Generic Attribute Scoring for Information Decay in Threat Information Sharing Platform

Cyber Threat Intelligence(CTI)has gained massive attention to collect hidden knowledge for a better understanding of the various cyber-attacks and eventually paving the way for predicting the future of such attacks.The information exchange and collaborative sharing through different platforms have a significant contribution towards a global solution.While CTI and the information exchange can help a lot in focusing and prioritizing on the use of the large volume of complex information among different organizations,there exists a great challenge ineffective processing of large count of different Indicators of Threat(IoT)which appear regularly,and that can be solved only through a collaborative approach.Collaborative approach and intelligence sharing have become the mandatory element in the entire world of processing the threats.In order to covet the complete needs of having a definite standard of information exchange,various initiatives have been taken in means of threat information sharing platforms like MISP and formats such as SITX.This paper proposes a scoring model to address information decay,which is shared within TISP.The scoring model is implemented,taking the use case of detecting the Threat Indicators in a phishing data network.The proposed method calculates the rate of decay of an attribute through which the early entries are removed.

CARE: Cloud Archival Repository Express via Algorithmic Machine Learning

CARE—Cloud Archive Repository Express has emerged from algorithmic machine learning, and acts like a “fastlane” to bridge between DATA and wiseCIO where DATA stands for digital archiving & trans-analytics, and wiseCIO for web-based intelligent service. CARE incorporates DATA and wiseCIO into a triad for content management and delivery (CMD) to orchestrate Anything as a Service (XaaS) by using mathematical and computational solutions to cloud-based problems. This article presents algorithmic machine learning in CARE for “DNA-like” ingredients with trivial information eliminated through deep learning to support integral content management over DATA and informative delivery on wiseCIO. In particular with algorithmic machine learning, CARE creatively incorporates express tokens for information interchange (eTokin) to promote seamless intercommunications among the CMD triad that enables Anything as a Service and empowers ordinary users to be UNIQ professionals: such as ubiquitous manager on content management and delivery, novel designer on universal interface and user-centric experience, intelligent expert for business intelligence, and quinary liaison with XaaS without explicitly coding required. Furthermore, CMD triad harnesses rapid prototyping for user interface design and propels cohesive assembly from Anything orchestrated as a Service. More importantly, CARE collaboratively as a whole promotes instant publishing over DATA, efficient presentation to end-users via wiseCIO, and diligent intelligence for business, education, and entertainment (iBEE) through highly robotic process automation.

Applying IT to Avoid Bullwhip Effect of Supply Chain

Bullwhip effect is the most important factor considered in the supply chain management. It gets many scholars＇ attention that bullwhip effect has been restricting the development of the supply chain all the time. Information Technology （IT） can reduce bullwhip effect by sharing the information among the enterprises in the supply chain.