in cybersecurity and AI, and collaboration with industry practitioners would provide valuable insights and state-of-the-art knowledge in these areas.