Improving Language Translation Using the Hidden Markov Model

Chang, Yunpeng; Xiao-liang, Wang; Xue, Meihua; Liu, Yuzhen; Jiang, Frank

doi:10.32604/cmc.2021.012304

Cited by 5 publications

(3 citation statements)

References 17 publications

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“…As a fundamental task in natural language processing, text classification is essential for entity recognition, relationship extraction, and knowledge graph construction. Traditional classification methods are based on the manual extraction of text features with machine learning, such as Bayesian classifiers, Decision Trees, SVM [14] and the Hidden Markov Model (HMM) [15], which are widely used in text classification tasks. At present, deep learning-based methods have been applied to text classification tasks by training different neural network models, such as Convolutional Neural Network (CNN) [16], Recurrent Neural Network (RNN) [17], and Bidirectional Long Short-Term Memory (BiLSTM) [18], etc.…”

Section: Text Classification Methodsmentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

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The continuous improvement of the cyber threat intelligence sharing mechanism provides new ideas to deal with Advanced Persistent Threats (APT). Extracting attack behaviors, i.e., Tactics, Techniques, Procedures (TTP) from Cyber Threat Intelligence (CTI) can facilitate APT actors' profiling for an immediate response. However, it is difficult for traditional manual methods to analyze attack behaviors from cyber threat intelligence due to its heterogeneous nature. Based on the Adversarial Tactics, Techniques and Common Knowledge (ATT&CK) of threat behavior description, this paper proposes a threat behavioral knowledge extraction framework that integrates Heterogeneous Text Network (HTN) and Graph Convolutional Network (GCN) to solve this issue. It leverages the hierarchical correlation relationships of attack techniques and tactics in the ATT&CK to construct a text network of heterogeneous cyber threat intelligence. With the help of the Bidirectional Encoder Representation from Transformers (BERT) pretraining model to analyze the contextual semantics of cyber threat intelligence, the task of threat behavior identification is transformed into a text classification task, which automatically extracts attack behavior in CTI, then identifies the malware and advanced threat actors. The experimental results show that F1 achieve 94.86% and 92.15% for the multi-label classification tasks of tactics and techniques. Extend the experiment to verify the method's effectiveness in identifying the malware and threat actors in APT attacks. The F1 for malware and advanced threat actors identification task reached 98.45% and 99.48%, which are better than the benchmark model in the experiment and achieve state of the art. The model can effectively model threat intelligence text data and acquire knowledge and experience migration by correlating implied features with a priori knowledge to compensate for insufficient sample data and improve the classification performance and recognition ability of threat behavior in text.

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Section: Text Classification Methodsmentioning

confidence: 99%

Attack Behavior Extraction Based on Heterogeneous Cyberthreat Intelligence and Graph Convolutional Networks

Tang¹,

Wang²,

Qiu³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…However, for time series data with nonlinear characteristics, the linear prediction model is difficult to fit effectively. Due to the limitations of linear prediction methods, machine learning-based prediction methods, such as the hidden Markov model (HMM) [16], support vector regression (SVR) model [17], and extreme gradient boosting (XGBoost) model [18] have been proposed. Machine learning-based prediction models essentially build mapping relationships between time series data.…”

Section: Related Workmentioning

confidence: 99%

“…The differences between the predicted value and the true value of the stock closing price by MGE-SP, ARIMA, and k-means-SVR are shown in Figs. [14][15][16], respectively. It can be seen from Fig.…”

Section: Experiments On Stockmentioning

confidence: 99%

A Time Series Short-Term Prediction Method Based on Multi-Granularity Event Matching and Alignment

Li,

Yu,

Zhao

et al. 2024

CMC

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Accurate forecasting of time series is crucial across various domains. Many prediction tasks rely on effectively segmenting, matching, and time series data alignment. For instance, regardless of time series with the same granularity, segmenting them into different granularity events can effectively mitigate the impact of varying time scales on prediction accuracy. However, these events of varying granularity frequently intersect with each other, which may possess unequal durations. Even minor differences can result in significant errors when matching time series with future trends. Besides, directly using matched events but unaligned events as state vectors in machine learning-based prediction models can lead to insufficient prediction accuracy. Therefore, this paper proposes a short-term forecasting method for time series based on a multi-granularity event, MGE-SP (multigranularity event-based short-term prediction). First, a methodological framework for MGE-SP established guides the implementation steps. The framework consists of three key steps, including multi-granularity event matching based on the LTF (latest time first) strategy, multi-granularity event alignment using a piecewise aggregate approximation based on the compression ratio, and a short-term prediction model based on XGBoost. The data from a nationwide online car-hailing service in China ensures the method's reliability. The average RMSE (root mean square error) and MAE (mean absolute error) of the proposed method are 3.204 and 2.360, lower than the respective values of 4.056 and 3.101 obtained using the ARIMA (autoregressive integrated moving average) method, as well as the values of 4.278 and 2.994 obtained using k-means-SVR (support vector regression) method. The other experiment is conducted on stock data from a public data set. The proposed method achieved an average RMSE and MAE of 0.836 and 0.696, lower than the respective values of 1.019 and 0.844 obtained using the ARIMA method, as well as the values of 1.350 and 1.172 obtained using the k-means-SVR method.

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