Mixed Wavelet-Based Neural Network Model for Cyber Security Situation Prediction Using MODWT and Hurst Exponent Analysis

He, Fannv; Zhang, Yuqing; Liu, Donghang; Dong, Ying; Liu, Caiyun; Wu, Chensi

doi:10.1007/978-3-319-64701-2_8

Cited by 17 publications

(10 citation statements)

References 24 publications

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“…Zhang et al [78] proposed using wavelet neural networks in 2016. Most recently, He et al [79] proposed using a mixed wavelet-based neural network.…”

Section: Output Layermentioning

confidence: 99%

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

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

212

110

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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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“…Zhang et al [78] proposed using wavelet neural networks in 2016. Most recently, He et al [79] proposed using a mixed wavelet-based neural network.…”

Section: Output Layermentioning

confidence: 99%

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

Husák

Komárková

Bou‐Harb

et al. 2019

IEEE Commun. Surv. Tutorials

212

110

View full text Add to dashboard Cite

show abstract

“…In the proposed method the trend of the SMI signal is extracted using the wavelet transformation method. The wavelet transform is a widely used method for the trend analysis and successfully implemented in various fields such as environmental science, biomedical, and cybersecurity [36] , [37] , [38] , [39] . Broadly the proposed method follows four basic steps.…”

Section: Methodsmentioning

confidence: 99%

Measurement of micro-harmonic vibration from optical feedback interferometry using wavelet trend analysis

Bhardwaj

Maini

2020

Optics Communications

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Self-mixed optical feedback interferometry based laser sensors show promising results in the measurement of the vibration frequency. To date several measurement methods have been developed to extract the vibration information from the self-mixed (SM) signal; however, the complexity and accuracy of the methods still need improvement. The presented work tries to fulfill the gap by realizing a novel method using maximal overlap discrete wavelet transformation (MODWT) and multi-resolution analysis (MRA). The proposed method can reconstruct the micro-harmonic (< ) vibration up to 1 kHz even under weak feedback conditions. The mean squared error and the maximum relative error of the proposed method for this range remained below 1.89 & 8.79%, respectively. Although, above 1 kHz, the proposed method turns out to be futile to reconstruct the vibration signal but still capable to measure vibration frequency up to 10 kHz with an accuracy of 0.0001. The method also found suitable to measure non-sinusoidal vibration frequency with reasonable accuracy even for the moderate feedback conditions. The authors envision that the proposed method will provide a compact, non-contact, and low-cost alternative for the vibration frequency measurement hence useful in early fault detection schemes and lung abnormality diagnosis.

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“…A deep neural network (DNN) was used for passenger profiling in aviation to classify ordinary passengers and potential attackers [250]. Authors in [251] proposed a wavelet-based neural network model to detect cyber security problems.…”

Section: ) Convolutional Neural Networkmentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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Mixed Wavelet-Based Neural Network Model for Cyber Security Situation Prediction Using MODWT and Hurst Exponent Analysis

Cited by 17 publications

References 24 publications

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

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

Measurement of micro-harmonic vibration from optical feedback interferometry using wavelet trend analysis

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

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