The article also highlights the importance of proactive rather than reactive cybersecurity, which involves recognizing and removing vulnerabilities before they can be exploited. While DL can significantly improve cybersecurity, the rapid evolution of AI technologies like DL could potentially be exploited by malicious attackers, necessitating protective legislation. The goal is to move beyond reactive detection to proactive protection, with automation and multilayered deep learning playing crucial roles.
Key takeaways:
- Deep learning (DL), an advanced subset of machine learning (ML), is behind some of the most innovative technologies today and can increase in accuracy without human intervention.
- Artificial neural networks (ANNs) are crucial in the fight against cybercrime, as they can recognize and predict suspicious behavior and understand what a potential attack looks like in order to prevent any payload execution or data encryption.
- While DL and ANNs are significantly heightening security defense postures, they are evolving faster than regulatory bodies can contain and control their capabilities, which could potentially be utilized and manipulated by malicious attackers.
- The goal of cybersecurity is to move beyond reactive detection and response to proactive protection and threat elimination, with automation and multilayered deep learning being crucial steps in that direction.