ICT and Society

Volume 431 of the series IFIP Advances in Information and Communication Technology pp 280-292

Human Perception of the Measurement of a Network Attack Taxonomy in Near Real-Time

  • Renier van HeerdenAffiliated withDefence Peace Safety & Security, Council for Industrial and Scientific Research
  • , Mercia M. MalanAffiliated withInformation and Computer Security Architecture Research Group, University of Pretoria
  • , Francois MoutonAffiliated withDefence Peace Safety & Security, Council for Industrial and Scientific Research
  • , Barry IrwinAffiliated withComputer Science Department, University of Rhodes

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This paper investigates how the measurement of a network attack taxonomy can be related to human perception. Network attacks do not have a time limitation, but the earlier its detected, the more damage can be prevented and the more preventative actions can be taken. This paper evaluate how elements of network attacks can be measured in near real-time(60 seconds). The taxonomy we use was developed by van Heerden et al (2012) with over 100 classes. These classes present the attack and defenders point of view. The degree to which each class can be quantified or measured is determined by investigating the accuracy of various assessment methods. We classify each class as either defined, high, low or not quantifiable. For example, it may not be possible to determine the instigator of an attack (Aggressor), but only that the attack has been launched by a Hacker (Actor). Some classes can only be quantified with a low confidence or not at all in a sort (near real-time) time. The IP address of an attack can easily be faked thus reducing the confidence in the information obtained from it, and thus determining the origin of an attack with a low confidence. This determination itself is subjective. All the evaluations of the classes in this paper is subjective, but due to the very basic grouping (High, Low or Not Quantifiable) a subjective value can be used. The complexity of the taxonomy can be significantly reduced if classes with only a high perceptive accuracy is used.


Network Attack near real-time Network Attack Taxonomy