Could artificial intelligence make life harder for hackers?

phys.org | 2/1/2019 | Staff
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As the volume of digital information in corporate networks continues to grow, so grows the number of cyberattacks, and their cost. One cybersecurity vendor, Juniper Networks, estimates that the cost of data breaches worldwide will reach $2.1 trillion in 2019, roughly four times the cost of breaches in 2015.

Now, two Boston University computer scientists, working with researchers at Draper, a not-for-profit engineering solutions company located in Cambridge, have developed a tool that could make it harder for hackers to find their way into networks where they don't belong.

Peter - Chin - Research - Professor - Computer

Peter Chin, a research professor of computer science and an affiliate of the Rafik B. Hariri Institute for Computing and Computational Science & Engineering, and Jacob Harer, a fourth-year Ph.D. student in computer science, worked with Draper researchers to develop technology that can scan software systems for the kinds of vulnerabilities that are often used by cybercriminals to gain entry. The tool, which used deep learning to train neural networks to identify patterns that indicate software flaws, can scan millions of lines of code in seconds, and will someday have the ability to fix the coding errors that it spots.

Chin says the idea for the project, called DeepCode and funded by the DARPA (Defense Advanced Research Projects Agency) MUSE program and the Air Force Research Laboratory, came to him four years ago while he was delivering a lecture to his machine learning class (CS 542). Chin was describing the breakthrough achievement of scientists at Google and Stanford University, who used deep learning to teach a neural network to spot common patterns in millions of images and use the patterns to identify cats in YouTube videos. He wondered if a similar network could mine the big data of open-source programs and find patterns that indicate software vulnerabilities.

Chin

Chin knew that it was possible to represent...
(Excerpt) Read more at: phys.org
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