With many data breaches and spoofing attacks on our networks, it becomes imperative to provide a reliable method for verifying the integrity of the source. Blockchain location-based proof-of-origin is explored for tracking trucks and vehicles. Blockchain applications that support quick authentication with these non-mutable ledger properties: consensus and implemented as smart contracts at the edge. This Blockchain application will now be known as the POWTracker platform, gathering data from multiple cameras. POWTracker is based on an existing GPS-based blockchain ledger and runs on an edge device that uses AI consensus and multiple cameras. By using GPS algorithms, we present a novel mining algorithm that rewards POW miners, providing a trustworthy, verifiable proof-of-location system.

This chapter discusses computing data in a cloud, current challenges to store, retrieve, process, as well as security requirements, and possible solutions. It frames the trust-based access control model that incorporates a digital signature to minimize the malicious activity for storage, retrieval, updates, and processing of sensitive data. The chapter provides the trust framework for the user and access control algorithms for data processing in the cloud environment. Cloud computing is a paradigm with unlimited on-demand services that is software as a service. It can virtualize hardware and software resources, with high processing power, storage, and pay-per-usage. Big Data technology solves many problems irrespective of volume, velocity, and source of generation. It is a continually changing technology, and many industries, customers, and government agencies are involved in usage and managing. Vulnerability assessment of the devices and software supporting these devices are essential to trust the functionality of these devices.

This chapter focuses on strategies implemented by Grambling State University (GSU) to enhance the academic success and retention of STEM majors. Student feedback identified two main reasons for the low retention of freshman STEM majors: the inability of students to connect the content in introductory STEM courses with their chosen career and a feeling of isolation within the degree program. Generous funding received from the National Science Foundation (NSF) enabled faculty to implement steps addressing these areas of concern. Faculty redesigned introductory STEM courses to include problems and mini projects that were relevant to real-world applications. A STEM Learning Community that integrated academic coaching was implemented to help freshmen students adjust to the college environment. Transforming the first-year experience for STEM majors had a huge impact on student success, including academic performance in specific STEM courses. The retention of STEM students from the first to second year increased significantly.

Deep learning (DL) is a process that consists of a set of methods which classifies the raw data into meaningful information fed into the machine. DL performs classification tasks directly from sound, text, and images. One of the famous algorithms for classification of images in DL is convolutional neural networks (CNN). In this research, we tested DL model for image recognition using TensorFlow from Dockers software. We received 99% accurate to identify the test image. The system configuration used for this research includes Ubuntu 16.04, Python 2.7, TensorFlow 1.9, and Google image set (Fatkun Batch Download Image: Google, Google, chrome. google.com/webstore/detail/fatkun-batch-download-ima/nnjjahlikiabnchcpehcpkdeckfgnohf ).

Connecting and controlling the access to the Internet of Things (IoT) are crucial since it converges and evolves multiple technologies. Traditional embedded systems, wireless sensor networks, control systems, software-defined radio technologies, and smart systems contribute to the Internet of Things. The deep learning, data analytics, and consumer applications have an essential role to IoT. The challenges are to store, process, and develop a meaningful form so that it is useful to the business, government, and customers. The paper discusses computing the data in a cloud; current challenges to store, retrieve, and process; security requirements; and possible solutions. We further provide the trust framework for the user and access control algorithms for data processing in the cloud environment.

Several customers access the data in a cloud environment. Security of such data for storing, processing, retrieving, and updating in the cloud environment is critical. If the data is sensitive, access level for every step takes primary responsibility of cloud provider. Currently, the communication of internet-enabled devices (IED) is processing big data and require cloud environment due to their limited storage, processing, and energy capabilities (battery). Encryption is the primary security for transmitting the data from IED's to the cloud, but there is no access control for the data stored in the cloud. Kerberos, a third party authentication protocol, builds on symmetric key (cryptography basis) to allow the nodes for non-secure information, but it does not provide the access control on stored data and other management of the data in the cloud. Because of the current status of the cloud access facilities, this paper builds an event-based access control model for the variety of users and the analysis of messages from social media data, text data, and continuously generated data. In trials, the event-based model was applied to identify the spikes in data (messages or stored data) and decisions based on spikes in the data in a window of time.

Big data conventionally coins large volume of data that continuously increases in a real-time basis and difficult to store, retrieve, and process in traditional database techniques. But it is necessary to know that big data is unstructured and does not follow the conventional storage retrieval methods. The challenges are to store in a cloud, process, and develop meaningful form so that it is useful to the businesses, government, and customers. This paper discusses the current challenges to store, retrieve, process, and implement security requirements, and possible solutions. We further provide the security model in a dynamic cloud environment to store, process and retrieve the data.

Deep learning (DL) is a process that consists of a set of methods which classifies the raw data to meaningful information that fed into the machine. Deep Convolutional nets composed of various processing layers to learn and represent the data. It has multiple levels of abstraction to process images, video, speech and audio. H2o deep learning architecture has many features that include supervised training protocol, memory efficient Java implementation, adaptive learning, and with related CRAN packages. H2o uses supervised training protocol with a uniform adaptive option which is an optimization based on the size of the network. It can take clusters of computing nodes to train on the entire data set but automatically shuffling the training examples for each iteration locally. The framework supports regularization techniques to prevent overfitting. Further, H2o R has intuitive web interface using localhost and IP address. In this research the computations are performed in the H2o cluster and initiated by REST calls (in highly optimized Java code) from R. Since SPARK is available in R, H2o uses a single R session to communicate H2o Java cluster via REST calls. H2o runs inside the Spark executor JVM. Using these packages in R, we demonstrate the classification and automatic recognition of objects. The research extends the NOAA VIIRS Night fires data to detect the persistent fire activity at a given location around the globe. To perform the classification, we use the H2o deep learning package in R Language.

Cloud computing based cognitive radio networks (CCCRN) is an eye-catching research area in recent years to improve the spectrum sensing and spectrum management. Cognitive radio networks (CRN) are capable of adaptive learning and reconfiguration to provide consistent communications in dynamic environments. The adoption and learning in CRN demand fast process of big data. The performance and security in CRN do not meet such requirements due to its low computational power capabilities, particularly in low computational power devices. The advent of cloud capabilities mitigate these constraints. Due to this reason, we suggest the steganography with Advanced Encryption Standard (AES) cryptography technique to protect the cloud data. We identify the critical issues and challenges to implementing CCCRN and provide possible solutions. Even though, both techniques have the same objective, the cloud data in cognitive radio network requires a combination to keep the hackers away from the classified and unclassified data. Integration of cloud computing and cognitive radio increases the performance with added security threats of cloud computing. If the integration overcome these security threats, CCCRN will replace traditional methods of radio operation. The proposed security model incorporated in CCCRN can help the primary user emulation and many other jamming problems. Integrating cognitive radio in cloud arrives secure problems along with real-time processing and energy supply problems. Cloud integration provides resource pooling with additional antennas to meet the real-time performance. Therefore, the cloud is one of the solutions that is facing by CRN. We discuss these problems in the current research paper.

The data stream generated in a social network or geophysical related or network flow is high speed, continuous, multi-dimensional, and contains massive data. Analytics require the insight behavior of the data stream. The government and business giants want to catch the exceptions to reveal the anomalies and take immediate action. To catch up the exceptions, the analysts need to identify the patterns in a single view of data stream trends, exceptions and catch up anomalies before the system collapses. In this paper, we present a system that detects the variations in the area of interest of data stream. The current research includes the classification of the data stream, detect the event type, commonly used detection methods, and interpret the detected events.

Deep learning (DL) is a set of methods that automatically classify the raw data fed into the machine. Deep Convolutional nets composed of multiple processing layers to learn and representation of data with multiple levels of abstraction to process images, video, speech and audio. H2o deep learning architecture has many features that include supervised training protocol, memory efficient Java implementation, adaptive learning, and with related CRAN packages. H2o uses supervised training protocol with a uniform adaptive option which is an optimization based on the size of the network. It can take clusters of computing nodes to train on the entire data set but automatically shuffling the training examples for each iteration locally. The framework supports regularization techniques to prevent overfitting. H2o R has intuitive web interface using localhost and IP address. Using the H2o package in R is easy. The computations are performed in the H2o cluster and initiated by REST calls (in highly optimized Java code) from R. Since SPARK is available in R, H2o uses a single R session and communicates to the H2o Java cluster via REST calls. H2o runs inside the Spark executor JVM. Using these packages in R, we demonstrate the classification and automatic recognition of objects. Further, we use the h2o deep learning package in R Language to classify the NOAA VIIRS Night fires data to detect the persistent fire activity at a given location around the globe.

Fingerprint matching with latent prints and Image recognition using partial information are few of the significant problems in current forensic and computer vision research. Computer vision research has the important role in medical, robotics, economics, and forensic (crime-related) areas. In the crime scene, complete fingerprint or image (caught by the camera) is usually not available. The full image of a fingerprint or partial image is typical in most of the crime data. The crime branch identifies the related match of criminal's data with the help of collected images including partial image data. In this research, we introduce the fingerprint background, classification, and deep learning artificial neural network model to identify the closest match image (print) for a given fingerprint image data using MATLAB tool. The model uses the digits to train and test the data. Later, we replaced the digits with fingerprint images to train the network. The test results are satisfactory. We did not use the GPU-based model in the current implementation. We are working on the GPU-based model using NVIDIA Caffe/Digits to recognize the actual fingerprint using latent print

Image recognition using partial information is one of the significant problems in current computer vision research. Computer vision has an important role in medical, robotics, economics, and crime-related subjects. Complete fingerprint or face is usually not available at a crime scene or camera image. The full image or partial image of crime is common in most of the crime data. The crime branch identifies the related match of criminal’s data with the help of collected image or partial image data. In this research, we introduce the fingerprint background, classification, and deep learning artificial neural network model to identify the closest match image (fingerprint) for a given partial image (latent fingerprint) data..

Most of the fingerprint matching systems use the minutiae-based algorithms with matching of ridge patterns. These systems consider ridge activity in the vicinity of minutiae points, which has poorly recorded/captured exemplar prints (information). In this research, we recommend the MapReduce technique to identify a required fingerprint from the reference fingerprint database. In the proposed MapReduce process, minutiae of the latent fingerprint used as keys. The latent fingerprints are analyzed using Bezier ridge descriptors to enhance the matching of partial latent against reference prints. Since the retrieval of reference print is same as retrieval of the required document, we suggested the MapReduce model for detection of required fingerprint.

Hadoop distributed file system (HDFS) must provide a distributed file system and MapReduce framework. The core components of HDFS are fault tolerant, high throughput, and files of arbitrary size. These components include the shared nothing architecture, a massive parallelism of tasks, and basic data structure is key/value pair. HDFS has shared multi-talent service and is used to store sensitive data. Currently, HDFS is used on private clusters behind firewalls and requires strong authentication and authorization (access control) to protect the sensitive private and public data. The HDFS job is partitioned and distributed on nodes for execution different from the node that the client authenticated and submitted the job. Further, job tasks from various users are executed on the same computer node and the system scales thousands of servers and performs many concurrent tasks. Therefore, the total performance path of the system requires authentication checks at multiple points. Kerberos authentication mechanism helps to meet the security requirements as a supplement to trusted users. Special access control mechanisms may require for high sensitive data to keep the hackers away. The proposed model helps the access control mechanism for high sensitive data in Big Data processing.

Cloud environment on wireless networks is a combination of cloud and mobile technologies. The goal is to support mobile applications more efficient way to utilize bandwidth, energy consumption, hardware utilization, and cost. Cloud is a virtualization concept (borrowed from the virtual machine environment introduced in 1970's) in wireless communications to utilize the resources efficiently and provide the quality of service. The new cloud architecture for wireless networks solves the 4G bottlenecks including current spectrum shortage, improving the data storage capacity and processing power, better network management, and minimizes the energy consumption. The objective of the tool is to simulate the cloud network virtualization platform to transfer the packets from any source node to a selected destination node. Since the cloud table has the current status of nodes in the network (including idle channels), transferring the data packets source node to a destination node becomes easier. The environment was simulated using PyGame in Python 3.4 environment. The simulations demonstrate that the packet selects the optimum path to travel from the source node to the destination node.

The cyber-physical systems are the combination of computational elements and physical entities that can interact with humans through many modalities. The security includes the malicious attempts by adversary that disrupts or fails the functions of physical systems and affects infrastructure, businesses, and routine human life. The research in cyber-physical systems is in its initial stage. Therefore, first we discussed the status of security in cloud cyber-physical systems. Second, we introduced the challenges ahead to the design and development of the future engineering systems with new security capabilities. Third, we presented the security requirements in Hadoop distributed file systems. Since trust based packet transfer in sensor networks is one of the important security issue infrastructure security, we presented a trust-based approach using Sporas formula and presented the simulations to trust of a successive node before transferring the packets. Finally, the paper presents the future research on cyber-physical systems in the cloud environment.

Big data is used for structured, unstructured and semi-structured large volume of data which is difficult to manage and costly to store. Using explanatory analysis techniques to understand such raw data, carefully balance the benefits in terms of storage and retrieval techniques is an essential part of the Big Data. The research discusses the MapReduce issues, framework for MapReduce programming model and implementation. The paper includes the analysis of Big Data using MapReduce techniques and identifying a required document from a stream of documents. Identifying a required document is part of the security in a stream of documents in the cyber world. The document may be significant in business, medical, social, or terrorism.

The cyber-physical systems are the combination of computational elements and physical entities that can interact with humans through many modalities. The security includes the malicious attempts by adversary that disrupts or destructs the functions of physical systems that affects infrastructure, businesses, and routine human life. The research in cyber-physical systems is in its infantry. The work requires the development of security models at cloud interacting with physical systems. The current research discusses four parts. The security requirements in the future engineering systems includes the state of security in cloud cyberphysical systems, security requirements in Hadoop distributed file systems and trust-based security model in sensor networks. Further, the proposed research develops the agent-based approach as an example of trust-based packet transfer. The approach keeps the each node’s current status. The results show that maintaining the ratings of each node, the trust can be calculated and eliminate the malicious node.