Big Data Cyber security Analytics is increasingly becoming an important area of research and practice aimed at protecting networks, computers, and data from unauthorized access by analyzing security event data using big data tools and technologies. Whilst a plethora of Big Data Cyber security Analytic Systems has been reported in the literature, there is a lack of a systematic and comprehensive review of the literature from an architectural perspective. In this paper, we formulate the SVM configuration process as a bi-objective optimization problem in which accuracy and model complexity are considered as two conflicting objectives. System proposes a novel hyper-heuristic framework for bi-objective optimization that is independent of the problem domain. This is the first time that a hyperheuristic has been developed for this problem. The proposed hyper-heuristic framework consists of a high-level strategy and low-level heuristics. The high-level strategy uses the search performance to control the selection of which low-level heuristic should be used to generate a new SVM configuration. The low-level heuristics each use different rules to effectively explore the SVM configuration search space. To address bi-objective optimization, the proposed framework adaptively integrates the strengths of decomposition- and Pareto based approaches to approximate the Pareto set of SVM configurations. The effectiveness of the proposed framework has been evaluated on two cyber security problems: Microsoft malware big data classification and anomaly intrusion detection.