Biblio

In view of the characteristics that rolling bearing is prone to failure under actual working conditions, and it is difficult to classify the fault category and fault degree, the deep belief network is introduced to diagnose the rolling bearing fault. Firstly, principal component analysis is used to reduce the dimension of original input data and delete redundant input information. Then, the dimension reduced data are input into the deep belief network to extract the low dimensional fault feature representation, and the extracted features are input into the classifier for rolling bearing fault pattern recognition. Finally, the diagnosis effect of the proposed network is compared with the existing common shallow neural network. The simulation experiment is carried out through the bearing data in the United States.

Self-adaptive systems commonly operate in heterogeneous contexts and need to consider multiple quality attributes. Human stakeholders often express their quality preferences by defining utility functions, which are used by self-adaptive systems to automatically generate adaptation plans. However, the adaptation space of realistic systems is large and it is obscure how utility functions impact the generated adaptation behavior, as well as structural, behavioral, and quality constraints. Moreover, human stakeholders are often not aware of the underlying tradeoffs between quality attributes. To address this issue, we present an approach that uses machine learning techniques (dimensionality reduction, clustering, and decision tree learning) to explain the reasoning behind automated planning. Our approach focuses on the tradeoffs between quality attributes and how the choice of weights in utility functions results in different plans being generated. We help humans understand quality attribute tradeoffs, identify key decisions in adaptation behavior, and explore how differences in utility functions result in different adaptation alternatives. We present two systems to demonstrate the approach’s applicability and consider its potential application to 24 exemplar self-adaptive systems. Moreover, we describe our assessment of the tradeoff between the information reduction and the amount of explained variance retained by the results obtained with our approach.

In the multivariable fault diagnosis of industrial process, due to the existence of correlation between variables, the result of fault diagnosis will inevitably appear "smearing" effect. Although the fault diagnosis method based on the contribution of multi-dimensional reconstruction is helpful when multiple faults occur. But in order to correctly isolate all the fault variables, this method will become very inefficient due to the combination of variables. In this paper, a fault diagnosis method based on kNN and MRBC is proposed to fundamentally avoid the corresponding influence of "smearing", and a fast variable selection strategy is designed to accelerate the process of fault isolation. Finally, simulation study on a benchmark process verifies the effectiveness of the method, in comparison with the traditional method represented by FDA-based method.

The accurate classification of WiFi-based activity patterns is still an open problem and is critical to detect behavior for non-visualization applications. This paper proposes a novel approach that uses WiFi-based IQ data and short-time Fourier transform (STFT) time-frequency images to automatically and accurately classify human activities. The offsets features, calculated from time-domain values and one-dimensional principal component analysis (1D-PCA) values and two-dimensional principal component analysis (2D-PCA) values, are applied as features to input the classifiers. The machine learning methods such as the bagging, boosting, support vector machine (SVM), random forests (RF) as the classifier to output the performance. The experimental data validate our proposed method with 15000 experimental samples from five categories of WiFi signals (empty, marching on the spot, rope skipping, both arms rotating;singlearm rotating). The results show that the method companying with the RF classifier surpasses the approach with alternative classifiers on classification performance and finally obtains a 62.66% classification rate, 85.06% mean accuracy, and 90.67% mean specificity.

This paper presents Zero-Day Attack Packet Highlighting System. Proposed system outputs zero-day attack packet information from flow extracted as result of regression inspection of packets stored in flow-based PCA. It also highlights raw data of the packet matched with rule. Also, we design communication protocols for sending and receiving data within proposed system. Purpose of the proposed system is to solve existing flow-based problems and provides users with raw data information of zero-day packets so that they can analyze raw data for the packets.

Smart home is one application of the pervasive computing branch of science. Three categories of smart homes, namely comfort, healthcare, and security. The security system is a part of smart home technology that is very important because the intensity of crime is increasing, especially in residential areas. The system will detect the face by the webcam camera if the user enters the correct password. Face recognition will be processed by the Raspberry pi 3 microcontroller with the Principal Component Analysis method using OpenCV and Python software which has outputs, namely actuators in the form of a solenoid lock door and buzzer. The test results show that the webcam can perform face detection when the password input is successful, then the buzzer actuator can turn on when the database does not match the data taken by the webcam or the test data and the solenoid door lock actuator can run if the database matches the test data taken by the sensor. webcam. The mean response time of face detection is 1.35 seconds.

Image watermarking is very important phenomenon in modern society where intellectual property right of information is necessary. Considering this impending problem, there are many image watermarking methods exist in the literature each of having some key advantages and disadvantages. After summarising state-of-the-art literature survey, an optimum digital watermark technique using singular value decomposition with principle component analysis (PCA) is proposed and verified. Basically, the host image is compressed using PCA which reduces multi-dimensional data to effective low-dimensional information. In this scheme, the watermark is embedded using the discrete wavelet transformation-singular value decomposition approach. Simulation results show that the proposed method improves the system performance compared with the existing method in terms of the watermark embedding, and extraction time. Therefore, this work is valuable for image watermarking in modern life such as tracing copyright infringements and banknote authentication.

Today, everyone is highly dependent on the internet. Everyone performed online shopping and online activities such as online Bank, online booking, online recharge and more on internet. Phishing is a type of website threat and phishing is Illegally on the original website Information such as login id, password and information of credit card. This paper proposed an efficient machine learning based phishing detection technique. Overall, experimental results show that the proposed technique, when integrated with the Support vector machine classifier, has the best performance of accurately distinguishing 95.66% of phishing and appropriate websites using only 22.5% of the innovative functionality. The proposed technique exhibits optimistic results when benchmarking with a range of standard phishing datasets of the “University of California Irvine (UCI)” archive. Therefore, proposed technique is preferred and used for phishing detection based on machine learning.

False data injection attack (FDIA) is a real threat to smart grids due to its wide range of vulnerabilities and impacts. Designing a proper detection scheme for FDIA is the 1stcritical step in defending the attack in smart grids. In this paper, we investigate two main statistical techniques-based approaches in this regard. The first is based on the principal component analysis (PCA), and the second is based on the canonical correlation analysis (CCA). The test cases illustrate a better characterization performance of FDIA using CCA compared to the PCA. Further, CCA provides a better differentiation of FDIA from normal grid contingencies. On the other hand, PCA provides a significantly reduced false alarm rate.

The purpose of any data analysis is to extract essential information implicitly present in the data. To do this, it often seems necessary to transform the initial data into a form that allows one to identify and interpret the essential features of their structure. One of the most important tasks of data analysis is to reduce the dimension of the original data. The paper considers an approach to solving this problem based on singular value decomposition (SVD).

With the advent of networking technologies and increasing network attacks, Intrusion Detection systems are apparently needed to stop attacks and malicious activities. Various frameworks and techniques have been developed to solve the problem of intrusion detection, still there is need for new frameworks as per the challenging scenario of enormous scale in data size and nature of attacks. Current IDS systems pose challenges on the throughput to work with high speed networks. In this paper we address the issue of high computational overhead of anomaly based IDS and propose the solution using discretization as a data preprocessing step which can drastically reduce the computation overhead. We propose method to provide near real time detection of attacks using only basic flow level features that can easily be extracted from network packets.

The detection of malicious HTTP(S) requests is a pressing concern in cyber security, in particular given the proliferation of HTTP-based (micro-)service architectures. In addition to rule-based systems for known attacks, anomaly detection has been shown to be a promising approach for unknown (zero-day) attacks. This article extends existing work by integrating outlier explanations for individual requests into an end-to-end pipeline. These end-to-end explanations reflect the internal working of the pipeline. Empirically, we show that found explanations coincide with manually labelled explanations for identified outliers, allowing security professionals to quickly identify and understand malicious requests.

The controller is an indispensable entity in software-defined networking (SDN), as it maintains a global view of the underlying network. However, if the controller fails to respond to the network due to a distributed denial of service (DDoS) attacks. Then, the attacker takes charge of the whole network via launching a spoof controller and can also modify the flow tables. Hence, faster, and accurate detection of DDoS attacks against the controller will make the SDN reliable and secure. Moreover, the Internet traffic is drastically increasing due to unprecedented growth of connected devices. Consequently, the processing of large number of requests cause a performance bottleneck regarding SDN controller. In this paper, we propose a hierarchical control plane SDN architecture for multi-domain communication that uses a statistical method called principal component analysis (PCA) to reduce the dimensionality of the big data traffic and the support vector machine (SVM) classifier is employed to detect a DDoS attack. SVM has high accuracy and less false positive rate while the PCA filters attribute drastically. Consequently, the performance of classification and accuracy is improved while the false positive rate is reduced.

Among the different types of malware, botnets are rising as the most genuine risk against cybersecurity as they give a stage to criminal operations (e.g., Distributed Denial of Service (DDOS) attacks, malware dispersal, phishing, and click fraud and identity theft). Existing botnet detection techniques work only on specific botnet Command and Control (C&C) protocols and lack in providing early-stage botnet detection. In this paper, we propose an approach for early-stage botnet detection. The proposed approach first selects the optimal features using feature selection techniques. Next, it feeds these features to machine learning classifiers to evaluate the performance of the botnet detection. Experiments reveals that the proposed approach efficiently classifies normal and malicious traffic at an early stage. The proposed approach achieves the accuracy of 99%, True Positive Rate (TPR) of 0.99 %, and False Positive Rate (FPR) of 0.007 % and provide an efficient detection rate in comparison with the existing approach.

Nowadays, smartphones are everywhere. They play an indispensable role in our lives and makes people convenient to communicate, pay, socialize, etc. However, they also bring a lot of security and privacy risks. Keystroke operations of numeric keypad are often required when users input password to perform mobile payment or input other privacy-sensitive information. Different keystrokes may cause different finger movements that will bring different interference to WiFi signal, which may be reflected by channel state information (CSI). In this paper, we propose WiPass, a password-keystroke recognition system for numerical keypad input on smartphones, which especially occurs frequently in mobile payment APPs. Based on only a public WiFi hotspot deployed in the victim payment scenario, WiPass would extracts and analyzes the CSI data generated by the password-keystroke operation of the smartphone user, and infers the user's payment password by comparing the CSI waveforms of different keystrokes. We implemented the WiPass system by using COTS WiFi AP devices and smartphones. The average keystroke segmentation accuracy was 80.45%, and the average keystroke recognition accuracy was 74.24%.

Partitional Clustering Algorithm (PCA) on the Hadoop Distributed File System is to perform big data securities using the Perturbation Technique is the main idea of the proposed work. There are numerous clustering methods available that are used to categorize the information from the big data. PCA discovers the cluster based on the initial partition of the data. In this approach, it is possible to develop a security safeguarding of data that is impoverished to allow the calculations and communication. The performances were analyzed on Health Care database under the studies of various parameters like precision, accuracy, and F-score measure. The outcome of the results is to demonstrate that this method is used to decrease the complication in preserving privacy and better accuracy than that of the existing techniques.

A main issue in approximate nearest neighbor search is to achieve an excellent tradeoff between search accuracy and computation cost. In this paper, we address this issue by leveraging k-nearest neighbor graph and hill-climbing to accelerate vector quantization in the query assignment process. A modified hill-climbing algorithm is proposed to traverse k-nearest neighbor graph to find closest centroids for a query, rather than calculating the query distances to all centroids. Instead of using random seeds in the original hill-climbing algorithm, we generate high-quality seeds based on the hashing technique. It can boost the query assignment efficiency due to a better start-up in hill-climbing. We evaluate the experiment on the benchmarks of SIFT1M and GIST1M datasets, and show the proposed hashing-based seed generation effectively improves the search performance.

The last decade has witnessed an increase in online human interactions, covering all aspects of personal and professional activities. Identification of people based on their behavior rather than physical traits is a growing industry, spanning diverse spheres such as online education, e-commerce and cyber security. One prominent behavior is the expression of opinions, commonly as a reaction to images posted online. Visual aesthetic is a soft, behavioral biometric that refers to a person's sense of fondness to a certain image. Identifying individuals using their visual aesthetics as discriminatory features is an emerging domain of research. This paper introduces a new method for aesthetic feature dimensionality reduction using gene expression programming. The advantage of this method is that the resulting system is capable of using a tree-based genetic approach for feature recombination. Reducing feature dimensionality improves classifier accuracy, reduces computation runtime, and minimizes required storage. The results obtained on a dataset of 200 Flickr users evaluating 40000 images demonstrates a 94% accuracy of identity recognition based solely on users' aesthetic preferences. This outperforms the best-known method by 13.5%.

Deep Learning is an area of Machine Learning research, which can be used to manipulate large amount of information in an intelligent way by using the functionality of computational intelligence. A deep learning system is a fully trainable system beginning from raw input to the final output of recognized objects. Feature selection is an important aspect of deep learning which can be applied for dimensionality reduction or attribute reduction and making the information more explicit and usable. Deep learning can build various learning models which can abstract unknown information by selecting a subset of relevant features. This property of deep learning makes it useful in analysis of highly complex information one which is present in intrusive data or information flowing with in a web system or a network which needs to be analyzed to detect anomalies. Our approach combines the intelligent ability of Deep Learning to build a smart Intrusion detection system.

Nowadays due to economic reasons most of the semiconductor companies prefer to outsource the manufacturing part of their designs to third fabrication foundries, the so-called fabs. Untrustworthy fabs can extract circuit blocks, the called intellectual properties (IPs), from the layouts and then pirate them. Such fabs are suspected of hardware Trojan (HT) threat in which malicious circuits are added to the layouts for sabotage objectives. HTs lead up to increase power consumption in HT-infected circuits. However, due to process variations, the power of HTs including few gates in million-gate circuits is not detectable in power consumption analysis (PCA). Thus, such circuits should be considered as a collection of small sub-circuits, and PCA must be individually performed for each one of them. In this article, we introduce an approach facilitating PCA-based HT detection methods. Concerning this approach, we propose a new logic locking method and algorithm. Logic locking methods and algorithm are usually employed against IP piracy. They modify circuits such that they do not correctly work without applying a correct key to. Our experiments at the gate level and post-synthesis show that the proposed locking method and algorithm increase the proportion of HT activity and consequently HT power to circuit power.

Sparse and low rank matrix decomposition is a method that has recently been developed for estimating different components of hyperspectral data. The rank component is capable of preserving global data structures of data, while a sparse component can select the discriminative information by preserving details. In order to take advantage of both, we present a novel decision fusion based on joint low rank and sparse component (DFJLRS) method for hyperspectral imagery in this paper. First, we analyzed the effects of different components on classification results. Then a novel method adopts a decision fusion strategy which combines a SVM classifier with the information provided by joint sparse and low rank components. With combination of the advantages, the proposed method is both representative and discriminative. The proposed algorithm is evaluated using several hyperspectral images when compared with traditional counterparts.

Designing a machine learning based network intrusion detection system (IDS) with high-dimensional features can lead to prolonged classification processes. This is while low-dimensional features can reduce these processes. Moreover, classification of network traffic with imbalanced class distributions has posed a significant drawback on the performance attainable by most well-known classifiers. With the presence of imbalanced data, the known metrics may fail to provide adequate information about the performance of the classifier. This study first uses Principal Component Analysis (PCA) as a feature dimensionality reduction approach. The resulting low-dimensional features are then used to build various classifiers such as Random Forest (RF), Bayesian Network, Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA) for designing an IDS. The experimental findings with low-dimensional features in binary and multi-class classification show better performance in terms of Detection Rate (DR), F-Measure, False Alarm Rate (FAR), and Accuracy. Furthermore, in this paper, we apply a Multi-Class Combined performance metric Combi ned Mc with respect to class distribution through incorporating FAR, DR, Accuracy, and class distribution parameters. In addition, we developed a uniform distribution based balancing approach to handle the imbalanced distribution of the minority class instances in the CICIDS2017 network intrusion dataset. We were able to reduce the CICIDS2017 dataset's feature dimensions from 81 to 10 using PCA, while maintaining a high accuracy of 99.6% in multi-class and binary classification.

False data injection is an on-going concern facing power system state estimation. In this work, a neural network is trained to detect the existence of false data in measurements. The proposed approach can make use of historical data, if available, by using them in the training sets of the proposed neural network model. However, the inputs of perceptron model in this work are the residual elements from the state estimation, which are highly correlated. Therefore, their dimension could be reduced by preserving the most informative features from the inputs. To this end, principal component analysis is used (i.e., a data preprocessing technique). This technique is especially efficient for highly correlated data sets, which is the case in power system measurements. The results of different perceptron models that are proposed for detection, are compared to a simple perceptron that produces identical result to the outlier detection scheme. For generating the training sets, state estimation was run for different false data on different measurements in 13-bus IEEE test system, and the residuals are saved as inputs of training sets. The testing results of the trained network show its good performance in detection of false data in measurements.

Localizing concurrency faults that occur in production is hard because, (1) detailed field data, such as user input, file content and interleaving schedule, may not be available to developers to reproduce the failure; (2) it is often impractical to assume the availability of multiple failing executions to localize the faults using existing techniques; (3) it is challenging to search for buggy locations in an application given limited runtime data; and, (4) concurrency failures at the system level often involve multiple processes or event handlers (e.g., software signals), which can not be handled by existing tools for diagnosing intra-process(thread-level) failures. To address these problems, we present SCMiner, a practical online bug diagnosis tool to help developers understand how a system-level concurrency fault happens based on the logs collected by the default system audit tools. SCMiner achieves online bug diagnosis to obviate the need for offline bug reproduction. SCMiner does not require code instrumentation on the production system or rely on the assumption of the availability of multiple failing executions. Specifically, after the system call traces are collected, SCMiner uses data mining and statistical anomaly detection techniques to identify the failure-inducing system call sequences. It then maps each abnormal sequence to specific application functions. We have conducted an empirical study on 19 real-world benchmarks. The results show that SCMiner is both effective and efficient at localizing system-level concurrency faults.

Profiled DPA is a new method combined with machine learning method in side channel attack which is put forward by Whitnall in CHES 2015.[1]The most important part lies in effectiveness of extracting information. This paper introduces a new rule Explained Local Variance (ELV) to extract information in profiled stage for profiled DPA. It attracts information effectively and shields noise to get better accuracy than the original rule. The ELV enables an attacker to use less power traces to get the same result as before. It also leads to 94.6% space reduction and 29.2% time reduction for calculation. For security circuit implementation, a high order masking scheme in modelsim is implemented. A new exchange network is put forward. 96.9% hardware resource is saved due to the usage of this network.