Abstract
In most of the modern-day computing systems, security enhancements are a part of security design. The majority of the effort in providing robust security to a system is involved in the identification of cyber threats and how to recover from such cyberattacks. Many researchers have proposed sub-optimal strategies which has been the motivation of this research. This study summarises the research gaps and proposes research direction for mitigating the challenges concerned in that direction. This work reviews the current methodologies to provide a framework which can auto identify cyber threats and to determine how the bio-inspired algorithms can be applied to minimize the effort involved in identification and recovery from cyberattacks. Cyber threat intelligence frameworks serve as crucial elements in providing secure operating environment for the cyber practitioners. The design and development of cyber threat intelligence framework is challenging not only for the cost and effort involved in it but also due to intrinsic dependent entities of cyber security. This study proposes novel principles for bridging the identified research gaps through feature engineering, trust computing base, and bio-inspired based time optimization. There is a lot of research potential in this direction and this study is a sincere and ideal attempt towards the same goal.