Maruthi Rohit Ayyagari

Bio: Maruthi Rohit Ayyagari is an academic researcher from University of Dallas. The author has contributed to research in topics: Computer science & Network security. The author has an hindex of 3, co-authored 11 publications receiving 170 citations.

A Survey of Deep Learning and Its Applications: A New Paradigm to Machine Learning

Abstract: Nowadays, deep learning is a current and a stimulating field of machine learning. Deep learning is the most effective, supervised, time and cost efficient machine learning approach. Deep learning is not a restricted learning approach, but it abides various procedures and topographies which can be applied to an immense speculum of complicated problems. The technique learns the illustrative and differential features in a very stratified way. Deep learning methods have made a significant breakthrough with appreciable performance in a wide variety of applications with useful security tools. It is considered to be the best choice for discovering complex architecture in high-dimensional data by employing back propagation algorithm. As deep learning has made significant advancements and tremendous performance in numerous applications, the widely used domains of deep learning are business, science and government which further includes adaptive testing, biological image classification, computer vision, cancer detection, natural language processing, object detection, face recognition, handwriting recognition, speech recognition, stock market analysis, smart city and many more. This paper focuses on the concepts of deep learning, its basic and advanced architectures, techniques, motivational aspects, characteristics and the limitations. The paper also presents the major differences between the deep learning, classical machine learning and conventional learning approaches and the major challenges ahead. The main intention of this paper is to explore and present chronologically, a comprehensive survey of the major applications of deep learning covering variety of areas, study of the techniques and architectures used and further the contribution of that respective application in the real world. Finally, the paper ends with the conclusion and future aspects.

MLEsIDSs: machine learning-based ensembles for intrusion detection systems—a review

Abstract: Network security plays an essential role in secure communication and avoids financial loss and crippled services due to network intrusions. Intruders generally exploit the flaws of popular software to mount a variety of attacks against network computer systems. The damage caused in the network attacks may vary from a little disruption in service to on developing financial loss. Recently, intrusion detection systems (IDSs) comprising machine learning techniques have emerged for handling unauthorized usage and access to network resources. With the passage of time, a wide variety of machine learning techniques have been designed and integrated with IDSs. Still, most of the IDSs reported poor intrusion detection results using false positive rate and detection rate. For solving these issues, researchers focused on the development of ensemble classifiers involving the integration of predictions by multiple individual classifiers. The ensemble classifiers enable to compensate for the weakness of individual classifiers and use their combined knowledge to enhance its performance. This study presents motivation and comprehensive review of intrusion detection systems based on ensembles in machine learning as an extension of our previous work in the field. Particularly, different ensemble methods in the field are analysed, taking into consideration different types of ensembles, and various approaches for integrating the predictions of individual classifiers for an ensemble classifier. The representative studies are compared in chronological order for systematic and critical analysis, understanding the current challenges and status of research in the field. Finally, the study presents essential future research directions for the development of effective IDSs.

Intrusion detection techniques in network environment: a systematic review

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.

A Framework for Analytical CRM Assessments Challenges and Recommendations

Abstract: Businesses are increasingly adopting analytical customer relationship management (CRM) solutions. The critical customer information that resides within CRM can guide the decision-making process. Therefore, CRM analysis leads to higher loyalty and customer satisfaction, as well as enhanced competitive and financial performance. Data mining techniques are used to understanding customers and discovering interesting patterns. However, data mining techniques are considered a complicated process for non-technical decision makers and administrators. Therefore, the problem increases with the technical difficulty of large-scale CRM solutions for novice administrators and decision makers. This paper proposes a framework for the process of data mining in the context of analytical CRM to enhance the decision-making process. The paper also highlights the role of data mining in analytical CRM and how it assists the businesses to manage customer information better. The framework was evaluated and accepted by two senior CRM experts. The proposed framework revealed that there are still issues of customer data privacy and issues related to collected data types.

Scientific Machine Learning Through Physics–Informed Neural Networks: Where we are and What’s Next

Abstract: Abstract Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like Partial Differential Equations (PDE), as a component of the neural network itself. PINNs are nowadays used to solve PDEs, fractional equations, integral-differential equations, and stochastic PDEs. This novel methodology has arisen as a multi-task learning framework in which a NN must fit observed data while reducing a PDE residual. This article provides a comprehensive review of the literature on PINNs: while the primary goal of the study was to characterize these networks and their related advantages and disadvantages. The review also attempts to incorporate publications on a broader range of collocation-based physics informed neural networks, which stars form the vanilla PINN, as well as many other variants, such as physics-constrained neural networks (PCNN), variational hp-VPINN, and conservative PINN (CPINN). The study indicates that most research has focused on customizing the PINN through different activation functions, gradient optimization techniques, neural network structures, and loss function structures. Despite the wide range of applications for which PINNs have been used, by demonstrating their ability to be more feasible in some contexts than classical numerical techniques like Finite Element Method (FEM), advancements are still possible, most notably theoretical issues that remain unresolved.

Machine learning approaches to IoT security: A systematic literature review

Abstract: With the continuous expansion and evolution of IoT applications, attacks on those IoT applications continue to grow rapidly. In this systematic literature review (SLR) paper, our goal is to provide a research asset to researchers on recent research trends in IoT security. As the main driver of our SLR paper, we proposed six research questions related to IoT security and machine learning. This extensive literature survey on the most recent publications in IoT security identified a few key research trends that will drive future research in this field. With the rapid growth of large scale IoT attacks, it is important to develop models that can integrate state of the art techniques and technologies from big data and machine learning. Accuracy and efficiency are key quality factors in finding the best algorithms and models to detect IoT attacks in real or near real-time