Multiscale Entropy-Based Weighted Hidden Markov Network Security Situation Prediction Model

Liang, Wei; Chen, Zuo; Yan, Xiaolong; Zheng, Xiaokun; Zhuo, Ping

doi:10.1109/ieee.iciot.2017.31

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…One example of the sequence of transactions can be shown as llmhmlhm. The types of purchases such [13], [27], [43], [86], [93], [101], [107] [2], [34], [39], [50], [59], [113], [118] [30], [41], [77], [83] [4], [8], [17], [36], [38], [42] [6], [7], [18], [48], [74], [92] [28], [33], [40], [47], [53], [67], [71], [97], [105], [114], [116], [119] [54], [55], [112] [15], [70], [77], [95], [99], [102] [10], [44], [66] [41], [53], [56], [74],…”

Section: Credit Card Fraud Detectionmentioning

confidence: 99%

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A systematic review on intrusion detection based on the Hidden Markov Model

Ramaki

Rasoolzadegan

Jafari

2018

Statistical Analysis

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Apart from using traditional security solutions in software systems such as firewalls and access control mechanisms, utilizing intrusion detection systems are also necessary. Intrusion detection is a process in which a set of methods are used to detect malicious activities against the victims. Many techniques for detecting potential intrusions in software systems have already been introduced. One of the most important techniques for intrusion detection based on machine learning is using Hidden Markov Models (HMM). In recent decades, many research communities have been working toward HMM-based intrusion detection. Therefore, a large volume of research works has been published and hence, various research areas have emerged in this field. However, until now, there has been no systematic and up-to-date review of research works within the field. This paper aims to survey the research in this field and provide open problems and challenges based on the analysis of advantages, limitations, types of architectural models, and applications of current techniques. Six various architecture models for intrusion detection purposes are proposed in the literature. We compare these models based on performance criteria in order to select an appropriate type for a specific application. The results show that HMM-based intrusion detection techniques have 6 main advantages-precise intrusion detection, ability to detect new and unknown intrusions, prediction of the intruder's potential next steps, usage in real-time applications by processing data streams on-the-fly, usage of heterogeneous data sources as input, and visual representation of acquired knowledge relative to the other techniques of machine learning. KEYWORDSHidden Markov Model, intrusion detection, intrusion detection system, statistical learning, system and network security

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Section: Credit Card Fraud Detectionmentioning

confidence: 99%

“…Research on alert correlating and predicting systems based on the HMM include [28,33,40,47,53,61,67,71,97,105,114,116,119] which aim to extract and track multistep attack scenarios by analyzing the correlation between alerts created by IDS. As a detailed example, ref.…”

Section: Multistep Attack Detection and Predictionmentioning

confidence: 99%

A systematic review on intrusion detection based on the Hidden Markov Model

Ramaki

Rasoolzadegan

Jafari

2018

Statistical Analysis

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“…It can handle uncertain and fuzzy information, providing more flexible and comprehensive prediction results. For example, methods based on hidden Markov models [22,23], fuzzy neural networks [24], and LSTM with Bayesian optimization algorithms [25] fall into this category. These methods are capable of addressing uncertain information by integrating qualitative and quantitative information for security situation prediction.…”

Section: Introductionmentioning

confidence: 99%

A Security Situation Prediction Model for Industrial Control Network Based on EP-CMA-ES

Wang,

Yang,

Gao

et al. 2023

IEEE Access

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To solve the problem of industrial control network (ICN) security situation prediction, this paper proposes a security situation prediction model for ICN based on evidential reasoning (ER) and belief rule base (BRB). First, this paper analyzes multiple factors influencing the security situation of the ICN, establishes a framework for security situation assessment, employs the ER algorithm for attribute fusion, and derives the security situation value of the ICN. Second, using historical data combined with expert knowledge, a security situation prediction model for ICN based on the BRB is constructed. Additionally, an extended projection covariance matrix adaptive evolution strategy (EP-CMA-ES) optimization algorithm is proposed, which is employed to optimize the parameters of the prediction model. The model not only comprehensively uses qualitative knowledge and quantitative data, but also integrates more uncertain information, and the reasoning process is interpretable. It also solves the subjectivity problem of expert knowledge, overcomes the problem of small amount of data caused by the difficulty of collecting industrial control safety data, and improves the accuracy of model prediction. Finally, prediction experiments were conducted on industrial datasets, confirming the feasibility and effectiveness of the security situation prediction model for ICN and the EP-CMA-ES optimization algorithm proposed in this paper.

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“…Liang W [6] et al Proposed a network security situation prediction model based on the weighted Hidden Markov Model to solve the problem that existing methods cannot make full use of historical data to predict future changes. Firstly, multi-scale entropy information is used to solve the problem of training data, then the parameters of HMM transfer matrix are optimized, and finally, the autocorrelation coefficient is used to predict the future security situation based on the correlation between the features of historical data.…”

Section: Introductionmentioning

confidence: 99%

Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network

Zhang

et al. 2021

Preprint

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The traditional network security situation prediction method depends on the accuracy of historical situation values, and there are correlations and differences in importance among various network security factors. To solve the above problems, a combined forecasting model based on Empirical Mode Decomposition and improved Particle Swarm Optimization (ELPSO) to optimize BiGRU neural network (EMD-ELPSO-BiGRU) is proposed. Firstly, the model decomposes the network security situation data sequence into a series of intrinsic modal components by empirical mode decomposition; Then, the prediction model of the BiGRU neural network is established for each modal component, and an improved Particle Swarm Optimization Algorithm (ELPSO) is proposed to optimize the super parameters of BiGRU neural network. Finally, the prediction results of each modal component are superimposed to obtain the final prediction value of the network security situation. In the experiment, on the one hand, ELPSO is compared with other particle swarm optimization algorithms, and the results show that ELPSO has better optimization performance; On the other hand, through simulation test and comparison between EMD-ELPSO-BiGRU and other traditional forecasting methods, the results show that the established combined forecasting model has higher forecasting accuracy.

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Multiscale Entropy-Based Weighted Hidden Markov Network Security Situation Prediction Model

Cited by 6 publications

References 10 publications

A systematic review on intrusion detection based on the Hidden Markov Model

A systematic review on intrusion detection based on the Hidden Markov Model

A Security Situation Prediction Model for Industrial Control Network Based on EP-CMA-ES

Network Security Situation Prediction Model Based on EMD and ELPSO Optimized BiGRU Neural Network

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