scholarly journals A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base

2021 ◽  
Vol 11 (21) ◽  
pp. 9899
Author(s):  
Guozhu Wang ◽  
Yiwen Cui ◽  
Jie Wang ◽  
Lihua Wu ◽  
Guanyu Hu
Keyword(s):  
Expert Knowledge ◽  
Attack Detection ◽  
Detection Methods ◽  
Small Samples ◽  
Rule Base ◽  
Uncertain Information ◽  
Advanced Persistent Threat ◽  
Detection Model ◽  
Belief Rule Base ◽  
Stage Characteristic

Advanced persistent threat (APT) is a special attack method, which is usually initiated by hacker groups to steal data or destroy systems for large enterprises and even countries. APT has a long-term and multi-stage characteristic, which makes it difficult for traditional detection methods to effectively identify. To detect APT attacks requires solving some problems: how to deal with various uncertain information during APT attack detection, how to fully train the APT detection model with small attack samples, and how to obtain the interpretable detection results for subsequent APT attack forensics. Traditional detection methods cannot effectively utilize multiple uncertain information with small samples. Meanwhile, most detection models are black box and lack a transparent calculation process, which makes it impossible for managers to analyze the reliability and evidence of the results. To solve these problems, a novel detection method based on belief rule base (BRB) is proposed in this paper, where expert knowledge and small samples are both utilized to obtain interpretable detection results. A case study with numerical simulation is established to prove the effectiveness and practicality of the proposed method.

Interval-Valued Belief Rule Inference Methodology Based on Evidential Reasoning-IRIMER

2016 ◽  
Vol 15 (06) ◽  
pp. 1345-1366 ◽  
Author(s):  
Hua Zhu ◽  
Jianbin Zhao ◽  
Yang Xu ◽  
Limin Du
Keyword(s):  
Implementation Process ◽  
Evidential Reasoning ◽  
Rule Base ◽  
Uncertain Information ◽  
Ieee Transaction ◽  
Belief Rule Base ◽  
Representation Scheme ◽  
Interval Valued ◽  
Rule Inference ◽  
Rule Representation

In this paper, an interval-valued belief rule inference methodology based on evidential reasoning (IRIMER) is proposed, which includes the interval-valued belief rule representation scheme and its inference methodology. This interval-valued belief rule base is designed with interval-valued belief degrees embedded in both the consequents and the antecedents of each rule, which can represent uncertain information or knowledge more flexible and reasonable than the previous belief rule base. Then its inference methodology is developed on the interval-valued evidential reasoning (IER) approach. The IRIMER approach improves and extends the recently uncertainty inference methods from the rule representation scheme and the inference framework. Finally, a case is studied to demonstrate the concrete implementation process of the IRIMER approach, and comparison analysis shows that the IRIMER approach is more flexible and effective than the RIMER [J. B. Yang, J. Liu, J. Wang, H. S. Sii and H. W. Wang, Belief rule-base interference methodology using the evidential reasoning approach-RIMER, IEEE Transaction on Systems Man and Cybernetics Part A-Systems and Humans36 (2006) 266–285.] approach and the ERIMER [J. Liu, L. Martínez, A. Calzada and H. Wang, A novel belief rule base representation, generation and its inference methodology, Knowledge-Based Systems 53 (2013) 129–141.] approach.

scholarly journals A new health prediction model for a sensor network based on belief rule base with attribute reliability

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shaohua Li ◽  
Jingying Feng ◽  
Wei He ◽  
Ruihua Qi ◽  
He Guo
Keyword(s):  
Prediction Model ◽  
Sensor Network ◽  
Expert Knowledge ◽  
Average Distance ◽  
Rule Base ◽  
Distance Method ◽  
Oil Storage ◽  
Belief Rule Base ◽  
Qualitative Knowledge ◽  
Monitoring Information

AbstractHealth prediction plays an essential role in improving the reliability of a sensor network by guiding the network maintenance. However, affected by interference factors in the real operational environment, the reliability of the monitoring information about the sensor network tends to decline, which affects the health prediction accuracy. Furthermore, the lack of monitoring information and high complexity of the network increase the difficulty of health prediction. To solve these three problems, this paper proposes a new sensor network health prediction model based on the belief rule base model with attribute reliability (BRB-r). The BRB-r model is an expert system that fully considers the qualitative knowledge and quantitative data of the sensor network. In addition, it can address the fuzziness and nondeterminacy of this qualitative knowledge. In the new model, the unreliable monitoring information of the sensor network is handled by the attribute reliability mechanism. The reliability of the sensor is calculated by the average distance method. Due to the effect of the fuzziness and nondeterminacy of expert knowledge, the health status of the sensor network cannot be accurately estimated by the initial health prediction model. Consequently, the optimization model for the health prediction model is established. Finally, a case study regarding a sensor network for oil storage tanks is conducted, and the validity of this method is demonstrated.

scholarly journals An Improved SQL Injection Attack Detection Model Using Machine Learning Techniques

2021 ◽  
Vol 11 (1) ◽  
pp. 53-57
Author(s):  
Yazeed Abdulmalik
Keyword(s):  
Machine Learning ◽  
Web Application ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Detection Methods ◽  
Semantic Features ◽  
Sql Injection ◽  
Detection Model ◽  
Learning Techniques ◽  
Sql Injection Attack

SQL Injection Attack (SQLIA) is a common cyberattack that target web application database. With the ever increasing and varying techniques to exploit web application SQLIA vulnerabilities, there is no a comprehensive method that can solve this kind of attacks. Therefore, these various of attack techniques required to establish many methods against in order to mitigate its threats. However, most of these methods have not yet been evaluated, where it is still just theories and require to implement and measure its performance and set its limitation. Moreover, most of the existing SQL injection countermeasures either used syntax-based detection methods or a list of predefined rules to detect the SQL injection, which is vulnerable in advance and sophisticated type of attacks because attackers create new ways to evade the detection utilizing their pre-knowledge. Although semantic-based features can improve the detection, up to our knowledge, no studies focused on extracting the semantic features from SQL stamens. This paper, investigates a designed model that can improve the efficacy of the SQL injection attack detection using machine learning techniques by extracting the semantic features that can effectively indicate the SQL injection attack. Also, a tenfold approach will be used to evaluate and validate the proposed detection model.

A novel structural safety assessment method of large liquid tank based on the belief rule base and finite element method

2021 ◽  
pp. 1748006X2110216
Author(s):  
Yuan Chen ◽  
Zhijie Zhou ◽  
Lihao Yang ◽  
Guanyu Hu ◽  
Xiaoxia Han ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Safety Assessment ◽  
Expert Knowledge ◽  
Assessment Method ◽  
Assessment Model ◽  
Structural Safety ◽  
Rule Base ◽  
Belief Rule Base ◽  
Element Method

The structural safety assessment of large liquid tanks (LLT) has attracted an extensive attention. As a typical gray box model, the belief rule base (BRB) model can handle qualitative information and quantitative data simultaneously, which is a suitable modeling tool for structural safety assessment. However, it is difficult to establish and train the BRB model when there is a lack of expert experience and fault samples of LLT. Therefore, a novel safety assessment model for LLT based on BRB and finite element method (FEM-BRB) is proposed in this paper. The FEM is introduced to construct the BRB model by combining expert knowledge and industry standards for the first time, which can effectively compensate for the lack of expert experience. The fault samples are generated in the mechanism simulation model under different working conditions. Based on the fault samples generated by the FEM and historical samples, the projection covariance matrix adaption evolution strategy (P-CMA-ES) optimization algorithm is then used to train the model, which further improves the structural safety assessment accuracy when lacking fault samples. A case study of three actual oil tanks in a coastal port is conducted to illustrate the effectiveness and advantage of the developed structural safety assessment method.

A new risk assessment method based on belief rule base and fault tree analysis

2021 ◽  
pp. 1748006X2110114
Author(s):  
Hai-Long Zhu ◽  
Shan-Shan Liu ◽  
Yuan-Yuan Qu ◽  
Xiao-Xia Han ◽  
Wei He ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Expert Knowledge ◽  
Fault Tree ◽  
Fault Tree Analysis ◽  
Evaluation Process ◽  
Assessment Method ◽  
Assessment Model ◽  
Rule Base ◽  
Tree Analysis ◽  
Belief Rule Base

Risk assessment methods are often used in complex industrial systems to avoid risks and reduce losses. The existing methods have not effectively solved the problems of lack of evaluation data and the interpretability of the entire evaluation process. This paper proposes a new risk assessment model based on the belief rule base (BRB) and Fault Tree Analysis (FTA). The FTA algorithm overcomes the difficulties of traditional BRB model in obtaining expert knowledge, clear indicators, and establishing logical relationships. This method establishes FTA rules based on the BRB model and expands the knowledge base through the FTA algorithm. A Bayesian network is applied as a conversion bridge between the FTA and BRB model. In addition, the model is optimized to reduce the uncertainty in the model. The method proposed is described by a case and its effectiveness is verified.

scholarly journals Coordinated Cyber-Attack Detection Model of Cyber-Physical Power System Based on the Operating State Data Link

2021 ◽  
Vol 9 ◽  
Author(s):  
Lei Wang ◽  
Pengcheng Xu ◽  
Zhaoyang Qu ◽  
Xiaoyong Bo ◽  
Yunchang Dong ◽  
...  
Keyword(s):  
Power System ◽  
Principal Component ◽  
Attack Detection ◽  
Detection Methods ◽  
Gradient Boosting ◽  
Intensity Difference ◽  
Detection Accuracy ◽  
Cyber Attack ◽  
Data Link ◽  
Detection Model

Existing coordinated cyber-attack detection methods have low detection accuracy and efficiency and poor generalization ability due to difficulties dealing with unbalanced attack data samples, high data dimensionality, and noisy data sets. This paper proposes a model for cyber and physical data fusion using a data link for detecting attacks on a Cyber–Physical Power System (CPPS). The two-step principal component analysis (PCA) is used for classifying the system’s operating status. An adaptive synthetic sampling algorithm is used to reduce the imbalance in the categories’ samples. The loss function is improved according to the feature intensity difference of the attack event, and an integrated classifier is established using a classification algorithm based on the cost-sensitive gradient boosting decision tree (CS-GBDT). The simulation results show that the proposed method provides higher accuracy, recall, and F-Score than comparable algorithms.

Implementation of Fuzzy Logic Control on Battery Charging System

2019 ◽  
Vol 3 (1) ◽  
pp. 118-126 ◽  
Author(s):  
Prihangkasa Yudhiyantoro
Keyword(s):  
Complex System ◽  
Fuzzy Logic ◽  
Fuzzy Logic Control ◽  
Expert Knowledge ◽  
Experimental Result ◽  
Rule Base ◽  
Battery Charging ◽  
Charging System ◽  
Logic Control ◽  
Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the  embership function and the rule base of the fuzzy controller.

Brief Survey on Attack Detection Methods for Cyber-Physical Systems

2020 ◽  
Vol 14 (4) ◽  
pp. 5329-5339 ◽  
Author(s):  
Sen Tan ◽  
Josep M. Guerrero ◽  
Peilin Xie ◽  
Renke Han ◽  
Juan C. Vasquez
Keyword(s):  
Cyber Physical Systems ◽  
Attack Detection ◽  
Detection Methods ◽  
Physical Systems

scholarly journals A Fusion Prognostic Method for Remaining Useful Life Prediction Based on an Extended Belief Rule Base and Particle Filters

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Bincheng Wen ◽  
Mingqing Xiao ◽  
Guanghao Wang ◽  
Zhao Yang ◽  
Jianfeng Li ◽  
...  
Keyword(s):  
Life Prediction ◽  
Particle Filters ◽  
Remaining Useful Life ◽  
Rule Base ◽  
Belief Rule Base ◽  
Useful Life
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