A Novel Adaptive Grey Verhulst Model for Network Security Situation Prediction

Leau, Yu‐Beng; Manickam, Selvakumar

doi:10.14569/ijacsa.2016.070113

Cited by 9 publications

(5 citation statements)

References 13 publications

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“…In 2016, Leau and Manickam [69] endeavor to overcome the limitations of GM (1, 1) and Grey-Verhulst models, namely that they are accurate only for specific input series. In their work, they introduce an adaptive Grey-Verhulst model that is robust as applied to wider types of time series.…”

Section: B Grey Modelsmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

192

100

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This paper provides a survey of prediction, and forecasting methods used in cyber security. Four main tasks are discussed first, attack projection and intention recognition, in which there is a need to predict the next move or the intentions of the attacker, intrusion prediction, in which there is a need to predict upcoming cyber attacks, and network security situation forecasting, in which we project cybersecurity situation in the whole network. Methods and approaches for addressing these tasks often share the theoretical background and are often complementary. In this survey, both methods based on discrete models, such as attack graphs, Bayesian networks, and Markov models, and continuous models, such as time series and grey models, are surveyed, compared, and contrasted. We further discuss machine learning and data mining approaches, that have gained a lot of attention recently and appears promising for such a constantly changing environment, which is cyber security. The survey also focuses on the practical usability of the methods and problems related to their evaluation.

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Section: B Grey Modelsmentioning

confidence: 99%

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

192

100

View full text Add to dashboard Cite

show abstract

“…Recall: Recall, as in (12), is the ratio of correctly predicted positive attack traffic to all traffic in actual attack class. this a metric to judge the correctness to distinguish positive attacks.…”

Section: Metricsmentioning

confidence: 99%

“…Nowadays, many types of IDS play an important role in the network security business, which enhances the security of the network and protects users from cyberattacks. Scholars have used various methods to classify attacks, including traditional methods based on hidden Markov model [11], gray Verhulst models [12] and so on.…”

Section: Introductionmentioning

confidence: 99%

A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System

2020

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The development of computer and network technology has provided convenience to our daily life, however, attack and intrusion in network emerge endlessly. Intrusion Detection System (IDS) has been developed to confront network attacks. As a result, the research of IDS is one of the most popular fields in recent years. This paper proposes a Gradient Boosting Decision Tree (GBDT)-paralleled quadratic ensemble learning method for intrusion detection system. We use GBDT to deal with the spatial part of traffic data and use Gated Recurrent Unit (GRU) model with special modification for network traffic to deal with temporal data. Then, in order to combine the spatial feature and temporal feature, we fuse GBDT model and GRU model to make a quadratic ensemble model as our final intrusion detection system. The experimental results based on CICIDS2017 dataset show that the advanced spatial-temporal intrusion detection system based on ensemble learning achieves better accuracy, recall, precision and F1 score than the state-of-the-art methods. The accuracies of detecting benign, port scan, Distributed Denial of Service (DDoS), infiltration and web attack traffic are up to 99.9%, 99.9%, 99.9%, 99.9%, and 99.9%, respectively. We also use our method in Information-Centric Networking (ICN) dataset and the results show our method achieves much better performance compared with existing methods.

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“…The experiments on the KDD Cup 99 data set showed that the model was suitable and well developed in network security. Aiming at the deficiency of the gray Verhulst model, Leau et al [12] designed an adaptive gray Verhulst model with adjustable generation order, tested the model with DARPA 1999 and 2000 benchmark data sets, and found that the model showed good performance in predicting the network. Kim et al [13] pointed out that the traditional time series analysis could not predict the dynamic network and proposed a hidden Markov model (HMM) to analyze and predict the realtime changes of network traffic.…”

Section: Introductionmentioning

confidence: 99%

Research on gray correlation analysis and situation prediction of network information security

Shi

Zhang

2021

EURASIP J. on Info. Security

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In order to further improve the accuracy and efficiency of network information security situation prediction, this study used the dynamic equal-dimensional method based on gray correlation analysis to improve the GM (1, N) model and carried out an experiment on the designed network security situation prediction (NSSP) model in a simulated network environment. It was found that the predicted result of the improved GM (1, N) model was closer to the actual value. Taking the 11th hour as an example, the predicted value of the improved GM (1, N) model was 28.1524, which was only 0.8983 larger than the actual value; compared with neural network and Markov models, the error of the improved GM (1, N) model was smaller: the average error was only 2.3811, which was 67.88% and 70.31% smaller than the other two models. The improved GM (1, N) model had a time complexity that was 49.99% and 39.53% lower than neural network and Markov models; thus, it had high computational efficiency. The experimental results verify the effectiveness of the improved GM (1, N) model in solving the NSSP problem. The improved GM (1, N) model can be further promoted and applied in practice and deployed in the network of schools and enterprises to achieve network information security.

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A Novel Adaptive Grey Verhulst Model for Network Security Situation Prediction

Cited by 9 publications

References 13 publications

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

Survey of Attack Projection, Prediction, and Forecasting in Cyber Security

A GBDT-Paralleled Quadratic Ensemble Learning for Intrusion Detection System

Research on gray correlation analysis and situation prediction of network information security

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