Traditional data analytics tools are designed to deal with the asymmetrical type of data i.e., structured, semi-structured, and unstructured. The diverse behavior of data produced by different sources requires the selection of suitable tools. The restriction of recourses to deal with a huge volume of data is a challenge for these tools, which affects the performances of the tool’s execution time. Therefore, in the present paper, we proposed a time optimization model, shares common HDFS (Hadoop Distributed File System) between three Name-node (Master Node), three Data-node, and one Client-node. These nodes work under the DeMilitarized zone (DMZ) to maintain symmetry. Machine learning jobs are explored from an independent platform to realize this model. In the first node (Name-node 1), Mahout is installed with all machine learning libraries through the maven repositories. The second node (Name-node 2), R connected to Hadoop, is running through the shiny-server. Splunk is configured in the third node (Name-node 3) and is used to analyze the logs. Experiments are performed between the proposed and legacy model to evaluate the response time, execution time, and throughput. K-means clustering, Navies Bayes, and recommender algorithms are run on three different data sets, i.e., movie rating, newsgroup, and Spam SMS data set, representing structured, semi-structured, and unstructured data, respectively. The selection of tools defines data independence, e.g., Newsgroup data set to run on Mahout as others cannot be compatible with this data. It is evident from the outcome of the data that the performance of the proposed model establishes the hypothesis that our model overcomes the limitation of the resources of the legacy model. In addition, the proposed model can process any kind of algorithm on different sets of data, which resides in its native formats.

Today we are living with the extensive volume of information which is developing at a very fast pace. Clustering is the process of group similar kinds of information. The serial k-means clustering method takes a large amount of computational time when it is applied to enormous data sets which are large in size of tera and petabyte. In this paper, we evaluated the performance of the K-means algorithm in different ways like K-mean simple (using java codes on MapReduce), K-means (using java codes on MapReduce) with IEC (Initial Equidistant Centres), K-mean on Mahout (using Machine learning library) and K-mean on Spark (Machine learning library) on static IP address data sets over the MapReduce framework. In addition to this we also compare the K-mean simple and K-mean (IEC) on different iteration level. Outcome shows on the better performance on the basis of time on different infrastructure and it also defines the behaviour of K-means algorithms on the basis of centroids and different iteration levels.

For many years, researchers have researched various aspects of WSN like clustering, security, data aggregation, routing protocols, anomaly detection, etc. Various techniques are being implemented in WSN’s for better data gathering, clustering, security, and many more to make these sensors inexpensive, cost-effective, and easy to deploy. One of the most pressing concerns in WSN is security and to secure sensor networks diverse techniques for instance cryptography, KMS, IDS, etc are used. In the paper, we try to cover varying recent researchers concerning intrusion detection in WSN are being done.

Understanding the ancient script can provide rich details of a civilization, like its cultural, political and social scenarios. Brahmi, an ancient mother script, has been a key to the development of many modern Indian scripts like Gurmukhi, Devanagari, Bangla etc. Inferring ancient writings can be a tedious job and moreover, if executed manually, it requires several language experts. The current paper presents a recognition system for Brahmi characters using linear Support Vector machine classifier. Gradient information of the character images pixels is extracted, and histogram of the gradients is stored as a feature vector for each character image. Character dataset includes both handwritten character images and images from the internet. Linear SVM classifier is trained on the feature set of 24 images of each character. The proposed recognition system is performed with an accuracy of 91.6% to recognize the Brahmi characters from the test images.

Chaotic cryptosystems are using for securing text data and images over the network. In 1998, M S Baptista suggested a chaotic cryptosystem based on partitioning the visiting interval of chaotic orbits of the logistic map, which attracted much attention. In 2002, K W Wong presented a prompt chaotic cryptosystem based onthe dynamic lookup table using a logistic maptoimprove the security in Baptista type cryptosystem. In 2004, Alvarezet al. [1] found that Wong’s cryptosystem [15] is not resistant tochosen-plaintext and known-plaintext attacks. To improve the security of the dynamic lookup table, weintroduce an alternate dynamic lookup table that will be more secure in context to the existing system.

Wireless sensor networks (WSN) have dependability, integrity, and confidentiality issues because of their extensive use. An efficient defensive line for WSN is provided by intrusion detection, a crucial active defence technology. Given the uniqueness of WSN, it is necessary to strike a balance between precise data transmission and constrained sensor energy, as well as between the detecting effect and a lack of network resources. In this study, we suggested a fog computing-based intrusion detection system (IDS) for a smart power grid. This article's main objective is to explain how to use IDS in a smart grid setting. We introduce a stacked model based on the ensemble learning algorithm, which can accurately portray the connections among fog nodes that are vulnerable to cyber-attacks. We conclude by conducting a series of comparative experiments using the KDD CUP 99 dataset with cross-validation, and determine that the Fog-IDS and Stacking-based approaches are more effective in enhancing the performance of the IDS. By conducting simulations, we confirm that our approach effectively analysis fog nodes using the deployed IDS can predict the energy requirement refill station. With an accuracy rate of over 99.837%, MCC rate 99.73%, and F1-Score rate 99.827%.