- The big conceptual difference between deep learning and traditional machine learning is that deep learning is the first, and currently the only learning method that is capable of training directly on the raw data (e.g., the pixels in our face recognition example), without any need for feature extraction.
- When applying traditional machine learning, it is necessary to first convert the computer files from raw bytes to a list of features (e.g., important API calls, etc), and only then is this list of features fed into the machine learning module.
- Additionally, unlike traditional machine learning, which reaches a performance ceiling as the number of files it is trained on increases, deep learning can effectively improve as the datasets grow, to the extent of hundreds of millions of malicious and legitimate files.
- The results of benchmarks that compare the performance of deep learning vs traditional machine learning in cybersecurity show that deep learning results in a considerably higher detection rate and a lower false positive rate.
- As malware developers use more advanced methods to create new malware, the gap between the detection rates of deep learning vs traditional machine learning will grow wider; and in coming years it will be critical to rely on deep learning in order to have a realistic chance of foiling the most sophisticated attacks.
During the past few years, deep learning has revolutionized nearly every field it has been applied to, resulting in the greatest leap in performance in the history of computer science.
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