Sunday, July 30, 2017

A Public Key Encryption Algorithm for Network Security

Enhanced Public Key Encryption Algorithm for Security of Network

Abstract -- Network security has become more important to personal computer users, organizations, and the military. With the advent of the internet,
security became a major concern and the history of security allows a better understanding of the emergence of security technology. The internet
structure itself allowed for many security threats to occur. When the architecture of the internet is modified it can reduce the possible attacks that can be
sent across the network. Knowing the attack methods, allows for the appropriate security to emerge. By means of firewalls and encryption mechanisms
many businesses secure themselves from the internet. The businesses create an "intranet" to remain connected to the internet but secured from
possible threats. Data integrity is quite a issue in security and to maintain that integrity we tends to improve as to provides the better encryption
processes for security. In our proposed work we will make encryption harder with enhanced public key encryption protocol for security and will discuss
the applications for proposed work. We will enhance the hardness in security by improving the Diffie-Hellman encryption algorithm by making changes or
adding some more security codes in current algorithm.

REFERENCES
[1] Farhat, Farshid, Somayeh Salimi, and Ahmad Salahi. "Private
Identification, Authentication and Key Agreement Protocol with
Security Mode Setup." IACR Cryptology ePrint Archive 2011.
[2] Emmanuel Bresson, Olivier Chevassut, David
Pointcheva, Jean-Jacques Quisquater, "Authenticated
Group Diffie-Hellman Key Exchange", Computer and
Communication Security- proc of ACM CSS'01,
Philadelphia, Pennsylvania, USA, Pages 255-264, ACM Press,
November 5-8, 2001.
[3] Mario Cagaljm, Srdjan Capkun and Jean-Pierre
Hubaux," Key agreement in peer-to-peer wireless
networks", Ecole Polytechnique F´ed´erale de Lausanne
(EPFL), CH-1015 Lausanne.
[4] Michel Abdalla, Mihir Bellare, Phillip Rogaway,"
DHIES: An encryption scheme based on the Diffie-Hellman
Problem", September 18, 2001.
[5] Jean-Fran¸cois Raymond, Anton Stiglic," Security Issues
in the Diffie-Hellman Key Agreement Protocol".
[6] Whitfield Diffie and Martin E. Hellman," New Directions
in Cryptography", invited paper.
[7] F. Lynn Mcnulty," Encryption's importance to
economic and infrastructure security" in 2002.
[8] Tony Chung and Utz Roedig," Poster Abstract: DHBKEY -A Diffie-Hellman Key Distribution Protocol for
Wireless Sensor Networks", Infolab21, Lancaster University,
UK.
[9] A. Chandrasekar, V.R. Rajasekar, V. Vasudevan,"
Improved Authentication and Key Agreement Protocol
Using Elliptic Curve Cryptography" in 2006.
[10] SANS Institute Info Sec Reading Room," A Review of
the Diffie-Hellman Algorithm and its use in Secure
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*11+ Paul C. Kocher, "Timing Attacks on Implementations of
Diffie-Hellman, RSA, DSS, and Other Systems", Cryptography
Research, Inc. 607 Market Street, 5th Floor, San Francisco, CA
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[12] Brita VesterĂ¥s," Analysis of Key Agreement Protocols",
Mtech Thesis, Department of Computer Science and Media
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xchange.

Saturday, July 15, 2017

Mirzakhani, Maryam

Maryam Mirzakhani was first women to win Fields Medal in math also professor at Stanford university.

in Farshid Farhat 's Twitter





Monday, July 10, 2017

Deep Learning at Pennsylvania State University

Integrating Deep-learned Models and Photography Idea Retrieval

ABSTRACT: Retrieving photography ideas corresponding to a given location facilitates the usage of smart cameras, where there is a high interest among amateurs and enthusiasts to take astonishing photos at anytime and in any location. Existing research captures some aesthetic techniques such as the rule of thirds, triangle, and perspectiveness, and retrieves useful feedbacks based on one technique. However, they are restricted to a particular technique and the retrieved results have room to improve as they can be limited to the quality of the query. There is a lack of a holistic framework to capture important aspects of a given scene and give a novice photographer informative feedback to take a better shot in his/her photography adventure. This work proposes an intelligent framework of portrait composition using our deep-learned models and image retrieval methods. A highly-rated web-crawled portrait dataset is exploited for retrieval purposes. Our framework detects and extracts ingredients of a given scene representing as a correlated hierarchical model. It then matches extracted semantics with the dataset of aesthetically composed photos to investigate a ranked list of photography ideas, and gradually optimizes the human pose and other artistic aspects of the composed scene supposed to be captured. The conducted user study demonstrates that our approach is more helpful than the other constructed feedback retrieval systems.