scholarly journals Using a probabilistic neural network to assess the state of information security

2021 ◽  
Vol 2032 (1) ◽  
pp. 012051
Author(s):  
M E Sukhoparov ◽  
I A Sikarev ◽  
T M Tatarnikova ◽  
I S Lebedev ◽  
A A Butsanets
Keyword(s):  
Neural Network ◽  
Information Security ◽  
Probabilistic Neural Network ◽  
The State

scholarly journals An approach to identifying threats of extracting confidential data from automated control systems based on internet technologies

2021 ◽  
Vol 15 (3) ◽  
pp. 35-47
Author(s):  
Vladimir Kuzmin ◽  
Artem Menisov
Keyword(s):  
Neural Network ◽  
Information Security ◽  
Control Systems ◽  
Short Term Memory ◽  
Methodological Approach ◽  
The State ◽  
External Information ◽  
Automated Control ◽  
Universal Sentence ◽  
Confidential Data

Together with ubiquitous, global digitalization, cybercrime is growing and developing rapidly. The state considers the creation of an environment conducive to information security to be a strategic goal for the development of the information society in Russia. However, the question of how the “state of protection of the individual, society and the state from internal and external information threats” should be achieved in accordance with the “Information Security” and the “Digital Economy of Russia 2024” programs remains open. The aim of this study is to increase the efficiency whereby automated control systems identify confidential data from html-pages to reduce the risk of using this data in the preparatory and initial stages of attacks on the infrastructure of government organizations. The article describes an approach that has been developed to identify confidential data based on the combination of several neural network technologies: a universal sentence encoder and a neural network recurrent architecture of bidirectional long-term short-term memory. The results of an assessment in comparison with modern means of natural language text processing (SpaCy) showed the merits and prospects of the practical application of the methodological approach.

ALGORITHM FOR DETERMINING THE DEGREE VALUES OF CONFIDENTIAL DOCUMENTS OF THE ORGANIZATION

2017 ◽  
pp. 80-88
Author(s):  
Sergey Valerevich Belov ◽  
Irina Mikhalovna Kosmacheva ◽  
Irina Vyacheslavovna Sibikina
Keyword(s):  
Information Security ◽  
Russian Federation ◽  
Value Of Information ◽  
Weighting Factor ◽  
State Regulation ◽  
The State ◽  
Information Security Management ◽  
Pair Comparison ◽  
The Russian Federation ◽  
Properties Of Information

To solve the problem of information security management the method was proposed that allows determining the degree of importance of confidential documents of the organization. The urgency of the proposed algorithm was substantiated taking into account the requirements of the legislation of the Russian Federation in the sphere of information security. The stages prior to the formation of the list of confidential documents of the organization were described. A review of the main documents of the legal and regulatory framework was carried out including documents relating to the state regulation of relations in the sphere of information security. The classes of protected information for the accessing categories were considered. The criteria changes of the value of information in the process of time were represented. The algorithm of formation of the list of confidential documents of the organization based on the properties of information was offered. The algorithm is based on an expert method of pair comparison of alternatives. The result of the use of this method is a number of confidential documents, ranked in descending order of importance. For each document the weighting factor of importance can be calculated. The verification stage of the degree of expert consistency was included in the methodology to eliminate the use of erroneous expert data. The application of the methodology is illustrated by a calculated example.

Classification and Identification of Power System Disturbances Using Wavalet and Artificial Neural Network Technique

2005 ◽  
Vol 2 (2) ◽  
pp. 25
Author(s):  
Noraliza Hamzah ◽  
Wan Nor Ainin Wan Abdullah ◽  
Pauziah Mohd Arsad
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Probabilistic Neural Network ◽  
Microsoft Excel ◽  
Waveform Data ◽  
The Neural Network ◽  
Harmonic Voltage ◽  
Power Quality Disturbances ◽  
Electronic Load ◽  
Wavelet Technique

Power Quality disturbances problems have gained widespread interest worldwide due to the proliferation of power electronic load such as adjustable speed drives, computer, industrial drives, communication and medical equipments. This paper presents a technique based on wavelet and probabilistic neural network to detect and classify power quality disturbances, which are harmonic, voltage sag, swell and oscillatory transient. The power quality disturbances are obtained from the waveform data collected from premises, which include the UiTM Sarawak, Faculty of Science Computer in Shah Alam, Jati College, Menara UiTM, PP Seksyen 18 and Putra LRT. Reliable Power Meter is used for data monitoring and the data is further processed using the Microsoft Excel software. From the processed data, power quality disturbances are detected using the wavelet technique. After the disturbances being detected, it is then classified using the Probabilistic Neural Network. Sixty data has been chosen for the training of the Probabilistic Neural Network and ten data has been used for the testing of the neural network. The results are further interfaced using matlab script code.  Results from the research have been very promising which proved that the wavelet technique and Probabilistic Neural Network is capable to be used for power quality disturbances detection and classification.

Accurate and Transferable Multitask Prediction of Chemical Properties with an Atoms-in-Molecule Neural Network

2018 ◽  
Author(s):  
Roman Zubatyuk ◽  
Justin S. Smith ◽  
Jerzy Leszczynski ◽  
Olexandr Isayev
Keyword(s):  
Neural Network ◽  
Molecular System ◽  
Computational Cost ◽  
Chemical Properties ◽  
The State ◽  
Molecular Properties ◽  
Training Data ◽  
Dft Methods ◽  
Benchmark Datasets ◽  
Quantum Phenomena

<p>Atomic and molecular properties could be evaluated from the fundamental Schrodinger’s equation and therefore represent different modalities of the same quantum phenomena. Here we present AIMNet, a modular and chemically inspired deep neural network potential. We used AIMNet with multitarget training to learn multiple modalities of the state of the atom in a molecular system. The resulting model shows on several benchmark datasets the state-of-the-art accuracy, comparable to the results of orders of magnitude more expensive DFT methods. It can simultaneously predict several atomic and molecular properties without an increase in computational cost. With AIMNet we show a new dimension of transferability: the ability to learn new targets utilizing multimodal information from previous training. The model can learn implicit solvation energy (like SMD) utilizing only a fraction of original training data, and archive MAD error of 1.1 kcal/mol compared to experimental solvation free energies in MNSol database.</p>

Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 1-25
Author(s):  
Yongsen Ma ◽  
Sheheryar Arshad ◽  
Swetha Muniraju ◽  
Eric Torkildson ◽  
Enrico Rantala ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Reinforcement Learning ◽  
Activity Recognition ◽  
Deep Neural Networks ◽  
State Machine ◽  
Recognition Algorithm ◽  
The State ◽  
Neural Architecture ◽  
Learning Agent

In recent years, Channel State Information (CSI) measured by WiFi is widely used for human activity recognition. In this article, we propose a deep learning design for location- and person-independent activity recognition with WiFi. The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting/standing/walking locations/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.

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