11,925 research outputs found
The Challenges in SDN/ML Based Network Security : A Survey
Machine Learning is gaining popularity in the network security domain as many
more network-enabled devices get connected, as malicious activities become
stealthier, and as new technologies like Software Defined Networking (SDN)
emerge. Sitting at the application layer and communicating with the control
layer, machine learning based SDN security models exercise a huge influence on
the routing/switching of the entire SDN. Compromising the models is
consequently a very desirable goal. Previous surveys have been done on either
adversarial machine learning or the general vulnerabilities of SDNs but not
both. Through examination of the latest ML-based SDN security applications and
a good look at ML/SDN specific vulnerabilities accompanied by common attack
methods on ML, this paper serves as a unique survey, making a case for more
secure development processes of ML-based SDN security applications.Comment: 8 pages. arXiv admin note: substantial text overlap with
arXiv:1705.0056
Analytical developments for definition and prediction of USB noise
A systematic acoustic data base and associated flow data are used in identifying the noise generating mechanisms of upper surface blown flap configurations of short takeoff and landing aircraft. Theory is developed for the radiated sound field of the highly sheared flow of the trailing edge wake. An empirical method is also developed using extensive experimental data and physical reasonings to predict the noise levels
Outward Influence and Cascade Size Estimation in Billion-scale Networks
Estimating cascade size and nodes' influence is a fundamental task in social,
technological, and biological networks. Yet this task is extremely challenging
due to the sheer size and the structural heterogeneity of networks. We
investigate a new influence measure, termed outward influence (OI), defined as
the (expected) number of nodes that a subset of nodes will activate,
excluding the nodes in S. Thus, OI equals, the de facto standard measure,
influence spread of S minus |S|. OI is not only more informative for nodes with
small influence, but also, critical in designing new effective sampling and
statistical estimation methods.
Based on OI, we propose SIEA/SOIEA, novel methods to estimate influence
spread/outward influence at scale and with rigorous theoretical guarantees. The
proposed methods are built on two novel components 1) IICP an important
sampling method for outward influence, and 2) RSA, a robust mean estimation
method that minimize the number of samples through analyzing variance and range
of random variables. Compared to the state-of-the art for influence estimation,
SIEA is times faster in theory and up to several orders of
magnitude faster in practice. For the first time, influence of nodes in the
networks of billions of edges can be estimated with high accuracy within a few
minutes. Our comprehensive experiments on real-world networks also give
evidence against the popular practice of using a fixed number, e.g. 10K or 20K,
of samples to compute the "ground truth" for influence spread.Comment: 16 pages, SIGMETRICS 201
Alien Registration- Orser, Tam N. (Sanford, York County)
https://digitalmaine.com/alien_docs/3908/thumbnail.jp
Cybonto: Towards Human Cognitive Digital Twins for Cybersecurity
Cyber defense is reactive and slow. On average, the time-to-remedy is
hundreds of times larger than the time-to-compromise. In response to the
expanding ever-more-complex threat landscape, Digital Twins (DTs) and
particularly Human Digital Twins (HDTs) offer the capability of running massive
simulations across multiple knowledge domains. Simulated results may offer
insights into adversaries' behaviors and tactics, resulting in better proactive
cyber-defense strategies. For the first time, this paper solidifies the vision
of DTs and HDTs for cybersecurity via the Cybonto conceptual framework
proposal. The paper also contributes the Cybonto ontology, formally documenting
108 constructs and thousands of cognitive-related paths based on 20 time-tested
psychology theories. Finally, the paper applied 20 network centrality
algorithms in analyzing the 108 constructs. The identified top 10 constructs
call for extensions of current digital cognitive architectures in preparation
for the DT future.Comment: 6 pages, 3 figures, 1 tabl
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