Anup K Ghosh

One of the hardest challenges for companies and their officers is determining how much to spend on cybersecurity and the appropriate allocation of those resources. Security “investments” are a cost on the ledger, and as such, companies do not want to spend more on security than they have to. The question most boards have is “how much security is enough?” and “how good is our security program?” Most CISOs and SOC teams have a hard time answering these questions for a lack of data and framework to measure risk and compare with other similar sized companies. This paper presents a data-driven practical approach to assessing and scoring cybersecurity risk that can be used to allocate resources efficiently a nd mitigate cybersecurity risk in areas that need it the most. We combine both static and dynamic measures of risk to give a composite score more indicative of cybersecurity risk over static measures alone.

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Tian Dong (Shanghai Jiao Tong University), Shaofeng Li (Shanghai Jiao Tong University), Guoxing Chen (Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Haojin Zhu (Shanghai Jiao Tong University), Zhen Liu (Shanghai Jiao Tong University)

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Automata-Based Automated Detection of State Machine Bugs in Protocol...

Paul Fiterau-Brostean (Uppsala University, Sweden), Bengt Jonsson (Uppsala University, Sweden), Konstantinos Sagonas (Uppsala University, Sweden and National Technical University of Athens, Greece), Fredrik Tåquist (Uppsala University, Sweden)

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PISE: Protocol Inference using Symbolic Execution and Automata Learning

Ron Marcovich, Orna Grumberg, Gabi Nakibly (Technion, Israel Institute of Technology)

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Understanding the Ethical Frameworks of Internet Measurement Studies

Eric Pauley and Patrick McDaniel (University of Wisconsin–Madison)

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