Summary
Throughout this chapter, we embarked on a comprehensive journey through malware and network intrusion detection and classification. We delved deep into the advantages of AI compared to traditional methodologies. We then ventured into publicly available datasets, recognizing them as invaluable resources for training and validating detection models. By leveraging these datasets, we equipped ourselves with the tools to develop and fine-tune our models effectively, ensuring their efficacy in real-world scenarios.
Two hands-on exercises provided a practical avenue for applying theoretical knowledge, allowing us to actively engage in the model development process. Through these exercises, we honed our skills in crafting tailored solutions for detecting malware and identifying malicious network traffic, bridging the gap between theory and practice. Finally, we discussed the transition from detection to classification.
In summary, this chapter has equipped us with a comprehensive...
