MIT Review Addresses Cyber Threats Within Deep Learning
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Cybercrime is on the rise, and that isn’t a notion that can be disproven. During the first quarter of 2022 there were 404 publicly reported data breaches in the US alone and a rise of 13% in a single year, a dangerous acceleration despite the technological progress in the field of cyber security. It is no surprise that many companies and organizations have opted to search for the most advanced methods of protecting themselves against cyber threats. MIT scientists and leading network defenders urge these companies to explore deep learning as a way to secure systems. The ability of deep learning to mimic the human brain might be able to outsmart even the world’s fastest and most dangerous cyber threats.
MIT Technology Review has published a research paper regarding deep learning and malware prevention in hopes that it will encourage organizations to turn to innovation in their fight against cybercrime. According to cybernews.com, deep learning is the most advanced form of AI technology that uses neural networks to instinctively and autonomously anticipate and prevent unknown malware and zero-day attacks. Deep learning is also praised in the MIT paper for addressing the limitations of machine learning by circumventing the need for highly skilled and experienced data scientists to feed a solution data sets manually.
“A deep learning model, specifically developed for cybersecurity, can absorb and process vast volumes of raw data to fully train the system. Once trained, these neural networks become autonomous and do not require constant human intervention. This combination of a raw data–based learning methodology and larger data sets means that deep learning is eventually able to accurately identify much more complex patterns than machine learning, at much faster speeds,” the paper reads.
Furthermore, deep learning has the ability to predict the threat of adversarial AI by tricking the AI models and feeding them deceptive data. As such, it is far more challenging for threat actors to create malware that can understand and exploit how the system works.
Deep learning mimics the human brain’s functionality but with much greater speed and accuracy, and therefore can indicate intrusion by threat actors or malware much more effectively than any human employee and other cyber security methods.
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