Implications of Artificial intelligence for cybersecurity proceedings of a workshop

Cybersecurity Implications of Artificial Intelligence The final portion of Kambhampati’s talk focused on the implications of AI for cybersecurity, in terms of both opportunities and risks. Noting that this topic wou ...

Cybersecurity Implications of Artificial Intelligence The final portion of Kambhampati’s talk focused on the implications of AI for cybersecurity, in terms of both opportunities and risks. Noting that this topic would be the focus of subsequent workshop sessions, he provided a brief introduction to ways in which AI techniques can help to improve cybersecurity, along with their potential to open up new attack surfaces for adversaries. 8 S. Kambhampati, 2018, “Challenges of Human-Aware AI Systems,” presidential address at the 32nd AAAI Conference on Artificial Intelligence, http://rakaposhi.eas.asu.edu/haai-aaai/AAAI-Presidential-Address-final.pdf, YouTube link: http://bit.ly/2tHyzAh. Kambhampati noted that AI, like any tool or technology, could be useful for many applications, including for cybersecurity. He highlighted the following three key areas to consider: • Game-theoretic models (e.g., to understand dynamic security paradigms such as a moving target defense), • Planning and reasoning methods (e.g., to help uncover previously unidentified attack paths) • Machine learning (e.g., to identify malware or for intrusion detection) Kambhampati suggested that, while there may be a predisposition to focus entirely on the affordances of ML for cybersecurity, AI is broader than ML alone, and one can envision AI’s contributions in terms of combining multiple approaches. He noted that significant research has been conducted at the intersection of cybersecurity and all of the above areas. Kambhampati’s research group at Arizona State University has done work at the intersection of AI and cybersecurity. For example, they have worked on the so-called controlled observability planning problem, where an actor controls what the observer sees to effect a desired outcome: Actions are apparent to friends working in a collaborative environment but obfuscated for enemies in an adversarial context. 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. One major area of concern is the use of AI to generate fake and potentially deceptive content, Kambhampati said. In particular, perceptual AI can be used to spoof voices, images, and identities, which could be used to deceive people and cause them to question what is real, or to accept a fabricated version of reality. For example, voice spoofing could be used to deceive someone into thinking that a caller is his mother asking for money, or to commandeer a voice-activated device. Image spoofing can be used to make an autonomous vehicle mistake a stop sign for a speed limit sign. Such capabilities are apparent through a few specific examples, including an AI-generated news anchor used in Chinese news broadcasts. The website www.whichfaceisreal.com challenges visitors to discern which of two images depicts a real person and which is an AI-generated face; it is often hard for a human to tell the difference (although in many cases computers are still able to identify the “fake,” due to artifacts of the image generation method). As language algorithms improve, the same challenges are seen in the context of written text. How can one discern between real and computer-generated writing, for example, in the context of political discourse, education, or social media? One new tool uses an algorithm to detect the signature patterns of machine-generated language based on the fact that AI writes essays by learning massive language models, where a current word is used to generate the next word. Kambhampati referred to a 2016 workshop at Arizona State University10 and the recent report on “Malicious Use of AI”11 for further discussion on these and other potential challenges related to AI.