Sensor network security defense strategy based on attack graph and improved binary PSO

Dong, Chaoxian; Liang, Zhi Qiang

doi:10.1016/j.ssci.2019.04.007

Cited by 21 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dilated convolution takes interval sampling in each layer for convolution sampling. e size of the interval is determined by the dilation factor d, as shown in Figure 3, where the kernel size is 2 and dilations are [1,2,4]. e size of each layer d of the dilated convolution grows exponentially, where the first layer is 1 (1 means the interval is 0).…”

Section: Dilated Convolutionmentioning

confidence: 99%

See 1 more Smart Citation

A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM

Yao¹,

Yang²,

Yang³

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

The rapid development of information technology has brought much convenience to human life, but more network threats have also come one after another. Network security situation prediction technology is an effective means to protect against network threats. Currently, the network environment is characterized by high data traffic and complex features, making it difficult to maintain the accuracy of the situation prediction. In this study, a network security situation prediction model based on attention mechanism (AM) improved temporal convolutional network (ATCN) combined with bidirectional long short-term memory (BiDLSTM) network is proposed. The TCN is improved by AM to extract the input temporal features, which has a more stable feature extraction capability compared with the traditional TCN and BiDLSTM, which is more capable of processing temporal data, and is used to perform the situation prediction. Finally, by validating on a real network traffic dataset, the proposed method has better performance on multiple loss functions and has more accurate and stable prediction results than TCN, BiDLSTM, TCN-LSTM, and other time-series prediction methods.

show abstract

Section: Dilated Convolutionmentioning

confidence: 99%

“…ere are many methods for network security situation prediction. e main research focuses on two aspects based on time-series prediction [3] and graph theory-based prediction [4].…”

Section: Introductionmentioning

confidence: 99%

A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM

Yao¹,

Yang²,

Yang³

et al. 2022

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…In fact, it assumes that the particles have fallen into the local optimum when the gbest values are unchanged after three generations. The particles have to be induced to leave the local optimum using the 'and logical operation' 'and' pbest of all particles [163,164].…”

Section: Eai Endorsed Transactions On Security and Safetymentioning

confidence: 99%

A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks

Mliki¹,

Kaceam²,

Fourati³

2021

ICST Transactions on Security and Safety

View full text Add to dashboard Cite

The Internet of things (IoT) is a new ubiquitous technology that relies on heterogeneous devices and protocols. The IoT technologies are expected to offer a new level of connectivity thanks to its smart devices able to enhance everyday tasks and facilitate smart decisions based on sensed data. The IoT could collect sensitive data and should be able to face attacks and privacy issues. The IoT security issue is a hot topic of research and industrial concern. Indeed, threats against IoT devices and services could cause security breaches and data leakage. Aiming to identify attempts to abuse the IoT systems and mitigate malicious events, this paper studied the Intrusion Detection Systems (IDS) based on Machine Learning (ML) techniques. The ML approach could provide good tools to detect novel intrusion activities in a timely manner. This paper, therefore, highlighted the related issues to develop secured and efficient IoT services. It tried to allow a comprehensive review of IoT features and design. It mainly focused on intrusion detection based on the machine learning schema and built a taxonomy of different IoT attacks and threats. This paper also compared between the different intrusion detection techniques and established a taxonomy of machine leaning methods for intrusion detection solutions.

show abstract

“…These four gates are used to retain the error gradient through some limited algorithms. LSTM needs to use formula combination to give the restriction of each door to information transmission 18 …”

Section: Deep Time Series Network Security Situation Predictionmentioning

confidence: 99%

Network security situation prediction based on combining associated entropy and deep recurrent neural network

Xin-cun

Wang

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

The existing network security situation prediction algorithm cannot deal with the subjective attitude deviation of multi-experts, and the traditional sequential machine learning model cannot deal with the problem of deviation accumulation in time period. In this paper, an algorithm of network security situation prediction combining entropy correlation and deep time series network model is proposed. First, the expert fuzzy evaluation index is constructed by trigonometric fuzzy function, and the modified weighted dempster-shafer (DS) evidence reasoning correction index is used, then the loss and possibility matrix features are created, and finally the information security is evaluated by deep time series network. The simulation experiments are carried out on MIT dataset. The experiments analyze whether the features can cope with multi-expert conflicts, and evaluate the accuracy, robustness, and time efficiency of the algorithm.The experimental results show that the algorithm proposed in this paper has stronger fuzzy evaluation ability, stronger ability to deal with conflict opinions among experts, more accurate prediction of network security situation in time sequence, and higher robustness, but the efficiency of the algorithm has been maintained.

show abstract

Sensor network security defense strategy based on attack graph and improved binary PSO

Cited by 21 publications

References 13 publications

A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM

A Network Security Situation Prediction Method through the Use of Improved TCN and BiDLSTM

A Comprehensive Survey on Intrusion Detection based Machine Learning for IoT Networks

Network security situation prediction based on combining associated entropy and deep recurrent neural network

Contact Info

Product

Resources

About