Search NASA⌕ Search

DOE OSTI · 1159917

Intrusion detection using secure signatures

Abstract

A method and device for intrusion detection using secure signatures comprising capturing network data. A search hash value, value employing at least one one-way function, is generated from the captured network data using a first hash function. The presence of a search hash value match in a secure signature table comprising search hash values and an encrypted rule is determined. After determining a search hash value match, a decryption key is generated from the captured network data using a second hash function, a hash function different form the first hash function. One or more of the encrypted rules of the secure signatures table having a hash value equal to the generated search hash value are then decrypted using the generated decryption key. The one or more decrypted secure signature rules are then processed for a match and one or more user notifications are deployed if a match is identified.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nelson, Trent Darnel, Haile, Jedediah. 2014-09-30. Intrusion detection using secure signatures. https://www.osti.gov/biblio/1159917

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗