We live in the era of digital technologies where data is increasing day by day at a very high rate. The data is further popularly classified as ‘Big Data’ because of its velocity, veracity, variety and its huge volume. This data could be unstructured, semi-structured or structured as it is divergent in nature. In this work, we would assess various categories of Amazon Product Reviews, the large datasets that contain around 144 million reviews in total. The datasets consists of Product reviews collected from Amazon, each having various numbers of attributes of 11 different categories. The motive of this work is to find and compare the ratings of the products during the lifespan of the product reviews. Another goal of this work is to help Amazon regarding the listing of the products in their database.
This work aims to relate user’s ratings and reviews to discover how beneficial and good a product is [6]. User ratings are collected and are analyzed based on different categories (datasets) which gives an insight as to which product performs good and what are the problems associated to a certain non-performing product.

Due to the enormous growth of network based services and the need for secure communications over the network there is an increasing emphasis on improving intrusion detection systems so as to detect the growing network attacks. A lot of data mining techniques have been proposed to detect intrusions in the network. In this paper study of two different classification algorithms has been carried out: Na?ve Bayes and J48. Results obtained after applying these algorithms on 10% of the KDD’99 dataset and on 10% of the filtered KDD’99 dataset are compared and analyzed based on several performance metrics. Comparison between these two algorithms is also done on the basis of the percentage of correctly classified instances of different attack categories present in both the datasets as well as the time they take to build their classification models.Overall J48 is a better classifier compared to Na?ve Bayes on both the datasets but it is slow in building the classification model.

Intrusion-detection systems (IDS) aim at de-tecting attacks against computer systems and networks or, in general, against information systems. Its basic aim is to protect the system against malwares and unauthorized access of a network or a system. Intrusion Detection is of two types Network-IDS and Host Based- IDS. This paper covers the scope of both the types and their result analysis along with their comparison as stated. OSSEC (HIDS) is a free, open source host-base intrusion detection system. It performs log analysis, integrity checking, Windows registry monitoring, rootkit detection, time-based alerting and active response. While Snort (NIDS) is a lightweight intrusion detection system that can log packets coming across your network and can alert the user regarding any attack. Both are efficient in their own distinct fields.