Big Data Cyber Security Using Machine Learning

Cyber security in the context of big data is known to be a critical problem and presents a great challenge to the research community. Machine learning algorithms have been suggested as candidates for handling big data security problems. Among these algorithms, support vector machines (SVMs) have achieved remarkable success on various classification problems. However, to establish an effective SVM, the user needs to deny the proper SVM configuration in advance, which is a challenging task that requires expert knowledge and a large amount of manual effort for trial and error. Here we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. We propose a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyper-heuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics.

Autorentext

Dr. Arun Kumar Kandru Assoc. Professor CSE am Malla Reddy Engineering College, mit 13 Jahren Lehrerfahrung.Dr. Anuradha Chinta Assistenzprofessorin CSE am V R Siddhartha Engineering College, mit 11 Jahren Lehrerfahrung.Dr. Kunchala Little Flower Stellvertretende Professorin AIML an der Malla Reddy University, mit 11 Jahren Lehrerfahrung.