Emerging data-driven technologies and big data analytics generate and deal with high-dimensional data. Transformation of such data into a low-dimensional feature space brings about numerous benefits, such as a more discriminant feature space, performance enhancement, less computational burden, and facilitating data visualization. This paper proposes a novel dimensionality reduction algorithm based on generative adversarial networks to tackle the issues related to high-dimensional data and common challenges in dimensionality reduction. To this aim, two constraints are defined to preserve the characteristics of the original data while rectifying the data distribution upon transformation. Formulating the transformation as sequential projections, the proposed Constrained Adversarial Dimensionality Reduction (CADR) method finds a set of sequential projection vectors that lead to a feature space in which between-class separability and within-class integrity are satisfied. This is while the transformed data perfectly comply with the pairwise affinity correlation in the original feature space. To evaluate the proposed method, nine advanced dimensionality reduction techniques are employed to enable a comparative study. The experiments are performed on several real-world benchmark datasets in terms of classification accuracy, F-measure, and G-mean. The obtained results show that the CADR could yield classification performance at a satisfactory level and outperforms the other competitors.

There has been an increasing interest in the use of data-driven techniques for classifying cyber-attacks and physical faults in cyber-physical systems. In real-world applications, the number of cyber-attack and faulty samples is usually far less than normal samples. This causes the skewed class distribution in data collected from cyber-physical systems. Training an accurate predictive model under skewed class conditions is not an easy task. In this work, we introduce a new generative adversarial framework for learning from skewed class distributions. This novel Adversarial Class-Imbalance Learning (ACIL) scheme has a novel loss function that is used during the adversarial training session. ACIL tries to iteratively adjust weights of an auxiliary multilayer perceptron to learn the minority class (i.e., cyber-attacks and physical faults) distributions along with the majority class (i.e., normal) distribution. Moreover, we devise an inclusive data-driven scheme for classifying cyber-attacks and faults, which includes four experiments of a baseline, nine state-of-the-art class-imbalance learning methods, two different generative-adversarial network-based approaches, and ACIL. These techniques are verified and compared through several experimental cyber-physical power scenarios. The obtained results show the effectiveness of ACIL for classifying samples of cyber-attacks and faults with skewed class distributions.

The performance of intrusion classification systems is often hampered by the presence of missing values in data collected from cyber-physical systems. Therefore, it is of paramount importance to robustly handle such missing scores, which in turn enhances the efficiency of intrusion classification task, and, consequently, the cybersecurity of cyber-physical systems. To this aim, this paper studies the efficacy of missing data imputation techniques for safeguarding intrusion classification systems against missing scores. To do this, a hybrid intrusion classification system is designed that comprises several advanced imputation techniques. To evaluate this intrusion classification framework, various incomplete scenarios have been simulated from data collected from a cyber-physical water system. In total, forty-four incomplete scenarios are considered throughout the experiments. The evaluation is conducted based on the classification accuracy and F-measure, as well as the root mean square error of the imputed data. The experimental results indicate the efficiency of the proposed intrusion classification system and find the best match missing data imputation technique for the sake of intrusion classification.

Correct classification of rare samples is a vital data mining task and of paramount importance in many research domains. This article mainly focuses on the development of the novel class-imbalance learning techniques, which make use of oversampling methods integrated with bagging and boosting ensembles. Two novel oversampling strategies based on the single and the multiple imputation methods are proposed. The proposed techniques aim to create useful synthetic minority class samples, similar to the original minority class samples, by estimation of missing values that are already induced in the minority class samples. The re-balanced datasets are then used to train base-learners of the ensemble algorithms. In addition, the proposed techniques are compared with the commonly used class imbalance learning methods in terms of three performance metrics including AUC, F-measure, and G-mean over several synthetic binary class datasets. The empirical results show that the proposed multiple imputation-based oversampling combined with bagging significantly outperforms other competitors.

There has been a growing interest in using smart grids due to their capability in delivering automated and distributed energy level to the consumption units. However, in order to guarantee the safe and reliable delivery of the high-quality power from the generation units to the consumers, smart grids need to be equipped with diagnostic systems. This paper presents an efficient data-driven scheme for diagnosing faults in smart grids. In order to reduce the computational burden and monitor the state of the system with a lower number of smart meters, a method based on the affinity propagation clustering algorithm is suggested for the placement of meters, that makes use of the graph-based representation of the system. The collected voltage data measurements from the installed meters are then decomposed by matching pursuit decomposition in order to generate informative features. Extracted features are then used to train a convolutional neural network, and the constructed deep learning model is then tested using unseen samples of normal and faulty conditions. Simulation results based on the IEEE 39-Bus System demonstrate the effectiveness of the proposed data-driven fault diagnostic system.

Design of the data-driven diagnostic systems usually requires to have labeled data during the training session. This paper aims to design a hybrid data-driven framework for diagnosing faults, where the data labels are not available to a large extent. This hybrid framework has five steps for transforming raw vibration signals to informative sets of samples for decision making. It uses several state-of-the-art approaches for feature extraction and semi-supervised feature reduction. The decision-making step uses a number of state-of-the-art semi-supervised learners. This step also comprises a novel surface estimation approach that is developed for SSL. The proposed hybrid framework is applied for diagnosing bearing defects in induction motors and validated based on four scenarios, each of which is experimented with different amounts of labeled samples. The attained diagnostic accuracies show the efficiency of the proposed hybrid framework, including the novel semi-supervised learner in classifying bearing defects, regardless of the number of labeled samples.

Efficient diagnosis of bearing defects in induction motors usually requires extracting informative features from the vibration signal and efficiently reducing the dimensionality of the features. In this paper, the vibration signal is primarily analyzed by the empirical mode decomposition technique to extract informative intrinsic mode functions as a set of features. The dimensionality of the extracted feature set is reduced by means of maximally collapsing metric learning (MCML) to create an informative set of small-sized features for fault classification. MCML is an efficient supervised dimensionality reduction technique which aims to collapse patterns of the similar class to a point in the feature space while separates patterns of other classes to the maximum extent possible. To compare the performance of MCML, other state-of-the-art unsupervised and supervised techniques are used for the dimension reduction of the features. The fault diagnosis unit includes various classifiers which aim to diagnose multiple bearing defects that are ball, inner race and outer race defects of different diameters.

This paper focuses on the development of an integrated scheme for diagnosing bearing defects in induction motors, under the class-imbalanced condition. This scheme comprises of four main modules: segmentation, feature extraction, feature reduction, and fault classification. Various state-of-the-art techniques have been devised in the feature extraction and reduction modules to extract informative sets of features from a raw vibration signal, filter redundant features, and produce the most distinct features for the following module. The fault classification module adapts various state-of-the-art class-imbalanced learning techniques for diagnosing bearing defects. This module contains a novel imputation-based oversampling technique for class-imbalanced learning. This integrated diagnostic scheme is evaluated on three experimental scenarios with different imbalance ratios. The reasonable diagnostic performances confirm the ability of the proposed novel class-imbalanced learning technique in diagnosing bearing defects, independently from the imbalance ratios.

This paper presents a model to extract and select a proper set of features for diagnosing bearing defects in induction motors. An efficient pre-processing of the vibration signals is of paramount importance to provide informative features for the fault classification module. The vibration signals are firstly analyzed by the wavelet packet transform to extract informative frequency domain features. The dimension of the set of extracted features is reduced by resorting to linear discriminant analysis to provide a small-size set of informative features for decision making. The fault classification module contains different classifiers that can learn the features-faults relations and classify multiple bearing defects including ball, inner race and outer race defects of different diameters. Experimental results verify the effectiveness of the proposed technique for diagnosing multiple bearing defects in induction motors.

In this paper, a supervised fuzzy-neighborhood density-based clustering approach is proposed for the fault diagnosis of induction motors' bearings. The proposed approach makes use of the labeled data regarding the actual classes of faulty and fault-free cases, in order to train the fuzzy-neighborhood density-based clustering algorithm in a supervised manner, by resorting to an invasive weed optimization algorithm that aims to minimize an error-based objective function. The proposed classifier can properly classify multi-class data with complex and variously shaped decision boundaries among the different classes of faults and the fault-free state, and is robust against noise. This is due mainly to the fact that the classifier is constructed using the fuzzy-neighborhood density based clustering method, which is not sensitive to the geometrical shape of clusters in the feature space.

One of the most important issue for secure communication in wireless networks is generating a common key for many participants. Analysis of Tseng's protocol (Y.-M. TSENG, T.-Y. WU, INFORMATICA 2010, 21, 2, 247-259) is done.

Wireless networks need more security in comparison to the other networks due to their intrinsic vulnerabilities to possible attacks. It is expected that by using a distributed method for true random number generators (TRNG) in wireless sensor networks (WSN) and wireless LAN (WLAN) randomness quality of generated numbers can be enhanced. We analyze a protocol for a distributed TRNG named ScatterLight (L. R. Giuseppe, M. Fabrizio and O. Marco, 2011) for a WSN. After making some changes on ScatterLight structure and physical data sources, the Enhanced ScatterLight protocol is introduced; it provides secure and high qualified true random numbers. Thus, the quality of randomness of obtained random numbers using National Institute of Standards and Technology tests is evaluated. Finally, by analyzing the results, it can be conducted that Enhanced ScatterLight protocol in comparisons with ScatterLight protocol in WLAN and LAN provides 60% and 53% better randomness quality respectively, while the performance is equal in both protocols.