This thesis concerns anomaly detection as a mechanism for intrusion detection in a machine learning framework, using two kinds of audit data : system call traces and Unix shell command traces. Anomaly detection systems ...

In the last decade, a lot of machine learning and data mining based approaches have been used in the areas of intrusion detection, malware detection and classification and also traffic analysis. In the area of malware ...

The performance of any deep learning model depends heavily on the quantity and quality of the available training data. The generalization of the trained deep models improves with the availability of a large number of ...

The advent of Autonomous Vehicles (AVs) has the potential to revolutionize transportation systems, promising significant improvements in safety, efficiency, and passenger comforts. Safety, the cornerstone of AVs, demands ...

This thesis presents a novel way of leveraging Artificial Neural Network (ANN) to aid conventional numerical techniques for solving Singularly Perturbed Differential Equation (SPDE). SPDEs are challenging to solve with ...

Traditional strategies to build machine learning based classification systems employ discrete labels as targets. This limits the usefulness of such systems in two ways. First, the generalizability of these systems is limited ...

Deep Neural Networks (DNN) inspired by the human brain have redefined the state-of-the-art performance in AI during the past decade. Much of the research is still trying to understand and explain the function of these ...

Genome-wide association studies (GWAS) are used to find the association between genetic variants, Single Nucleotide Polymorphisms (SNPs), and phenotypic traits or diseases in a population. The number of GWAS has increased ...

The concept of smart city which began as being synonymous with electronically networked community underwent significant changes with the growth in mobile devices and social networking. This expanded the outlook of smart ...

Deep neural networks with millions of parameters are at the heart of many state of the art computer vision models. However, recent works have shown that models with much smaller number of parameters can often perform just ...