Abstract
This article is a consideration on computer network intrusion detection using artificial neural networks, or whatever else using machine learning techniques. We assume an intrusion to a network is like a needle in a haystack not like a family of iris flower, and we consider how an attack can be detected by an intelligent way, if any.
Highlights
The parachute drop went smoothly ... slithering down the chute and out into space
When we are to design a network intrusion detection system, which is one of the hottest topics these days, by means of so-called a soft computing such as artificial immune system, fuzzy logic, evolutionary computations, neural networks, whatever it might be, we need a set of sample data to train the system and to test the system afterwards
There have been fair amount of studies in which this iris flower database is employed as a dataset to train and to test the intrusion detection system
Summary
With her knees bent and her arms tucked into her sides as she fell to the ground She folded her parachute into a neat bundle, set out to find the other Jackdaws. Question is, "How many average trials-and-errors will be needed for a non-owner to know the PIN under a specific strategy?". This might be reminiscent of the famous problem called a-needle-in-a-haystack which was originally proposed by Hinton & Nowlan in 1987 [1]. We assume that TCP connections to a computer network are represented with n-dimensional vectors and those represented by intrusions are like needles among huge amount of normal transactions which might look like a haystack or pastoral
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