A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks

Khan, Izhar Ahmed; Pi, Dechang; Khan, Nasrullah; Khan, Zaheer; Hussain, Yasir; Nawaz, Asif; Ali, Farman

doi:10.1007/s10489-021-02222-8

Cited by 48 publications

(7 citation statements)

References 43 publications

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“…After determining the abnormal characteristics of malicious data in wireless personal communication, the Mahalanobis distance is used to determine the similarity between the features of malicious intrusion data, which provides more powerful evidence for its mining [15]. The Markov distance is normalized to the data features in the form of covariance matrix to eliminate the interference between malicious intrusion data [16].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Ji¹

2021

Wireless Communications and Mobile Computing

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Wireless personal communication network is easily affected by intrusion data in the communication process, resulting in the inability to ensure the security of personal information in wireless communication. Therefore, this paper proposes a malicious intrusion data mining algorithm based on legitimate big data in wireless personal communication networks. The clustering algorithm is used to iteratively obtain the central point of malicious intrusion data and determine its expected membership. The noise in malicious intrusion data is denoised by objective function, and the membership degree of communication data is calculated. The change factor of the neighborhood center of gravity of malicious intrusion data in wireless personal communication network is determined, the similarity between the characteristics of malicious intrusion data by using the Markov distance was determined, and the malicious intrusion data mining of wireless personal communication network supported by legal big data was completed. The experimental results show that the accuracy of mining malicious data is high and the mining time is short.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

Ji¹

2021

Wireless Communications and Mobile Computing

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“…Among these, ERT achieved the best performance. A 10-fold CV was widely used for solving biological problems like phage virion proteins classification, lysine 2-hydroxyisobutyrylation identification, identification of cancer-lectins and extracellular matrix proteins, , and detection of membrane protein types. , The binary classification was performed using a confusion matrix (CM). Afterward, five performance evaluation parameters are used: accuracy (Acc), sensitivity (Sn), specificity (Sp), Mathew’s correlation coefficient (MCC), and F-measure. , These parameters are calculated as follows: Acc = TP + TN TP + FP + TN + FN Sn = TP TP + FN Sp nobreak0em.25em⁡ = TN FP + TN MCC = false( TN × TP false) − false( FN × FP ) false( TP + FN false) false( TP + FP false) false( TN + FN false) false( TN + FP false) F‐measure = 2 × Precision × Recall Precision + Recall in which Precision = TP TP + …”

Section: Methodsmentioning

confidence: 99%

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

Khan

Uddin

Ali

et al. 2023

J. Chem. Inf. Model.

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The development of intracellular ice in the bodies of cold-blooded living organisms may cause them to die. These species yield antifreeze proteins (AFPs) to live in subzero temperature environments. Additionally, AFPs are implemented in biotechnological, industrial, agricultural, and medical fields. Machine learning-based predictors were presented for AFP identification. However, more accurate predictors are still highly desirable for boosting the AFP prediction. This work presents a novel approach, named AFP-SPTS, for the correct prediction of AFPs. We explored the discriminative features with four schemes, namely, dipeptide deviation from the expected mean (DDE), reduced amino acid alphabet (RAAA), grouped dipeptide composition (GDPC), and a novel representative method, called pseudo-position-specific scoring matrix tri-slicing (PseTS-PSSM). Considering the advantages of ensemble learning strategy, we fused each feature vector into different combinations and trained the models with five machine learning algorithms, i.e., multilayer perceptron (MLP), extremely randomized tree (ERT), decision tree (DT), random forest (RF), and AdaBoost. Among all models, PseTS-PSSM + RAAA with an extremely randomized tree attained the best outcomes. The proposed predictor (AFP-SPTS) boosted the accuracies of AFPs in the literature by 1.82 and 4.1%.

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“…After designing a computational predictor, the performance is evaluated using different validation methods. The most employed validation schemes are jackknife and k-fold cross-validations 31 – 37 . However, jackknife approach has high cost and computational time 38 – 44 .…”

Section: Methodsmentioning

confidence: 99%

XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set

Sikander

Ghulam

Ali

2022

Sci Rep

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Accurate identification of drug-targets in human body has great significance for designing novel drugs. Compared with traditional experimental methods, prediction of drug-targets via machine learning algorithms has enhanced the attention of many researchers due to fast and accurate prediction. In this study, we propose a machine learning-based method, namely XGB-DrugPred for accurate prediction of druggable proteins. The features from primary protein sequences are extracted by group dipeptide composition, reduced amino acid alphabet, and novel encoder pseudo amino acid composition segmentation. To select the best feature set, eXtreme Gradient Boosting-recursive feature elimination is implemented. The best feature set is provided to eXtreme Gradient Boosting (XGB), Random Forest, and Extremely Randomized Tree classifiers for model training and prediction. The performance of these classifiers is evaluated by tenfold cross-validation. The empirical results show that XGB-based predictor achieves the best results compared with other classifiers and existing methods in the literature.

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A privacy-conserving framework based intrusion detection method for detecting and recognizing malicious behaviours in cyber-physical power networks

Cited by 48 publications

References 43 publications

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

[Retracted] Malicious Intrusion Data Mining Algorithm of Wireless Personal Communication Network Supported by Legal Big Data

AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree

XGB-DrugPred: computational prediction of druggable proteins using eXtreme gradient boosting and optimized features set

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