Implications of Artificial Intelligence for Cybersecurity Proceedings of a Workshop (2019) / Chapter Skim
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1 Introduction and Context
1 Introduction and Context
Pages 1-8
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Chapter 7 summarizes a wrap-up discussion in which workshop participants reflected on key questions posed to the group and identified key takeaways from the workshop. 1 Rapporteur's note: Artificial intelligence is the general term for the field aimed at developing computing technologies that exhibit what humans would consider to be intelligent behavior, or for the technologies themselves.
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OPENING REMARKS Workshop chair Fred Chang, Southern Methodist University, and representative of the sponsoring organization, Vinh Nguyen, National Security Agency (NSA) , offered opening remarks to outline the workshop's impetus and goals.
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Cyber Grand Challenge demonstrated the possibility of automating cybersecurity tasks that previously required human intelligence. This most recent shift defines the landscape at which we are arriving: the realm of artificial intelligence and its implications.
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However, this has changed in recent years with new opportunities in machine learning -- making computers learn from observation of examples and experience that can be applied in new contexts, analogously to how humans learn through observation in early phases of development. Current Trends in Artificial Intelligence: Neural Networks and Deep Learning Interest in AI has exploded in recent years, largely because perceptual AI capabilities such as image and speech recognition have brought AI into tools we use in our everyday lives.
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In particular, Kambhampati noted, while some may believe that the recent progress in perceptual intelligence reflects a major theoretical advance in AI, this is not the case: much of this progress is due to the recent, wide deployment of neural networks -- an ML framework first developed in the 1960s. This approach has come to the fore today due to the separate, non-AI-related development of large-scale computation and communication and data capture infrastructure; only recently have data become abundantly available -- and computers powerful enough -- to deploy neural networks for practical applications.
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Kambhampati responded by noting that backwaters by definition are not widely recognized, which makes it hard to know which ones might rise to prominence in future years. He pointed out that 65 to 70 percent of current papers in the AI field address perceptual intelligence using deep learning.
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They have also done some work on the "moving target defense" for Web applications, in which game-theoretic approaches inform constant configuration changes to reduce an attacker's ability to bring down a system. Not surprisingly, this approach outperforms those using random configuration changes.9 While Kambhampati believes that AI systems will in general have many profoundly positive impacts on society, their widespread use will also open up new attack surfaces.
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8 IMPLICATIONS OF ARTIFICIAL INTELLIGENCE FOR CYBERSECURITY In summary, Kambhampati noted that, after a history of making progress mostly in carrying out declarative tasks, AI systems have shown significant success in learning tacit knowledge from data -- a development that has had notable ramifications for the field. However, he pointed out that AI systems are far from achieving general intelligence, having yet to cross the thresholds of demonstrating common sense and becoming human-aware.
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