Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2929
Title: Threat Intelligent Base Risk Observation Framework
Authors: Lakshitha, S. A. D. K.
Keywords: CTI
TIROF
WOL
CVE
vulnerabilities
possibility of being attacked or harmed
Common Vulnerabilities and Exposure
Web Ontology Language
Threat Intelligence Risk Observation Framework
Cyber threat Intelligence Framework
Issue Date: 2021
Abstract: Information systems of every organization are highly depending on information security framework. Day by day threat landscape is getting stronger and security technologies are developing accordingly. Always growing threat landscapes are adding organization an additional risk while organizations computer system risk factor is changing according to the end user traffic, running applications and operating system vulnerabilities. But enterprises always try to keep the risk factor in an acceptable level. For risk assessment and security practices, efficient analysis of distributed Cyber Threat Intelligence (CTI) information is very important. Threat profiling is gaining popularity to enforce a proactive line of resistance. However, assessing a systems resiliency in the face of appropriate threats and identified in CTI shared data remains problematic, and it hold lack of semantics and background detail in textual representations of threat awareness. This threat intelligence base risk observation framework (TIROF) is a software tool that observe and indicate risk level of the computer system using threat intelligence feed and National Vulnerability database. Further it will assess application risk factor separately using available Common Vulnerabilities and Exposure (CVE). Tool will be developed with rules and inferences, the system offers an automated method to examine about the cyber threats impacting the computer system by classifying threat significance, assessing threat probability, and identifying the affected and exposed properties.
URI: http://rda.sliit.lk/handle/123456789/2929
Appears in Collections:MSc 2021

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