Representation of Markov's chains functions over finite field based on stochastic matrix lumpability

Zakharov, V. M.; Eminov, B. F.; Shalagin, S. V.

doi:10.1109/icieam.2016.7911662

Cited by 6 publications

(3 citation statements)

References 1 publication

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“…An important innovation in implementing the proposed algorithm is the extension of the GLSM probabilistic algorithm, with the introduction of time intervals and confidence points in the cases where we have samples of independent and identically distributed variables and correspondingly when we have a Markov chain [ 29 ]. This extension leads to a system that can extract probabilistic recognition of complex events over time from a stream of low-level events.…”

Section: Proposed Deep Spiking Neural Architecturementioning

confidence: 99%

A Deep Spiking Neural Network Anomaly Detection Method

Liu

Qiu

2022

Computational Intelligence and Neuroscience

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Cyber-attacks on specialized industrial control systems are increasing in frequency and sophistication, which means stronger countermeasures need to be implemented, requiring the designers of the equipment in question to re-evaluate and redefine their methods for actively protecting against advanced mass cyber-attacks. The attacks in question have huge motivations, ranging from corporate espionage to political targets, but in any case, they have a substantial financial impact and severe real-world implications. It should also be said that it is challenging to defend against cyber threats because a single point of entry can be enough to destroy an entire organization or put it out of business. This paper examines threats to the digital security of vibration monitoring systems used in petroleum infrastructure protection services, such as pipelines, pumps, and tank farms, where malicious interventions can cause explosions, fires, or toxic releases, with incalculable economic and environmental consequences. Specifically, a deep spiking neural network anomaly detection method is presented, which models the spike sequences and the internal presentation mechanisms of the information to discover with very high accuracy anomalies in vibration analysis systems used in oil infrastructure protection services. This is achieved by simulating the complex structures of the human brain and the way neural information is processed and transmitted. This work uses a particularly innovative form of the Galves–Löcherbach Spiking Model (GLSM) [1], which is a spiking neural network model with intrinsic stochasticity, ideal for modeling complex spatiotemporal situations, which is enhanced with possibilities of exploiting confidence intervals by modeling optimally stochastic variable-length memory chains that have a finite state space.

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Section: Proposed Deep Spiking Neural Architecturementioning

confidence: 99%

A Deep Spiking Neural Network Anomaly Detection Method

Liu

Qiu

2022

Computational Intelligence and Neuroscience

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“…Variational inference is a relatively well-known and widely used modeling technique used to address unsolvable problems that arise in the context of Statistical Inference. In the literature, there are several instances of implementation of variational inference methods related to models like Variational Bayes [13,14], Expectation-Maximization [15], Maximum A Posteriori Estimation [16,17], Markov Chain [18,19], Monte Carlo methods as Gibbs Sampling [20,21], and variational autoencoders [22].…”

Section: Related Literature Workmentioning

confidence: 99%

An Autonomous Cyber-Physical Anomaly Detection System Based on Unsupervised Disentangled Representation Learning

Guo

Wang

2021

Security and Communication Networks

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Cyber-Physical Systems (CPS) in heavy industry are a combination of closely integrated physical processes, networking, and scientific computing. The physical production process is monitored and controlled by the CPS in question, through advanced real-time networking systems, where high-precision feedback loops can be changed when the overgrid of cooperative computing and communication components that make up the industrial process is required. These CPS operate independently but integrate interaction capabilities as well as with the external environment, creating the connection of the physical with the digital world. The outline is that the most effective modeling and development of high-reliability CPS are directly related to the maximization of the production process, extroversion, and industrial competition. In this paper, considering the high importance of the operational status of CPS for heavy industry, an innovative autonomous anomaly detection system based on unsupervised disentangled representation learning is presented. It is a temporal disentangled variational autoencoder (TDVA) which, mimicking the process of rapid human intuition, using high- or low-dimensional reasoning, finds and models the useful information independently, regardless of the given problem. Specifically, taking samples from the real data distribution representation space, separating them appropriately, and encoding them as separate disentangling dimensions create new examples that the system has not yet dealt with. In this way, first, it utilizes information from potentially inconsistent sources to learn the right representations that can then be broken down into subspace subcategories for easier and simpler categorization, and second, utilizing the latent representation of the model, it performs high-precision estimates of how similar or dissimilar the inputs are to each other, thus recognizing, with great precision and in a fully automated way, the system anomalies.

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“…[18], [19], [20]. Markov Chain juga digunakan untuk melakukan prediksi perubahan di waktu yang akan datang pada variabel-variabel dinamis atas dasar perubaha dari variabel-variabel dinamis tersebut dari waktu yang lalu [21], [22]. Markov Chain hampir sama dengan analisa keputusan, perbedaannya adalah analisis markov tidak memberikan keputusan tetapi hanya informasi probabilitas mengenai situasi keputusan yang dapat membantu mengambil keputusan [23], [24], [17].…”

Section: Pendahuluanunclassified

Analisa Prediksi Pertumbuhan Start-Up Di Era Industri 4.0 Menggunakan Metode Markov Chain

2020

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Bisnis Start-up semakin tumbuh dan berkembang di berbagai daerah di Indonesia, tidak terkecuali di Jabodetabek. Dari bisnis Start-up dapat menghasilkan berbagai platform atau aplikasi yang diminati konsumen, seperti marketplace pada E-commerce. Jabodetabek merupakan daerah dengan tingkat pertumbuhan Start-up tertinggi sebanyak 522 Start-up, yang mayoritas berasal dari kota Jakarta. Penggunaan metode Markov chain untuk memprediksi pertumbuhan dengan melakukan perhitungan matriks transisi probabilitas, serta menghitung probabilitas keadaan sampai diperoleh keadaan Steady state yang merupakan data acuan untuk hasil prediksi pertumbuhan Start-up di Jabodetabek di 5 tahun berikutnya.

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Representation of Markov's chains functions over finite field based on stochastic matrix lumpability

Cited by 6 publications

References 1 publication

A Deep Spiking Neural Network Anomaly Detection Method

A Deep Spiking Neural Network Anomaly Detection Method

An Autonomous Cyber-Physical Anomaly Detection System Based on Unsupervised Disentangled Representation Learning

Analisa Prediksi Pertumbuhan Start-Up Di Era Industri 4.0 Menggunakan Metode Markov Chain

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