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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CLExtract: Recovering Highly Corrupted DVB/GSE Satellite Stream with Contrastive...

Minghao Lin (University of Colorado Boulder), Minghao Cheng (Independent Researcher), Dongsheng Luo (Florida International University), Yueqi Chen (University of Colorado Boulder) Presenter: Minghao Lin

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Anomaly Detection in the Open World: Normality Shift Detection,...

Dongqi Han (Tsinghua University), Zhiliang Wang (Tsinghua University), Wenqi Chen (Tsinghua University), Kai Wang (Tsinghua University), Rui Yu (Tsinghua University), Su Wang (Tsinghua University), Han Zhang (Tsinghua University), Zhihua Wang (State Grid Shanghai Municipal Electric Power Company), Minghui Jin (State Grid Shanghai Municipal Electric Power Company), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia…

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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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