Abstract
Insider threat is a severe problem of many computer department since they have an authorized to do some assigned tasks. They can easily seek for any organizational computer security vulnerability. Protocol “Insider threat detection and prevention Protocol: ITDP” is designed to detect if a requesting “IT user” is authentic IT user whom has been allocated rights to particular application. User’s knowledge and his behavior are used to classify whether he is a real identified IT user or not. Statistical classification technique is used to predict if the guest is authentic. Best classification technique is linear binary discriminant function analysis with 98.3 percentage of accuracy in insider threat detection classification.
Highlights
The security breach in the organizational data processing system has arisen from both external and internal intruders
Insider threats can be malicious seeking the key of some target "IT user"
This paper presents a practical Insider Threat Detection and Prevention Protocol: ITDP
Summary
The security breach in the organizational data processing system has arisen from both external and internal intruders. Based on the decision rule, the Rough Set could B consider if some conditional attribute is essential to keep a crisp or certain rule. CA's member is certified his authentication to his participant under his digital certification When someone else, such as "B", wants to communicate with someone, such as “A” who is a CA’s member, “B” will ask “A” public key from CA. Human behavioral factors of an organization employee that encourage insider security threats are grouped into many topics such as organizational weak security policy, regulation, practicum, employees under job evaluation, cyber loafing, financial concern, criminal record, ideology, etc. These conditional attributes were used to classify insider threat ontology. Each web usage pattern could be used to identify an “IT user” whether he works in normal operation or deception operation
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