Maruthi Rohit Ayyagari

TL;DR: A comprehensive survey of the major applications of deep learning covering variety of areas is presented, study of the techniques and architectures used and further the contribution of that respective application in the real world are presented.

TL;DR: This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of previous work in the field, and presents essential future research directions for the development of effective IDSs.

Abstract: The entire world relates to some network capabilities in some way or the other. The data transmission on the network is getting more straightforward and quicker. An intrusion detection system helps distinguish unauthorized activities or intrusions that may settle the confidentiality, integrity, or availability of a resource. Nowadays, almost all institutions are using network-related facilities like schools, banks, offices, etc. Social media has become so popular that nearly every individual belongs to a new nation called ‘Netizen.’ Several approaches have been implemented to incorporate security features in network-related issues. However, vulnerable attacks are continuous, so intrusion detection systems have been proposed to secure computer systems and networks. Network security is a piece of the most fundamental issues in Computer Network Management. Moreover, an intrusion is considered to be the most revealed dangers to security. With the evolution of the networks, intrusion detection has emerged as a crucial field in networks’ security. The main aim of this article is to deliver a systematic review of intrusion detection approaches and systems that are used in various network environments.

TL;DR: In this article, a multi-factor authentication mechanism linked to a trust model was used on social networking sites (SNS) to identify users of SNS, and the explanatory power of the standard PMT model was raised by 15% by incorporating new components such as past experiences, behavioral control, habitual strength, security and safety support, and individual responsibility.

TL;DR: A framework for the process of data mining in the context of analytical CRM to enhance the decision-making process and how it assists the businesses to manage customer information better is proposed.