Showing papers in "Cluster Computing in 2018"

Privacy-preserving outsourced classification in cloud computing

Abstract: Classifier has been widely applied in machine learning, such as pattern recognition, medical diagnosis, credit scoring, banking and weather prediction. Because of the limited local storage at user side, data and classifier has to be outsourced to cloud for storing and computing. However, due to privacy concerns, it is important to preserve the confidentiality of data and classifier in cloud computing because the cloud servers are usually untrusted. In this work, we propose a framework for privacy-preserving outsourced classification in cloud computing (POCC). Using POCC, an evaluator can securely train a classification model over the data encrypted with different public keys, which are outsourced from the multiple data providers. We prove that our scheme is secure in the semi-honest model

Wearable sensor devices for early detection of Alzheimer disease using dynamic time warping algorithm

Abstract: Alzheimer disease is a significant problem in public health. Alzheimer disease causes severe problems with thinking, memory and activities. Alzheimer disease affected more on the people who are in the age group of 80-year-90. The foot movement monitoring system is used to detect the early stage of Alzheimer disease. internets of things (IoT) devices are used in this paper to monitor the patients’ foot movement in continuous manner. This paper uses dynamic time warping (DTW) algorithm to compare the various shapes of foot movements collected from the wearable IoT devices. The foot movements of the normal individuals and people who are affected by Alzheimer disease are compared with the help of middle level cross identification (MidCross) function. The identified cross levels are used to classify the gait signal for Alzheimer disease diagnosis. Sensitivity and specificity are calculated to evaluate the DTW algorithm based classification model for Alzheimer disease. The classification results generated using the DTW is compared with the various classification algorithms such as inertial navigation algorithm, K-nearest neighbor classifier and support vector machines. The experimental results proved the effectiveness of the DTW method.

Construction of employee training program evaluation system of three exponential forecast based on sliding window

Abstract: One construction method for employee training program evaluation system of three exponential forecast based on sliding window has been proposed to improve the effectiveness of construction process for employee training program evaluation system. Firstly, construct employee training program evaluation forecast model with gray model and set up regression forecast objective function; secondly, construct sliding window by setting segmentation points and make real-time segmentation for gray model data by combining three smoothness exponential algorithm, attain real-time statistical feature of data, construct the function relation between sequence error forecast and compression ratio, make use of error forecast sequence to make segmentation point judgment and improve robustness of test. Lastly, the effectiveness of the algorithm has been verified through simulation experiment.

CHOPPER: an intelligent QoS-aware autonomic resource management approach for cloud computing

Abstract: Cloud computing is the future generation of computational services delivered over the Internet. As cloud infrastructure expands, resource management in such a large heterogeneous and distributed environment is a challenging task. In a cloud environment, uncertainty and dispersion of resources encounters problems of allocation of resources. Unfortunately, existing resource management techniques, frameworks and mechanisms are insufficient to handle these environments, applications and resource behaviors. To provide an efficient performance and to execute workloads, there is a need of quality of service (QoS) based autonomic resource management approach which manages resources automatically and provides reliable, secure and cost efficient cloud services. In this paper, we present an intelligent QoS-aware autonomic resource management approach named as CHOPPER (Configuring, Healing, Optimizing and Protecting Policy for Efficient Resource management). CHOPPER offers self-configuration of applications and resources, self-healing by handling sudden failures, self-protection against security attacks and self-optimization for maximum resource utilization. We have evaluated the performance of the proposed approach in a real cloud environment and the experimental results show that the proposed approach performs better in terms of cost, execution time, SLA violation, resource contention and also provides security against attacks.

A Gaussian process based big data processing framework in cluster computing environment

Abstract: Machine learning algorithms play a vital role in the prediction of an outbreak of diseases based on climate change. Dengue outbreak is caused by improper maintenance of water storages, lack of urbanization, deforestation, lack of vaccination and awareness. Moreover, a number of dengue cases are varying based on climate season. There is a need to develop the prediction model for modeling the dengue outbreak based climate change. To model the dengue outbreak, Gaussian process regression (GPR) model is applied in this paper that uses the seasonal average of various climate parameters such as maximum temperature, minimum temperature, precipitation, wind, relative humidity and solar. The number of dengue cases and climate data for each block of Tamil Nadu, India are collected from Integrated Disease Surveillance Project and Global Weather Data for SWAT Inc respectively. Local Moran’s I spatial autocorrelation is used in this paper for geographical visualization of hotspot regions. The outbreak of dengue and its hot spot regions are geographically visualized with the help of ArcGIS 10.1 software. The day wise big climate data is collected and stored in the Hadoop cluster computing environment. MapReduce framework is used to reduce the day wise climate data into seasonal climate averages such as winter, summer, and monsoon. The seasonal climate data and number of dengue incidence (health data) are integrated based on the geo-location (latitude and longitude). GPR is used to develop the prediction model for dengue based on the integrated data (climate and health data). The proposed Gaussian process based prediction model is compared with various machine learning approaches such as multiple regression, support vector machine and random forests. Experimental results demonstrate the effectiveness of our Gaussian process based prediction framework.

A hybrid technique using binary particle swarm optimization and decision tree pruning for network intrusion detection

Abstract: A major drawback of signature-based intrusion detection systems is the inability to detect novel attacks that do not match the known signatures already stored in the database. Anomaly detection is a kind of intrusion detection in which the activities of a system are monitored and these activities are classified as normal or anomalous based on their expected behavior. Tree-based classifiers have been successfully used to separate the abnormal behavior from the normal one. Tree pruning is a machine learning technique used to minimize the size of a decision tree (DT) in order to reduce the complexity of the classifier and improve its predictive accuracy. In this paper, we attempt to prune a DT using particle swarm optimization (PSO) algorithm and apply it to the network intrusion detection problem. The proposed technique is a hybrid approach in which PSO is used for node pruning and the pruned DT is used for classification of the network intrusions. Both single and multi-objective PSO algorithms are used in the proposed approach. The experiments are carried out on the well-known KDD99Cup dataset. This dataset has been widely used as a benchmark dataset for network intrusion detection problems. The results of the proposed technique are compared to the other state-of-the-art classifiers and it is observed that the proposed technique performs better than the other classifiers in terms of intrusion detection rate, false positive rate, accuracy, and precision.

Support for spot virtual machine purchasing simulation

Abstract: With the rapid progress of cloud computing technology, a growing number of big data application providers begin to deploy applications on virtual machines rented from infrastructure as a service providers Current infrastructure as a service provider offers diverse purchasing options for the application providers There are mainly three types of purchasing options: reserved virtual machine, on-demand virtual machine and spot virtual machine The spot virtual machine is a specific type of virtual machine that employs a dynamic pricing model Because can be stopped by the infrastructure as a service providers without notice, the spot virtual machine is suitable for large-scale divisible applications, such as big data analysis Therefore, spot virtual machine is chosen by many big data application providers for its low rental cost per hour When spot virtual machine is chosen, a major issue faced by the big data application providers is how to minimize the virtual machine rental cost while meet service requirements Many optimal spot virtual machine purchasing approaches have been presented by the researchers However, there is a shortage of simulators that enable researchers to evaluate their newly proposed spot virtual machine purchasing approach To fill this gap, in this paper, we propose SpotCloudSim to support for dynamic virtual machine pricing model simulation SpotCloudSim provides an extensible interface to help researchers implement new spot virtual machine purchasing approach In addition, SpotCloudSim can also study the behavior of the newly proposed spot virtual machine purchasing approaches We demonstrate the capabilities of SpotCloudSim by using three spot virtual machine purchasing approaches The results indicate the benefits of our proposed simulation system

Mining biometric data to predict programmer expertise and task difficulty

Abstract: Programming mistakes frequently waste software developers’ time and may lead to the introduction of bugs into their software, causing serious risks for their customers. Using the correlation between various software process metrics and defects, earlier work has traditionally attempted to spot such bug risks. However, this study departs from previous works in examining a more direct method of using psycho-physiological sensors data to detect the difficulty of program comprehension tasks and programmer level of expertise. By conducting a study with 38 expert and novice programmers, we investigated how well an electroencephalography and an eye-tracker can be utilized in predicting programmer expertise (novice/expert) and task difficulty (easy/difficult). Using data from both sensors, we could predict task difficulty and programmer level of expertise with 64.9 and 97.7% precision and 68.6 and 96.4% recall, respectively. The result shows it is possible to predict the perceived difficulty of a task and expertise level for developers using psycho-physiological sensors data. In addition, we found that while using single biometric sensor shows good results, the composition of both sensors lead to the best overall performance.

Energy efficient job scheduling with workload prediction on cloud data center

Abstract: Data centers are the backbone of cloud infrastructure platform to support large-scale data processing and storage. More and more business-to-consumer and enterprise applications are based on cloud data center. However, the amount of data center energy consumption is inevitably lead to high operation costs. The aim of this paper is to comprehensive reduce energy consumption of cloud data center servers, network, and cooling systems. We first build an energy efficient cloud data center system including its architecture, job and power consumption model. Then, we combine the linear regression and wavelet neural network techniques into a prediction method, which we call MLWNN, to forecast the cloud data center short-term workload. Third, we propose a heuristic energy efficient job scheduling with workload prediction solution, which is divided into resource management strategy and online energy efficient job scheduling algorithm. Our extensive simulation performance evaluation results clearly demonstrate that our proposed solution has good performance and is very suitable for low workload cloud data center.

Energy efficient node selection algorithm based on node performance index and random waypoint mobility model in internet of vehicles

Abstract: Internet of vehicles (IoV) is an improved version of internet of things to resolve a number of issues in urban traffic environment. In this paper IoV technology is used to select the best ambulance based on a novel node selection algorithm. The proposed IoT healthcare monitoring system consists of number of mobile doctors, patient and mobile ambulance. Performance rank (PR) index is calculated for each mobile ambulance based on the medical capacity (b) of the mobile ambulance, the number of patients currently using the mobile ambulance (n), and the Euclidean distance from a neighboring mobile ambulance. The minimum PR index is considered as best ambulance to provide a service to the patient. Random waypoint mobility model is used to simulate the proposed IoT based healthcare monitoring system. The proposed energy efficient node selection algorithm is compared with various node selection algorithms such as cluster based routing protocol, workload-aware channel assignment algorithm and scenario-based clustering algorithm for performance evaluation. The packet delivery fraction, normalized routing load and average end-to-end delay are calculated to evaluate the performance of the proposed energy efficient node selection algorithm. We have used NS-2 simulator for the node simulation to show the performance of the energy efficient node selection framework. Experimental results prove that the efficiency of the proposed energy efficient node selection algorithm in IoT healthcare environment.

Exploring the innovation modes and evolution of the cloud-based service using the activity theory on the basis of big data

Abstract: With the cooperation between hospitals and advanced technologies such as mobile Internet, big data and cloud computing, emerging technologies and new service models have been applied in precaution, diagnosis, treatment and other medical services. As a result, people’s medical practices have been dramatically changed. On the basis of activity theory and service supply chain, the paper discusses hospital’s innovation in medical, health and care service by using cloud computing. It also explores the model and evolution of hospital’s internal activity and external supply chain.Cases in this paper from the year 2000 till now are our secondary data related to the cloud service innovation in hospitals. The study adopts textual analysis to interpret and analyze the data. Then, in-depth interviews would be taken on the basis of analytical result. Through the above-mentioned process, we find that homogeneities between different cases include treatment service cloud ranking first, community as the main promoter, substantial service innovation being emphasized, care service cloud as future trend, service innovation featured client-service model, eight procedures for service supply chain, improvement of suppliers’ self-design, and pluralism of medical care service etc. Differences of the cases include government policies versus industrial policies, multipoint distribution versus centralized organizational structure, regional institutionalization versus large-scale centralization, Substantive services application versus development of service value, market orientation versus industry orientation, service provider orientation versus service design orientation, physical customer versus invisible customer. In the end, the study offers relevant theories, management implications, and recommendations.

Intelligent traffic video surveillance and accident detection system with dynamic traffic signal control

Abstract: Enormous advance has proven throughout the years in the area of traffic surveillance by the growth of intelligent traffic video surveillance system. In the current work, through the traffic videos, the traffic video surveillance automatically keyed out the vehicles like ambulance and trucks, which in turn assisted us in directing the vehicles at the time of emergency. Nevertheless, it doesn’t provide us a vital solution for the regulating the traffic. Moreover, this idea just identifies the vehicles, but it couldn’t notice the accidents expeditiously. Therefore in the proposed work, expeditious traffic video surveillance and monitoring system are presented along with dynamic traffic signal control and accident detection mechanism. Hybrid median filter has been utilized at the beginning for pre-processing of traffic videos, and to remove the noise. Hybrid support vector machine (SVM with extended Kalman filter) has been utilized to chase the vehicles. Next, the histogram of flow gradient features are drew-out to categories the vehicles. According to the traffic density and through video files, vehicles are computed, and then for emergency vehicles, the traffic signal gets switched dynamically. To realize the arrival of ambulances, the cameras have been set to catch traffic videos minimum at 500 m of the signal and deep learning neural networks has been employed. Hence dynamic signal control has been incorporated expeditiously. Likewise, multinomial logistic regression has been utilized in real-time live streaming videos, to identify the accidents correctly. The observational solution shows that the proposed intelligent traffic video surveillance system render expeditious dynamic control of traffic signals and it raises the identification of accidents correctly.

A novel task scheduling approach based on dynamic queues and hybrid meta-heuristic algorithms for cloud computing environment

Abstract: Task scheduling is one of the most challenging aspects to improve the overall performance of cloud computing and optimize cloud utilization and Quality of Service (QoS). This paper focuses on Task Scheduling optimization using a novel approach based on Dynamic dispatch Queues (TSDQ) and hybrid meta-heuristic algorithms. We propose two hybrid meta-heuristic algorithms, the first one using Fuzzy Logic with Particle Swarm Optimization algorithm (TSDQ-FLPSO), the second one using Simulated Annealing with Particle Swarm Optimization algorithm (TSDQ-SAPSO). Several experiments have been carried out based on an open source simulator (CloudSim) using synthetic and real data sets from real systems. The experimental results demonstrate the effectiveness of the proposed approach and the optimal results is provided using TSDQ-FLPSO compared to TSDQ-SAPSO and other existing scheduling algorithms especially in a high dimensional problem. The TSDQ-FLPSO algorithm shows a great advantage in terms of waiting time, queue length, makespan, cost, resource utilization, degree of imbalance, and load balancing.

A control theoretical view of cloud elasticity: taxonomy, survey and challenges

Abstract: The lucrative features of cloud computing such as pay-as-you-go pricing model and dynamic resource provisioning (elasticity) attract clients to host their applications over the cloud to save up-front capital expenditure and to reduce the operational cost of the system. However, the efficient management of hired computational resources is a challenging task. Over the last decade, researchers and practitioners made use of various techniques to propose new methods to address cloud elasticity. Amongst many such techniques, control theory emerges as one of the popular methods to implement elasticity. A plethora of research has been undertaken on cloud elasticity including several review papers that summarise various aspects of elasticity. However, the scope of the existing review articles is broad and focused mostly on the high-level view of the overall research works rather than on the specific details of a particular implementation technique. While considering the importance, suitability and abundance of control theoretical approaches, this paper is a step forward towards a stand-alone review of control theoretic aspects of cloud elasticity. This paper provides a detailed taxonomy comprising of relevant attributes defining the following two perspectives, i.e., control-theory as an implementation technique as well as cloud elasticity as a target application domain. We carry out an exhaustive review of the literature by classifying the existing elasticity solutions using the attributes of control theoretic perspective. The summarized results are further presented by clustering them with respect to the type of control solutions, thus helping in comparison of the related control solutions. In last, a discussion summarizing the pros and cons of each type of control solutions are presented. This discussion is followed by the detail description of various open research challenges in the field.

Deep neural architectures for large scale android malware analysis

Abstract: Android is arguably the most widely used mobile operating system in the world Due to its widespead use and huge user base, it has attracted a lot of attention from the unsavory crowd of malware writers Traditionally, techniques to counter such malicious software involved manually analyzing code and figuring out whether it was malicious or benign However, due to the immense pace at which newer malware families are surfacing, such an approach is no longer feasible Machine learning offers a way to tackle this issue of speed by automating the classification task While several efforts have been made to use traditional machine learning techniques to Android malware detection, no reasonable effort has been made to utilize the newer, deep learning models in this domain In this paper, we apply several deep learning models including fully connected, convolutional and recurrent neural networks as well as autoencoders and deep belief networks to detect Android malware from a large scale dataset of more than 55 GBs of Android malware Further, we apply Bayesian machine learning to this problem domain to see how it fares with the deep learning based models while also providing insights into the dataset We show that we are able to achieve better results using these models as compared to the state-of-the-art approaches Our best model gets an F1 score of 0986 with an AUC of 0983 as compared to the existing best F1 score of 0875 and AUC of 0953

A robust algorithm for detecting people in overhead views

Abstract: In this research a human detection system is proposed in which people are viewed from an overhead camera with a wide angle lens. Due to perspective change a person can have different orientations and sizes at different positions in the scene relative to the optical centre. We exploit this property of the overhead camera and develop a novel algorithm which uses the variable size bounding boxes with different orientations, with respect to the radial distance of the center of the image. In these overhead view images we neither used any assumption about the pose or the visibility of a person nor imposed any restriction about the environment. When compare the results of proposed algorithm with a standard histogram of oriented gradient (HOG) algorithm, we achieve not only a huge gain in overall detection rate but also a significant improvement in reducing spurious detections per image. On average, 9 false detections occur per image. A new algorithm is proposed where transforming the image patch containing a person to remove positional dependency and then applying the HOG algorithm eliminates 98% of the spurious detections in noisy images from an industrial assembly line and detects people with a 95% efficiency.

Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition

Abstract: Face recognition is a challenging research field in computer sciences, numerous studies have been proposed by many researchers. However, there have been no effective solutions reported for full illumination variation of face images in the facial recognition research field. In this paper, we propose a methodology to solve the problem of full illumination variation by the combination of histogram equalization (HE) and Gaussian low-pass filter (GLPF). In order to process illumination normalization, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods. Next, a Support Vector Machine classifier is used for face classification. In the experiments, illustration performance was compared with our proposed approach and the conventional approaches with three different kinds of face databases. Experimental results show that our proposed illumination normalization approach (HE_GLPF) performs better than the conventional illumination normalization approaches, in face images with the full illumination variation problem.

Improved credit card churn prediction based on rough clustering and supervised learning techniques

Abstract: Every process is digitized in the current society. Transfer of money from one account holder to another has become possible in seconds because of advanced technologies in information processing. Not only this in all sectors like railways, insurance, health sector, fashion technology, education sector, sales and business sectors, and advertisement sectors every firm digitized its operations. One such sector is banking where every individual based on his or her financial status will be considered for crediting loan, and credit card etc. If the credit score of the loan availing person is high banks will be ready to provide him with the loan but the availing person can opt for any one of the banks on his or her own willing. Such scenario happens in credit card churn prediction also. Hence the banks should take healthy measures to retain the existing credit card holders without any churn. Withholding existing customers of a firm plays an important role to increase the overall revenue of the firm and retains the good name of the firm in competitive market. Hence every organization takes key measures to withhold existing customers using customer management models. Because customer retention is a crucial task as it reduces the time, money and workforce needed for adding new customers to the firm. Customers retention technique in credit card churn prediction (C3P) was done using only supervised classification techniques. But it could not end with better results. So, through many proven hybrid classification techniques we can bring better accuracy in C3P. Also C3P lags in highly efficient techniques like rough set theory. Hence in this work initially we perform data processing techniques and in second stage we propose modified rough K-means algorithm used for clustering credit card holders and in next stage hold-out method divides the cluster data into testing and training clusters. At last classification is performed using various algorithms like support vector machine, random forest, decision tree, K-nearest neighbor, and Naive Bayes. Finally we evaluate the work using precision, recall (sensitivity), specification, accuracy, and misclassification error.

RALBA: a computation-aware load balancing scheduler for cloud computing

Abstract: Cloud computing serves as a platform for remote users to utilize the heterogeneous resources in data-centers to compute High-Performance Computing jobs. The physical resources are virtualized in Cloud to entertain user services employing Virtual Machines (VMs). Job scheduling is deemed as a quintessential part of Cloud and efficient utilization of VMs by Cloud Service Providers demands an optimal job scheduling heuristic. An ideal scheduling heuristic should be efficient, fair, and starvation-free to produce a reduced makespan with improved resource utilization. However, static heuristics often lead to inefficient and poor resource utilization in the Cloud. An idle and underutilized host machine in Cloud still consumes up to 70% of the energy required by an active machine (Ray, in Indian J Comput Sci Eng 1(4):333–339, 2012). Consequently, it demands a load-balanced distribution of workload to achieve optimal resource utilization in Cloud. Existing Cloud scheduling heuristics such as Min–Min, Max–Min, and Sufferage distribute workloads among VMs based on minimum job completion time that ultimately causes a load imbalance. In this paper, a novel Resource-Aware Load Balancing Algorithm (RALBA) is presented to ensure a balanced distribution of workload based on computation capabilities of VMs. The RABLA framework comprises of two phases: (1) scheduling based on computing capabilities of VMs, and (2) the VM with earliest finish time is selected for jobs mapping. The outcomes of the RALBA have revealed that it provides substantial improvement against traditional heuristics regarding makespan, resource utilization, and throughput.

Facial appearance and texture feature-based robust facial expression recognition framework for sentiment knowledge discovery

Abstract: Facial sentiment analysis has been an enthusiastic research area for the last two decades A fair amount of work has been done by researchers in this field due to its utility in numerous applications such as facial expression driven knowledge discovery However, developing an accurate and efficient facial expression recognition system is still a challenging problem Although many efficient recognition systems have been introduced in the past, the recognition rate is not satisfactory in general due to inherent limitations including light, pose variations, noise, and occlusion In this paper, a hybrid approach of facial expression based sentiment analysis has been presented combining local and global features Feature extraction is performed fusing the histogram of oriented gradients (HOG) descriptor with the uniform local ternary pattern (U-LTP) descriptor These features are extracted from the entire face image rather than from individual components of faces like eyes, nose, and mouth The most suitable set of HOG parameters are selected after analyzing them experimentally along with the ULTP descriptor, boosting performance of the proposed technique over face images containing noise and occlusions Face sentiments are analyzed classifying them into seven universal emotional expressions: Happy, Angry, Fear, Disgust, Sad, Surprise, and Neutral Extracted features via HOG and ULTP are fused into a single feature vector and this feature vector is fed into a Multi-class Support Vector Machine classifier for emotion classification Three types of experiments are conducted over three public facial image databases including JAFFE, MMI, and CK+ to evaluate the recognition rate of the proposed technique during experimental evaluation; recognition accuracy in percent, ie, 9571, 9820, and 9968 are achieved for JAFFE, MMI, and CK+, respectively

An implementation of matrix–matrix multiplication on the Intel KNL processor with AVX-512

Abstract: The second generation Intel Xeon Phi processor codenamed Knights Landing (KNL) have recently emerged with 2D tile mesh architecture and the Intel AVX-512 instructions. However, it is very difficult for general users to get the maximum performance from the new architecture since they are not familiar with optimal cache reuse, efficient vectorization, and assembly language. In this paper, we illustrate several developing strategies to achieve good performance with C programming language by carrying out general matrix–matrix multiplications and without the use of assembly language. Our implementation of matrix–matrix multiplication is based on blocked matrix multiplication as an optimization technique that improves data reuse. We use data prefetching, loop unrolling, and the Intel AVX-512 to optimize the blocked matrix multiplications. When we use a single core of the KNL, our implementation achieves up to 98% of SGEMM and 99% of DGEMM using the Intel MKL, which is the current state-of-the-art library. Our implementation of the parallel DGEMM using all 68 cores of the KNL achieves up to 90% of DGEMM using the Intel MKL.

Extreme learning machine based microscopic red blood cells classification

Abstract: The digitalization of blood slides introduced pathology to a new era. Despite being the most powerful prognostic tool; automated analysis of microscopic blood smear images is still not used in routine clinical practices as manual pathological image analysis methods are still in use that is tedious, time consuming and subjective to technician dependent variation, furthermore it also needs training and skills. In this work, we present novel method based on extreme machine learning approach for the classification of red blood cells (RBC) images. Segmentation of RBC is initiated with statistical based thresholding to retrieve those pixels which are most relevant to RBC followed by Fuzzy C-means for the image segmentation and boundary detection. Different texture and geometrical features are extracted for the classification of normal and abnormal cells. The classification technique is rigorously evaluated against the dataset to evaluate the accuracy of classifier. We have compared the results with state of the art techniques. So far the proposed technique has produced more promising results as compared to the existing techniques.

Android malware detection method based on naive Bayes and permission correlation algorithm

Abstract: In order to detect Android malware more effectively, an Android malware detection model was proposed based on improved naive Bayes classification. Firstly, considering the unknown permission that may be malicious in detection samples, and in order to improve the Android detection rate, the algorithm of malware detection is proposed based on improved naive Bayes. Considering the limited training samples, limited permissions, and the new malicious permissions in the test samples, we used the impact of the new malware permissions and training permissions as the weight. The weighted naive Bayesian algorithm improves the Android malware detection efficiency. Secondly, taking into account the detection model, we proposed a detection model of permissions and information theory based on the improved naive Bayes algorithm. We analyzed the correlation of the permission. By calculating the Pearson correlation coefficient, we determined the value of Pearson correlation coefficient r, and delete the permissions whose value r is less than the threshold $$\rho $$ and get the new permission set. So, we got the improved detection model by clustering based on information theory. Finally, we detected the 1725 Android malware and 945 non malicious application of multiple data sets in the same simulation environment. The detection rate of the improved the naive Bayes algorithm is 86.54%, and the detection rate of the non-malicious application is increased to 97.59%. Based on the improved naive Bayes algorithm, the false detection rate of the improved detection model is reduced by 8.25%.

Heart rate variability based stress index service model using bio-sensor

Abstract: As infinite competition and materialism have become severe in the current society, stress management has emerged as a main topic. There are many causes that create stress, including external factors and personal events. Also, stress has different levels, depending on an individuals’ subjective analysis. Stress has high correlations with cardiovascular disorders and mental illness. In particular, long-term stress leads to lowered immunity, which makes people more exposed to various diseases, and brings personal and social costs. With the rapid development of the IoT, it has been easy to analyze and manage stress with the use of sensors and communications technology relating to the human body and its surroundings. This study proposes a heart-rate variability-based stress index service using a biosensor. The proposed method collects a variety of information in dual physical environments (such as temperature, humidity, and brightness) from IoT devices, and analyzes it in real-time. The discomfort index and wind chill temperature index offered by the Korea Meteorological Administration, and the temperature, humidity, noise, and brightness collected from a biosensor are the most clear factors to digitize the physical environments of stress. Also, a smart health platform analyzes different heart rates depending on individual conditions, and monitors current status. For a heart rate, the frequency of the R-R value and low frequency (LF) are analyzed. For R-R value, a maximum value detection algorithm is applied. For LF analysis, Fourier transform is used. Generally, fast Fourier transform is unable to analyze the relation between time and frequency. Accordingly, applied is a short time Fourier transform in which window size is limited in a graph so as to express an effect made by changing time effectively. A stress index is comprised of discomfort level, wind chill temperature, noise, brightness, and heart rate. The notification of risk is given to the user by signal lights indicating stability, warning, or danger. The stress index service enables a user to check the stress index in real-time over a smart health platform at any place and at any time. Therefore, it serves as a tool to notify one’s acquaintances of a risk when one faces an emergent situation or is about to be at risk.

Ensuring performance and provider profit through data replication in cloud systems

Abstract: Cloud computing is a relatively recent computing paradigm that is often the answer for dealing with large amounts of data. Tenants expect the cloud providers to keep supplying an agreed upon quality of service, while cloud providers aim to increase profits as it is a key ingredient of any economic enterprise. In this paper, we propose a data replication strategy for cloud systems that satisfies the response time objective for executing queries while simultaneously enables the provider to return a profit from each execution. The proposed strategy estimates the response time of the queries and performs data replication in a way that the execution of any particular query is still estimated to be profitable for the provider. We show with simulations that how the proposed strategy fulfills these two criteria.

Enhanced DNA and ElGamal cryptosystem for secure data storage and retrieval in cloud

Abstract: Cloud computing enables the access of the resources such as network hardware’s, storage, applications and services that are configurable based on the demand in a network especially specific to the operations on the data. The need for data security in the cloud is progressively higher as the abundant sensitive data in the cloud are transferred among various stakeholders for data operations leads to loss of data confidentiality. To maintain data confidentiality in the cloud, the data need to be encrypted with cryptographic algorithms. Existing cryptographic algorithms face the challenges of key management, dynamic encryption, and computational complexity. In this paper, a novel variant of DNA cryptosystem is proposed to secure the original data within the DNA nucleotides providing greater storage space, reduced overhead and dynamic operations. The significance of DNA is incorporated in the proposed Novel DNA cryptosystem, which encrypts the data transferred between the Data Owner and the Data User in the cloud. Enhanced ElGamal cryptosystem is the proposed asymmetric cryptosystem used to address key management issues in the cloud, by securely transferring the key file between the Data Owner and the Data User. Enhanced ElGamal cryptosystem provides better user authentication and performance with respect to the security accomplishment against attacks. At the same time, Novel DNA cryptosystem achieves better performance, reduced the complexity of implementing the properties of DNA and embarks upon a standardized algorithmic approach among the existing DNA cryptographic methodologies. The performance analysis, mathematical proof as well as security analysis forms the security metrics and it meets out the proposed objectives. Thus, on utilizing the proposed Novel DNA and Enhanced ElGamal cryptosystems (i.e) both symmetric and asymmetric cryptosystems, enhances the security and performance of data storage and retrieval in the cloud.

BIGMiner: a fast and scalable distributed frequent pattern miner for big data

Abstract: Frequent itemset mining is widely used as a fundamental data mining technique. Recently, there have been proposed a number of MapReduce-based frequent itemset mining methods in order to overcome the limits on data size and speed of mining that sequential mining methods have. However, the existing MapReduce-based methods still do not have a good scalability due to high workload skewness, large intermediate data, and large network communication overhead. In this paper, we propose BIGMiner, a fast and scalable MapReduce-based frequent itemset mining method. BIGMiner generates equal-sized sub-databases called transaction chunks and performs support counting only based on transaction chunks and bitwise operations without generating and shuffling intermediate data. As a result, BIGMiner achieves very high scalability due to no workload skewness, no intermediate data, and small network communication overhead. Through extensive experiments using large-scale datasets of up to 6.5 billion transactions, we have shown that BIGMiner consistently and significantly outperforms the state-of-the-art methods without any memory problems.

Arrhythmia and ischemia classification and clustering using QRS-ST-T (QT) analysis of electrocardiogram

Abstract: The objective is to propose a collective analytical model for QRS complex, ST segment and T wave (i.e., QT complex) of electrocardiogram to evaluate the onset or occurrence of cardiovascular abnormalities. The proposed methodologies also classify healthy subjects, arrhythmic and ischemic patients. The idea is to extract the QRS-ST-T features; where, QT interval and 99% occupied bandwidth (Hz) features are extracted from QT complex and QRS versus ST-T interval ratio (%) is also formulated after segmenting the QT complex into QRS complex and ST-T segment by localizing the inflection points. The evaluation of this proposed approach has been carried out using the selected 36 recordings (true positive (TP) beats) from each standard databases i.e., MIT-BIH arrhythmia database, FANTASIA and European ST-T database. The method is initiated with the preprocessing stage and then the inflection points (i.e., $$Q,S,T_{\mathrm{offset}})$$ are detected using Pan-Tompkins method and curve analysis techniques. Then the time-frequency domain features (e.g. QT interval (s) and 99% occupied bandwidth (Hz)) are extracted from the segmented mean QT complex and the QRS versus ST-T interval (%) ratio is extracted from the segmented mean QRS versus ST-T segments simultaneously. These features are introduced to the classifier like decision tree, support vector machine and K-means for clustering operation. The classification success rate is 97.03% and resubstitution error rate is 2.97% among the arrhythmia, ischemia and healthy classes using QT interval and QRS versus ST-T interval ratio (%) features. The evaluations of other features are also analyzed along with graphical classification results. Allied evaluation of segments belonging to ventricular depolarization (QRS complex) and repolarization (ST segment and T wave) i.e., QT complex, will certainly improve the detection probability of ischemia and arrhythmia with further correlative parametric features. This also leads to automatic detection and classification of arrhythmia and ischemia by avoiding visual inspection and error free decison making.

Multi-level trust based intelligence intrusion detection system to detect the malicious nodes using elliptic curve cryptography in MANET

Abstract: Mobile ad hoc networks (MANETs) are qualified by multi-hop wireless links and resource restrained nodes Generally, mobile ad hoc networks (MANETs) are susceptible to various attacks like gray hole attack, black hole attack, selective packet dropping attack, Sybil attack, and flooding attack Therefore, the wireless network should be protected using encryption, firewalls, detection schemes to identify the attackers and decreasing their impact on the network So, it’s an essential task to design the intelligent intrusion detection system This research work deals with designing the multilevel trust based intelligence intrusion detection system with cryptography schemes for detecting the attackers In order to identify the attackers, we propose a novel trust management with elliptic curve cryptography (ECC) algorithm At first, a trust manager is maintained, its functions is to classify the trust into three different sets of trust level based upon the elliptic curve cryptography and Schnorr’s signature in the MANET Each trust level has identified a single attacker Thus, the proposed method has detected three types of attackers such as black hole attack, flooding attack and selective packet dropping attack Furthermore, it have provided countermeasure for these attackers in the MANET as well as improved performances Hence, it obtains higher throughput, minimum delay, minimum packet loss and efficient end to end delivery in MANET Thus, the proposed scheme is a secure and optimal solution to encounter attackers, which represents to be efficient and significant

Optimizing the minimum spanning tree-based extracted clusters using evolution strategy

Abstract: There are many approaches available for extracting clusters. A few are based on the partitioning of the data and others rely on extracting hierarchical structures. Graphs provide a convenient representation of entities having relationships. Clusters can be extracted from a graph-based structure using minimum spanning trees (MSTs). This work focuses on optimizing the MST-based extracted clusters using Evolution Strategy (ES). A graph may have multiple MSTs causing varying cluster formations based on different MST selection. This work uses (1+1)-ES to obtain the optimal MST-based clustering. The Davies–Bouldin Index is utilized as fitness function to evaluate the quality of the clusters formed by the ES population. The proposed approach is evaluated using eleven benchmark datasets. Seven of these are based on microarray and the rest are taken from the UCI machine learning repository. Both, external and internal cluster validation indices are used to evaluate the results. The performance of the proposed approach is compared with two state-of-the-art MST-based clustering algorithms. The results support promising performance of the proposed approach in terms of time and cluster validity indices.