Inspiration
The increasing sophistication of malware and the growing number of cyber attacks inspired me to explore advanced solutions for threat detection. I wanted to leverage artificial intelligence to provide a proactive approach to cyber security, offering more accurate and real-time protection against evolving threats.
What it does
This project uses machine learning to distinguish between malicious and benign software behaviour in real-time. It analyses system activity, predicts threats, and detects malware before it can do any harm, helping to keep systems secure.
How we built it
I developed the project using Python, focusing on data processing and model implementation. I trained the machine learning models with a comprehensive dataset of both malware and benign behaviour. I used libraries like scikit-learn and TensorFlow to build and optimise the models, capturing various runtime logs from the system to analyse behaviour.
Challenges we ran into
One of the biggest challenges was obtaining diverse dataset for training our model. It was crucial to ensure the model could accurately differentiate between complex malware patterns and normal activity without generating false positives. Additionally, I had to optimise the system for real-time detection while maintaining performance, which required a lot of tweaking.
Accomplishments that we're proud of
I’m really proud of developing a system that can accurately detect malicious behaviour in real-time. Balancing detection accuracy with performance, without overwhelming the system, was a significant achievement for me.
What we learned
This project taught me a lot about machine learning in the context of cyber security. I gained insights into the intricacies of malware behaviour, learned how to engineer features for better threat detection, and improved my skills in enhancing models for real-time applications.
What's next for Malware Detection and Analysis Using Machine Learning
Looking ahead, I plan to enhance the project by integrating more advanced algorithms and expanding the dataset for better coverage. I also want to connect the system to cloud-based threat intelligence platforms to boost detection capabilities. Plus, I would like to add some strategies to recover the system from the malware attack as well as to add prevention measures in avoiding the malware attacks.
Built With
- cuckoosandbox
- jupyter-notebook
- keras
- numpy
- pandas
- pefile
- pypdf2
- python
- scikit-learn
- tensorflow
- virustotal
