Secure Opportunistic Routing for Vehicular Adhoc Networks

More study is needed to make VANETs more relevant. Opportunistic routing (OR) is a new model that has been proposed for wireless networks. OR has emerged from the research communities because of its ability to increase the performance of wireless networks. It benefits from the broadcast characteristic of wireless mediums to improve network performance. The basic function of OR is its ability to overhear the transmitted packet and to coordinate among relaying nodes. In this chapter, an exhaustive survey of existing OR protocols is done by considering various factors. More precisely, existing secure OR protocols are deliberated. Future directions of research are also included, which provide a superior way to overcome some of the limitations of these existing protocols. Through this detailed survey, an outline and in-depth knowledge of existing OR protocols can be acquired.

Harnessing Collective Intelligence Through Pattern Mining in Social Computational Systems

Due to the digital nature of the web, the social web mimics the real-world social dynamics that manifest themselves as data and can be easily mined as patterns, making the web a fertile ground for business and research-oriented analytical applications. Collective intelligence (CI) is a multifaceted field with roots in sociology, biology, and many other disciplines. Various manifestations of CI support the successful existence of large-scale social systems. This chapter gives an overview of the principles of CI and the concept of “wisdom of crowds” and highlights how to maximize the potential of big data analytics for CI. Also, various techniques and approaches have been described that leverage these CI concepts across a diverse range of ever-evolving social systems for commercial business applications like influence mining, expertise discovery, etc.

Big Data Analytics Tools and Platform in Big Data Landscape

The term big data analytics refers to mining and analyzing of the voluminous amount of data in big data by using various tools and platforms. Some of the popular tools are Apache Hadoop, Apache Spark, HBase, Storm, Grid Gain, HPCC, Casandra, Pig, Hive, and No SQL, etc. These tools are used depending on the parameter taken for big data analysis. So, we need a comparative analysis of such analytical tools to choose best and simpler way of analysis to gain more optimal throughput and efficient mining. This chapter contributes to a comparative study of big data analytics tools based on different aspects such as their functionality, pros, and cons based on characteristics that can be used to determine the best and most efficient among them. Through the comparative study, people are capable of using such tools in a more efficient way.

Insight Into Big Data Analytics

In the present digital era, more data are generated and collected than ever before. But, this huge amount of data is of no use until it is converted into some useful information. This huge amount of data, coming from a number of sources in various data formats and having more complexity, is called big data. To convert the big data into meaningful information, the authors use different analytical approaches. Information extracted, after applying big data analytics methods over big data, can be used in business decision making, fraud detection, healthcare services, education sector, machine learning, extreme personalization, etc. This chapter presents the basics of big data and big data analytics. Big data analysts face many challenges in storing, managing, and analyzing big data. This chapter provides details of challenges in all mentioned dimensions. Furthermore, recent trends of big data analytics and future directions for big data researchers are also described.

Performance Analysis of Mail Clients on Low Cost Computer With ELGamal and RSA Using SNORT

An intrusion detection system (IDS) focuses on determining malicious tasks by verifying network traffic and informing the network administrator for restricting the user or source or source IP address from accessing the network. SNORT is an open source intrusion detection system (IDS) and SNORT also acts as an intrusion prevention system (IPS) for monitoring and prevention of security attacks on networks. The authors applied encryption for text files by using cryptographic algorithms like Elgamal and RSA. This chapter tested the performance of mail clients in low cost, low power computer Raspberry Pi, and verified that SNORT is efficient for both algorithms. Within low cost, low power computer, they observed that as the size of the file increases, the run time is constant for compressed data; whereas in plain text, it changed significantly.

Management and Monitoring Patterns and Future Scope

Extricating information from expansive, heterogeneous, and loud datasets requires capable processing assets, as well as the programming reflections to utilize them successfully. The deliberations that have risen in the most recent decade mix thoughts from parallel databases, dispersed frameworks, and programming dialects to make another class of adaptable information investigation stages that shape the establishment of information science. In this chapter, the scene of important frameworks, the standards on which they depend, their tradeoffs, and how to assess their utility against prerequisites are given.

Web Access Patterns of Actual Human Visitors and Web Robots

Web robots are autonomous software agents used for crawling websites in a mechanized way for non-malicious and malicious reasons. With the popularity of Web 2.0 services, web robots are also proliferating and growing in sophistication. The web servers are flooded with access requests from web robots. The web access requests are recorded in the form of web server logs, which contains significant knowledge about web access patterns of visitors. The presence of web robot access requests in log repositories distorts the actual access patterns of human visitors. The human visitors' actual web access patterns are potentially useful for enhancement of services for more satisfaction or optimization of server resources. In this chapter, the correlative access patterns of human visitors and web robots are discussed using the web server access logs of a portal.

A Relative Performance of Dissimilarity Measures for Matching Relational Web Access Patterns Between User Sessions

Customized web services are offered to users by grouping them according to their access patterns. Clustering techniques are very useful in grouping users and analyzing web access patterns. Clustering can be an object clustering performed on feature vectors or relational clustering performed on relational data. The relational clustering is preferred over object clustering for web users' sessions because of high dimensionality and sparsity of web users' data. However, relational clustering of web users depends on underlying dissimilarity measures used. Therefore, correct dissimilarity measure for matching relational web access patterns between user sessions is very important. In this chapter, the various dissimilarity measures used in relational clustering of web users' data are discussed. The concept of an augmented user session is also discussed to derive different augmented session dissimilarity measures. The discussed session dissimilarity measures are used with relational fuzzy clustering algorithms. The comparative performance binary session similarity and augmented session similarity measures are evaluated using intra-cluster and inter-cluster distance-based cluster quality ratio. The results suggested the augmented session dissimilarity measures in general, and intuitive augmented session (dis)similarity measure, in particular, performed better than the other measures.

Machine Learning Models for Forecasting of Individual Stocks Price Patterns

Stock investors always consider potential future prices before investing in any stock for making a profit. A large number of studies are found on the prediction of stock market indices. However, the focus on individual stock closing price predictions well ahead of time is limited. In this chapter, a comparative study of machine-learning-based models is used for the prediction of the closing price of a particular stock. The proposed models are designed using back propagation neural networks (BPNN), support vector regression (SVR) with SMOReg, and linear regression (LR) for the prediction of the closing price of individual stocks. A total of 37 technical indicators (features) derived from historical closing prices of stocks are considered for predicting the future price of stock in a time window of five days. The experiment is performed on stocks listed on Bombay Stock Exchange (BSS), India. The model is trained and tested using feature values extracted from the past five-year closing price of stocks of different sectors including aviation, pharma, banking, entertainment, and IT.

Investigation on Deep Learning Approach for Big Data

In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.