Near optimal attack strategies for the maximization of information theft
Journal
WMSCI 2007 - The 11th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 13th International Conference on Information Systems Analysis and Synthesis, ISAS 2007
Journal Volume
2
Pages
140-145
Date Issued
2007
Author(s)
Abstract
With the prevalence and varied applications of the Internet, new cyber-crimes are mushrooming all over cyberspace. The crimes are characterized by their "silent" attack behavior, which enables an attacker to exploit the vulnerabilities of a system and steal information, without actually crashing the system. Information theft is a relatively new cyber-crime that not only causes property damage and monetary loss to its victims, but can also ruin their reputations. To detect and analyze the serious impact of information theft, we model it as a mathematical programming problem, defined by the AS model. In the model, an attacker applies his limited attack power intelligently to the targeted network in order to steal as much valuable information as possible. A Lagrangean relaxation-based algorithm is adopted to solve the AS problem, and the "susceptibility" metric is used to evaluate the effect of the attack.
Subjects
Information Theft; Lagrangean Relaxation; Network Attack; Optimization Problem; Resource Allocation; Scale-free Networks
SDGs
Other Subjects
Information thefts; Lagrangean Relaxation; Network attack; Optimization problems; Scale free networks; Crime; Cybernetics; Economics; Information science; Information systems; Management information systems; Mathematical programming; Problem solving; Resource allocation; Systems analysis; Computer crime
Type
conference paper