Detecting malicious tweets in twitter using runtime monitoring with hidden information
Loading...
Authors
Yilmaz, Abdullah
Subjects
formal specifications
hidden Markov model
hidden data
twitter
runtime verification
runtime monitoring
statechart assertions
hidden Markov model
hidden data
runtime verification
runtime monitoring
statechart assertions
Advisors
Drusinsky, Doron
Date of Issue
2016-06
Date
Jun-16
Publisher
Monterey, California: Naval Postgraduate School
Language
Abstract
Although there is voluminous data flow in social media, it is still possible to create an effective system that can detect malicious activities within a shorter time and provide situational awareness. This thesis developed patterns for a probabilistic approach to identify malicious behavior by monitoring big data. We collected twenty-two thousand tweets from publicly available Twitter data and used them in our testing and validation processes. We combined deterministic and nondeterministic approaches to monitor and verify the system. In the deterministic part, we determined assertions by using natural language (NL) and associated formal specifications. We then specified visible and hidden parameters, which are used for subsequent identification of hidden parameters in Hidden Markov Model (HMM) techniques. In the nondeterministic part, we used probabilistic formal specifications with visible and hidden parameters, used in HMM, to monitor and verify the system. An important contribution of the work is that we specified some event patterns indicating malicious activities. Based on these patterns, we obtained output to indicate the possibility of each tweet being malicious.
Type
Thesis
Description
Series/Report No
Department
Computer Science
Organization
Identifiers
NPS Report Number
Sponsors
Funder
Format
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
Copyright is reserved by the copyright owner.