A New Approach to the Development of Additive Fibonacci Generators Based on Prime Numbers

Pseudorandom number and bit sequence generators are widely used in cybersecurity, measurement, and other technology fields. A special place among such generators is occupied by additive Fibonacci generators (AFG). By itself, such a generator is not cryptographically strong. Nevertheless, when used as a primary it can be quite resistant to cryptanalysis generators. This paper proposes a modification to AGF, the essence of which is to use prime numbers as modules of recurrent equations describing the operation of generators. This modification made it possible to ensure the constancy of the repetition period of the output pseudorandom pulse sequence in the entire range of possible values of the initial settings–keys (seed) at specific values of the module. In addition, it has proposed a new generator scheme, which consists of two generators: the first of which is based on a modified AFG and the second is based on a linear feedback shift register (LFSR). The output pulses of both generators are combined through a logic element XOR. The results of the experiment show that the specific values of modules provide a constant repetition period of the output pseudorandom pulse sequence in a whole range of possible values of the initial settings–keys (seed) and provide all the requirements of the NIST test to statistical characteristics of the sequence. Modified AFGs are designed primarily for hardware implementation, which allows them to provide high performance.

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A high performance, hybrid wave-pipelined linear feedback shift register with skew tolerant clocks

2003 46th Midwest Symposium on Circuits and Systems ◽

10.1109/mwscas.2003.1562553 ◽

2006 ◽

Author(s):

Jabulani Nyathi ◽

J.G. Delgado-Frias ◽

J. Lowe

Keyword(s):

High Performance ◽

Shift Register ◽

Linear Feedback Shift Register ◽

Linear Feedback ◽

Hybrid Wave ◽

Feedback Shift Register

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Generalization of the Self-Shrinking Generator in the Galois Field

Advances in Artificial Intelligence ◽

10.1155/2011/464971 ◽

2011 ◽

Vol 2011 ◽

pp. 1-10 ◽

Cited By ~ 7

Author(s):

Antoniya Todorova Tasheva ◽

Zhaneta Nikolova Tasheva ◽

Aleksandar Petrov Milev

Keyword(s):

Hardware Implementation ◽

Stream Cipher ◽

Low Cost ◽

Binary Sequence ◽

Galois Field ◽

The Self ◽

Security Requirements ◽

Linear Feedback ◽

Large Period ◽

Feedback Shift Register

The proposed by Meier and Staffelbach Self-Shrinking Generator (SSG) which has efficient hardware implementation only with a single Linear Feedback Shift Register is suitable for low-cost and fast stream cipher applications. In this paper we generalize the idea of the SSG for arbitrary Galois Field . The proposed variant of the SSG is called the -ary Generalized Self-Shrinking Generator (pGSSG). We suggest a method for transformation of a non-binary self-shrunken pGSSG sequence into balanced binary sequence. We prove that the keystreams of the pGSSG have large period and good statistical properties. The analysis of the experimental results shows that the pGSSG sequences have good randomness properties. We examine the complexity of exhaustive search and entropy attacks of the pGSSG. We show that the pGSSG is more secure than SSG and Modified SSG against these attacks. We prove that the complexity of the used pGSSG attacks increases with increasing the prime . Previously mentioned properties give the reason to say that the pGSSG satisfy the basic security requirements for a stream chipper and can be useful as a part of modern stream ciphers.

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A New Approach in Grid Computing using Procedure distribution for high performance computing

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i5.8691 ◽

2019 ◽

Vol 7 (5) ◽

pp. 86-91

Author(s):

Maitry Joshi ◽

Ankit Shah

Keyword(s):

Grid Computing ◽

High Performance Computing ◽

High Performance ◽

New Approach ◽

Performance Computing

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Theoretical Interpretation of High-Performance Chromatographic Data for a Heterogeneous Series of Compounds

Collection of Czechoslovak Chemical Communications ◽

10.1135/cccc19920033 ◽

1992 ◽

Vol 57 (1) ◽

pp. 33-45

Author(s):

Vladimír Jakuš

Keyword(s):

Experimental Data ◽

High Performance ◽

Theoretical Interpretation ◽

Retention Data ◽

Theoretical Evaluation ◽

New Approach ◽

Free Energy Of Solvation ◽

Rp Hplc ◽

Mobile Phases ◽

Reversed Phases

A new approach to theoretical evaluation of the Gibbs free energy of solvation was applied for estimation of retention data in high-performance liquid chromatography on reversed phases (RP-HPLC). Simple and improved models of stationary and mobile phases in RP-HPLC were employed. Statistically significant correlations between the calculated and experimental data were obtained for a heterogeneous series of twelve compounds.

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An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽

10.3390/s21020656 ◽

2021 ◽

Vol 21 (2) ◽

pp. 656

Author(s):

Xavier Larriva-Novo ◽

Víctor A. Villagrá ◽

Mario Vega-Barbas ◽

Diego Rivera ◽

Mario Sanz Rodrigo

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

High Performance ◽

Learning Algorithm ◽

Detection System ◽

Machine Learning Algorithms ◽

Statistical Characteristics ◽

Detection Techniques ◽

Traffic Characteristics ◽

Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

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