National Security Agency

About: National Security Agency is a government organization based out in Fort George Meade, Maryland, United States. It is known for research contribution in the topics: Signal & Encryption. The organization has 393 authors who have published 485 publications receiving 15916 citations. The organization is also known as: NSA & N.S.A..

Method of recognizing phones in speech of any language

Abstract: Method of recognizing phones in speech of any language. Acquire phones for all languages and a set of languages. Acquire a pronunciation dictionary, a transcript of speech for the set of languages, and speech for the transcript. Receive speech containing unknown phones. If the speech's language is unknown, compare it to the phones for all languages to determine the phones. If the language is known but no phones were acquired in that language, compare the speech to the phones for all languages to determine the phones. If phones were acquired in the speech's language but no corresponding pronunciation dictionary was acquired, compare the speech to the phones for all languages to determine the phones. If a pronunciation dictionary was acquired for the phones in the speech's language but no transcript was acquired then compare the speech to the phones for all languages to determine the phones.

Method of image-based user authentication

Abstract: A method of authenticating a user by capturing an image of the user, assigning a user-name, selecting an image category, selecting a pattern, selecting locations on the user's face, assigning a rotation-value, logging onto a computer, presenting images, where some images are in the image category, selecting images that are the pattern away from the images in the image category, calculating a rotation angle, projecting the selected images that are rotated by the rotation angle onto the user's face at the locations, capturing an image of the user's face with the rotated images projected thereon, and authenticating the user if the user's facial features match the user's face and the projected images are the pattern away from the images in the image-category, are at the locations, are rotated by the rotation angle, and exhibit distortion consistent with the contour of the user's face.

Method of removing noise and interference from signal using peak picking

Abstract: Removing noise and interference from a signal by calculating a joint time-frequency domain of the signal, estimating instantaneous frequencies of the joint time-frequency domain, modifying each estimated instantaneous frequency, if necessary, to correspond to a frequency of the joint time-frequency domain to which it most closely compares, redistributing elements within the joint time-frequency domain according to the modified instantaneous frequencies, computing a magnitude for each element in the redistributed joint time-frequency domain, plotting the results, identifying peak values, eliminating from the redistributed joint time-frequency domain elements that do not correspond to the peak values, identifying noise and interference in the peak values, eliminating the noise and the interference from the redistributed joint time-frequency domain elements, and recovering a signal devoid of noise and interference from the modified redistributed joint time-frequency domain.

Gaps and Opportunities in Situational Awareness for Cybersecurity

Abstract: Demand is present among security practitioners for improving cyber situational awareness (SA), but capability and assessment have not risen to match. SA is an integral component of cybersecurity for everyone from individuals to business to response teams and threat exchanges. In this Field Note, we highlight existing research and our field observations, a recent review of cyber SA research literature, and call upon the research community to help address three research problems in situational awareness for cybersecurity. The gaps suggest the need to (1) understand what cyber SA is from the human operators’ perspectives, then (2) measure it so that (3) the community can learn whether SA makes a difference in meaningful ways to cybersecurity, and whether methods, technology, or other solutions would improve SA and thus, improve those outcomes.

Method of elliptic curve digital signature using coefficient splitting

Abstract: A method of generating and verifying a cryptographic digital signature using coefficient splitting. The digital signature is formed by first selecting a finite field, an elliptic curve of a first type or a second type, a point P, an integer w1, and an integer k1. Next, generating, via coefficient splitting, a point W=w1P and a point K=k1P. Next, transforming, K to a bit string K*. Next, combining K*, W, and a message M in a first manner to produce h1, and in a second manner to produce c. Next, generating s be either s=h1w1+ck1 (mod q), s=(h1w1+c)/k1 (mod q), or s=(h1k1+c)/w1 (mod q). Next, forming the cryptographic digital signature as (K*,s). The digital signature is verified by acquiring the finite field, the elliptic curve, the point P, the point W, the message M, and the cryptographic digital signature (K*,s). Next, computing h1 and c. Next, selecting (n0, n1) from (sc−1 (mod q), −h1c−1 (mod q)), (cs−1 (mod q), h1s−1 (mod q)) or (−ch1 −1 (mod q), sh1 −1 (mod q)). Next, generating the point n0P via coefficient splitting. Next, generating the point n1W via coefficient splitting. Next, summing the points computed in the last two steps and designating the sum Q. Next, transforming Q to Q*. Lastly, verifying the digital signature (K*,s) if Q*=K*. Otherwise rejecting the cryptographic digital signature (K*,s) as unverified.