8,040 research outputs found
Nature-Inspired Interconnects for Self-Assembled Large-Scale Network-on-Chip Designs
Future nano-scale electronics built up from an Avogadro number of components
needs efficient, highly scalable, and robust means of communication in order to
be competitive with traditional silicon approaches. In recent years, the
Networks-on-Chip (NoC) paradigm emerged as a promising solution to interconnect
challenges in silicon-based electronics. Current NoC architectures are either
highly regular or fully customized, both of which represent implausible
assumptions for emerging bottom-up self-assembled molecular electronics that
are generally assumed to have a high degree of irregularity and imperfection.
Here, we pragmatically and experimentally investigate important design
trade-offs and properties of an irregular, abstract, yet physically plausible
3D small-world interconnect fabric that is inspired by modern network-on-chip
paradigms. We vary the framework's key parameters, such as the connectivity,
the number of switch nodes, the distribution of long- versus short-range
connections, and measure the network's relevant communication characteristics.
We further explore the robustness against link failures and the ability and
efficiency to solve a simple toy problem, the synchronization task. The results
confirm that (1) computation in irregular assemblies is a promising and
disruptive computing paradigm for self-assembled nano-scale electronics and (2)
that 3D small-world interconnect fabrics with a power-law decaying distribution
of shortcut lengths are physically plausible and have major advantages over
local 2D and 3D regular topologies
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
A forensics investigation after a breach often uncovers network and host
indicators of compromise (IOCs) that can be deployed to sensors to allow early
detection of the adversary in the future. Over time, the adversary will change
tactics, techniques, and procedures (TTPs), which will also change the data
generated. If the IOCs are not kept up-to-date with the adversary's new TTPs,
the adversary will no longer be detected once all of the IOCs become invalid.
Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular
expressions (regexes), up-to-date with a dynamic adversary. Our framework
solves the TTK problem in an automated, cyclic fashion to bracket a previously
discovered adversary. This tracking is accomplished through a data-driven
approach of self-adapting a given model based on its own detection
capabilities.
In our initial experiments, we found that the true positive rate (TPR) of the
adaptive solution degrades much less significantly over time than the naive
solution, suggesting that self-updating the model allows the continued
detection of positives (i.e., adversaries). The cost for this performance is in
the false positive rate (FPR), which increases over time for the adaptive
solution, but remains constant for the naive solution. However, the difference
in overall detection performance, as measured by the area under the curve
(AUC), between the two methods is negligible. This result suggests that
self-updating the model over time should be done in practice to continue to
detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science &
Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas,
Nevada, US
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
A forensics investigation after a breach often uncovers network and host
indicators of compromise (IOCs) that can be deployed to sensors to allow early
detection of the adversary in the future. Over time, the adversary will change
tactics, techniques, and procedures (TTPs), which will also change the data
generated. If the IOCs are not kept up-to-date with the adversary's new TTPs,
the adversary will no longer be detected once all of the IOCs become invalid.
Tracking the Known (TTK) is the problem of keeping IOCs, in this case regular
expressions (regexes), up-to-date with a dynamic adversary. Our framework
solves the TTK problem in an automated, cyclic fashion to bracket a previously
discovered adversary. This tracking is accomplished through a data-driven
approach of self-adapting a given model based on its own detection
capabilities.
In our initial experiments, we found that the true positive rate (TPR) of the
adaptive solution degrades much less significantly over time than the naive
solution, suggesting that self-updating the model allows the continued
detection of positives (i.e., adversaries). The cost for this performance is in
the false positive rate (FPR), which increases over time for the adaptive
solution, but remains constant for the naive solution. However, the difference
in overall detection performance, as measured by the area under the curve
(AUC), between the two methods is negligible. This result suggests that
self-updating the model over time should be done in practice to continue to
detect known, evolving adversaries.Comment: This was presented at the 4th Annual Conf. on Computational Science &
Computational Intelligence (CSCI'17) held Dec 14-16, 2017 in Las Vegas,
Nevada, US
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