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SEP 12, 2019
Research Paper

• SMART INTELLIGENT INTRUSION DETECTION
  SYSTEMS(IDS)
• How to build a Deep learning Networks based IDS

Trending Algorithms Coding in Machine Learning,
Engineering Technology
Tags PhD Research Topics Computer Science
Research Artificial Intelligence Machine
Learning Deep Learning Intrusion Detection
Systems Deep Neural Network
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SHORT NOTES
Artificial Intelligence is a recent trend in
machine learning, and Deep Learning is a major
leap forward in terms of technology
AI
Deep Learning performs complicated computations,
thereby making it suitable for large datasets
and big data applications Large Image datasets
can make use of Deep Learning effectively.
However, it can also be useful for IDS
applications
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suggesting better novel alternatives with
appropriate tools, technologies and approaches,
thereby proving their effectiveness in real time
applications.
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DEEP LEARNING
Advanced capabilities in field of machine
learning Imitates the process of the human brain
Generating patterns for making decisions Also
known as DeepNeural network Differ from
conventional neural networks Generating patterns
for making decisions
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LAYERS OF DNN
Input layer
Hidden layer1
Hidden layer2
Hidden layer3
output layer
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DIFFICULTIES OF DNN
Requires computational power, not necessary to
have large amount of data with respect to
textual data. Requires lots of data to the tune
of big data. Take years to understand and
retrieve the data.
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OUTCOME
Improved the speed of training and
classification. Used to collect user data and
managing them. Image processing to detecting
bank frauds over the internet.
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DNN- Feature Selection
Unnecessary features are removed using feature
selection algorithms Results in two outputs
classified into attacks or normal traffic Number
of outputs is not two, it depends on the number
of attacks. Unlabeled attacks sent to classifier,
it identify the attacks with high precision.
Reduced features form hidden layer they are
mapped through individual features of
instances. Multiclass results are available and
represented by an output. Training dataset given
to DNN, the artificial neurons get assigned
solve related solutions. Identify the attacks
with higher accuracy.
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SUMMARY
Neural networks are sometimes not preferred since
they start blankly without any knowledge and
then pass through to get accurate models.
However, this is only applicable for very small
data. Hence, this algorithm can considered
whenever large datasets are considered
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