The document was issued by academy or academy organization.
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ABSTRACT: Humans have goals, hopes, dreams, and fears. Humans are brilliant. They make incredible intuitive inferences. They conceptualize amazing algorithms to augment cybersecurity. But they can be misled; tricked; foole d into carrying out actions counter to their own best-interests.
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ABSTRACT
Recent research suggests that modern machine learning methods are fragile and easily attacked, which raises concerns about their use in security-critical settings. I will survey several attacks on machine learning and directions for making machine learning more robust against attack. I will also briefly mention my own research in this area.
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Vulnerabilities that compromise memory safety are at the heart of many attacks. Spatial safety, one aspect of memory safety, is ensured when any pointer dereference is always within the memory allocated to that pointer. Buffer overruns violate spatial safety, and still constitute a com-mon cause of vulnerability. During 2012-2018, buffer overruns were the source of 9.7% to 18.4% of CVEs re-ported in the NIST vulnerability database, constituting the leading single cause of CVEs.
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Advanced metering infrastructure (AMI) is a critical part of a modern smart grid that performs the bidirectional data flow of sensitive power information such as smart metering data and control commands. The real-time monitoring and control of the grid are ensured through AMI. While smart meter data helps to improve the overall performance of the grid in terms of efficient energy management, it has also made the AMI an attractive target of cyberattackers with a goal of stealing energy.
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State estimation is very important for securely, reliably, and efficiently maintaining a power grid. If state estimation is not protected, an attacker can compromise meters or communication systems and introduce false measurements, which can evade existing Bad Data Detection (BDD) algorithms and lead to incorrect state estimation. This kind of attack is stealthy and widely known as an Undetected False Data Injection (UFDI) attack.
