Evaluating Accuracy in Prudence Analysis for Cyber Security

Maruatona, Omaru; Vamplew, Peter; Dazeley, Richard; Watters, Paul A.

doi:10.1007/978-3-319-70139-4_41

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…where p( , ) is the joint probability function of Φ and Ω, and p( ) and p( ) are the probability distribution functions of Φ and Ω, respectively. F and ACC are derivations from the confusion matrix whose structure is reflected in Table 2 [40,41]. F comprehensively considers the proportion of true positive result in…”

Section: Engineering Applicationmentioning

confidence: 99%

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

Tan

Zhang

et al. 2019

Complexity

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As a crucial and widely used component in industrial fields with great complexity, the health condition of rotating machinery is directly related to production efficiency and safety. Consequently, recognizing and diagnosing rotating machine faults remain to be one of the main concerns in preventing failures of mechanical systems, which can enhance the reliability and efficiency of mechanical systems. In this paper, a novel approach based on blind parameter identification of MAR model and mutation hybrid GWO-SCA optimization is proposed to diagnose faults for rotating machinery. Signals collected from different types of faults were firstly split into sets of intrinsic mode functions (IMFs) by variational mode decomposition (VMD), the decomposing mode number K of which was preset with central frequency observation method. Then the multivariate autoregressive (MAR) model of all IMFs was established, whose order was determined by Schwartz Bayes Criterion (SBC), and all parameters of the model were identified blindly through QR decomposition, where key features were subsequently extracted via principal component analysis (PCA) to construct feature vectors of different fault types. Afterwards, a hybrid optimization algorithm combining mutation operator, grey wolf optimizer (GWO), and sine cosine algorithm (SCA), termed mutation hybrid GWO-SCA (MHGWOSCA), was proposed for parameter selection of support vector machine (SVM). The optimal SVM model was later employed to classify different fault samples. The engineering application and contrastive analysis indicate the availability and superiority of the proposed method.

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Section: Engineering Applicationmentioning

confidence: 99%

Blind Parameter Identification of MAR Model and Mutation Hybrid GWO‐SCA Optimized SVM for Fault Diagnosis of Rotating Machinery

Tan

Zhang

et al. 2019

Complexity

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“…The PAEM refers to such expected cases which are requiring prompt to be far less common as compared to the cases which do not require prompt. For detection system based on classification, it may also be worthwhile to evaluate the proposed model not just in term of the classification accuracy which is often criticized in literature but also relative to its classifier and prudence accuracy [28]. Therefore, the PAEM is applied to evaluate the effectiveness of the proposed prudence alert system.…”

Section: Performance Evaluation Of the Proposed Approachmentioning

confidence: 99%

Compromised user credentials detection in a digital enterprise using behavioral analytics

Shah

Amin

et al. 2019

Future Generation Computer Systems

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In today's digital age, the digital transformation is necessary for almost every competitive enterprise in terms of having access to the best resources and ensuring customer satisfaction. However, due to such rewards, these enterprises are facing key concerns around the risk of nextgeneration data security or cybercrime which is continually increasing issue due to the digital transformation four essential pillars-cloud computing, big data analytics, social and mobile computing. Data transformation-driven enterprises should ready to handle this next-generation data security problem, in particular, the compromised user credential (CUC). When an intruder or cybercriminal develops trust relationships as a legitimate account holder and then gain privileged access to the system for misuse. Many state-of-the-art risk mitigation tools are being developed, such as encrypted and secure password policy, authentication, and authorization mechanism. However, the CUC has become more complex and increasingly critical to the digital transformation process of enterprise's database by a cybercriminal, we propose a novel technique that effectively detects CUC at the enterprise-level. The proposed technique is learning from the user's behavior and builds a knowledge base system (KBS) which observe changes in the user's operational behavior. For that reason, a series of experiments were carried out on the dataset that collected from a sensitive database. All empirical results are validated through well-known evaluation measures, such as (i) accuracy, (ii) sensitivity, (iii) specificity, (iv) prudence accuracy, (v) precision, (vi) f-measure, and (vii) error rate. The experiments show that the proposed approach obtained weighted accuracy up to 99% and overall error of about 1%. The results clearly demonstrate that the proposed model efficiently can detect CUC which may keep an organization safe from major damage in data through cyber-attacks.

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