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- Abbas, Haider, et al.
(author)
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Addressing Dynamic Issues in Information Security Management
- 2011
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In: Information Management & Computer Security. - UK : Emerald Group Publishing Limited. - 0968-5227 .- 1758-5805. ; 19:1, s. 5-24
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Journal article (peer-reviewed)abstract
- Ett ramverk för behandling av osäkerhet inom ledningssystem för informationssäkerhet presenteras. Ramverket baseras på teorier från corporate finance. En fallstudie visar hur ramverket kan appliceras.
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- Abbas, Haider, et al.
(author)
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Architectural Description of an Automated System for Uncertainty Issues Management in Information Security
- 2010
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In: International Journal of computer Science and Information Security. - USA. - 1947-5500. ; 8:3, s. 59-67
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Journal article (peer-reviewed)abstract
- Information technology evolves at a faster pace giving organizations a limited scope to comprehend and effectively react to steady flux nature of its progress. Consequently the rapid technological progression raises various concerns for the IT system of an organization i.e. existing hardware/software obsoleteness, uncertain system behavior, interoperability of various components/method, sudden changes in IT security requirements and expiration of security evaluations. These issues are continuous and critical in their nature that create uncertainty in IT infrastructure and threaten the IT security measures of an organization. In this research, Options theory is devised to address uncertainty issues in IT security management and the concepts have been developed/validated through real cases on SHS (Spridnings-och-Hämtningssystem) and ESAM (E-society) systems. AUMSIS (Automated Uncertainty Management System in Information Security) is the ultimate objective of this research which provides an automated system for uncertainty management in information security. The paper presents the architectural description of AUMSIS, its various components, information flow, storage and information processing details using options valuation techniques. It also presents heterogeneous information retrieval problems and their solution. The architecture is validated with examples from SHS system
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- Lansner, Anders, et al.
(author)
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Spiking Brain Models : Computation, Memory and Communication Constraints for Custom Hardware Implementation
- 2014
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In: 2014 19th Asia and South Pacific Design Automation Conference (ASP DAC). - : IEEE Computer Society. - 9781479928163 ; , s. 556-562
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Conference paper (peer-reviewed)abstract
- We estimate the computational capacity required to simulate in real time the neural information processing in the human brain. We show that the computational demands of a detailed implementation are beyond reach of current technology, but that some biologically plausible reductions of problem complexity can give performance gains between two and six orders of magnitude, which put implementations within reach of tomorrow's technology.
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- Stathis, Dimitrios, et al.
(author)
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eBrainII : a 3 kW Realtime Custom 3D DRAM Integrated ASIC Implementation of a Biologically Plausible Model of a Human Scale Cortex
- 2020
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In: Journal of Signal Processing Systems. - : Springer. - 1939-8018 .- 1939-8115. ; 92:11, s. 1323-1343
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Journal article (peer-reviewed)abstract
- The Artificial Neural Networks (ANNs), like CNN/DNN and LSTM, are not biologically plausible. Despite their initial success, they cannot attain the cognitive capabilities enabled by the dynamic hierarchical associative memory systems of biological brains. The biologically plausible spiking brain models, e.g., cortex, basal ganglia, and amygdala, have a greater potential to achieve biological brain like cognitive capabilities. Bayesian Confidence Propagation Neural Network (BCPNN) is a biologically plausible spiking model of the cortex. A human-scale model of BCPNN in real-time requires 162 TFlop/s, 50 TBs of synaptic weight storage to be accessed with a bandwidth of 200 TBs. The spiking bandwidth is relatively modest at 250 GBs/s. A hand-optimized implementation of rodent scale BCPNN has been done on Tesla K80 GPUs require 3 kWs, we extrapolate from that a human scale network will require 3 MWs. These power numbers rule out such implementations for field deployment as cognition engines in embedded systems. The key innovation that this paper reports is that it is feasible and affordable to implement real-time BCPNN as a custom tiled application-specific integrated circuit (ASIC) in 28 nm technology with custom 3D DRAM - eBrainII - that consumes 3 kW for human scale and 12 watts for rodent scale. Such implementations eminently fulfill the demands for field deployment.
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