Neural Networks
In the
field of computer science, Neural Networks refers to as an Artificial Neural
Network (ANN), a definition provided by Dr. Robert Hecht-Nielsen, the inventor
of neurocomputers.
ANNs
models are based on the neuronal structure of the mammalian
cerebral cortex to include hundreds of processors versus billions of neurons in
a mammalian brain. One of the first applications of neurocomputing is the retina
scan and identification. However, the main purpose of this post is to introduce
you the importance of neurocomputing in Neuro Networks and its impact on malware
intrusion detection.
Neural Neworks design
is based on three different parts on layers that include a huge number of
interconnected nodes with "activation function". The input layer
receives patterns that will be introduced to other hidden layers for data
processing and then present them to an output layer, which provides the answer.
As Hecht-Nielsen says, "The neurocomputer is
inherently able to deal with small variations in the data -- with ums and ahs.
It recognizes familiar patterns but is unconcerned about small mismatches".
Hecht-Nielsen
achievement allows Neural Networks to provide solutions to problems where
standard Networks fail " miserably ".
It important
to mention that Neural Networks' Applications are so vast and complicated, but
some of well known research centers were able to advance this technology such
as Ford Aerospace under subcontract to NASA that serve the U.S government, Global
Holonetics that developed "smart
camera" that is capable of inspecting items on an assembly line at a rate
of 15 items per second, and General Dynamics Corp developed a neural network to
identify ships by their sonar signatures.
The technology
will eliminate the need for humans in processing letters, bank receipts done by
hand, and handwriting.
Neural
networks in computers process data in the same manner neural networks in cells do.
They are based on interconnection similar to synapses.
Based on
the above achievements, this technology will bring an extreme power to
detecting malicious codes, in other words, virus's intrusion detection.
A host will have a Self-organizing feature to
represent the behaviors of a system by using "Back Propagation" to
model the intrusive patterns. The host will have the ability to learn and
expand its knowledge about the discovery of malicious intrusions as time goes
on.
Source:
Bibliography:
Applying Neural Network based Approaches to Host based Intrusion Detection: Soft Signatures
https://www.youtube.com/watch?v=aVId8KMsdUU
https://www.youtube.com/watch?v=aVId8KMsdUU
This is an interesting topic. I’m still not sure I can way a Neural Network is or how it works, but I think it’s applications are fascinating. The virus detection aspect is okay I guess, but I think there’s always going to malware and viruses no matter what we do. The really interesting thing in the post, at least to me, was where you mentioned the smart camera and the ship identification applications. I’m sure the inspection of 15 items per second is more, and probably just as thorough, as a human could do. Detecting and identifying ships from sonar sounds is pretty amazing too. I’m ready for the day when computers and AI take care of everything humans need to live comfortably, and if neural networks can help with that, then I hope they get more attention.
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