Machine Learning Algorithms for Traffic Flows, Network Traffic Anomaly Detection, and Hard Disk Drive Failure

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Abstract:
This paper will discuss about the use of the machine learning algorithms in traffic flows,
network traffic anomaly detection and in hard disk drive failure. In this paper the machine
learning algorithms will also be compared with each other and in the conclusion part the
worst and best algorithm will be discussed.

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Introduction:
In so many disciplines of the science, the major objective is to make a model of the
relationship that is between the input and output. Where the input means is the set of
quantities that are observable and the output means one more set of the variables which is
related with these. The time when, a mathematical model is being determined, the prediction
of value of the variables that are desired is possible by doing the measurement of the
observables. In other words, the machine learning is one of the app lications of the Artificial
Intelligence which offers the system, the ability to learn automatically [1]. A sytem where the
machine learning is implemented is ready to learn new thing whenever it identifies some new
patterns in the data. To select the suitable algorithm is the major part of any project that is
related to the machine learning. This paper will discuss about the use of the machine learning
algorithms in traffic flows, network traffic anomaly detection and in robot hard disk drive
failure. In this paper the machine learning algorithms will also be compared with each other
and in the conclusion part the worst and best algorithm will be discussed.
IP Traffic flows:
The traffic state detection is done conventionally by using the sensors that are point
based that include the microwave radars. The researchers have developed several
mechanisms for detecting the congestion through comparing the measures from the loops that
are inductive across several locations. The use of suitable algorithms and to classify the flows
of the traffic correctly is so much important. The algorithms that has been used to clarify the
traffic flows are given below:
Native Bayes:
The classification of the native Bayes uses the theorem of Bayes for classifying and
predicting the labels for the information and data. Assigning of the algorithm is based on the

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posterior probability of the vector that is estimated maximum [2]. Each of the features are
considered by it, that to be more independent of each other where the original class of it is
given.
Figure: Feature Comparison of Natıve Bayes Algorithm
Decision tree classifier:
One more non parametric method that is belonged to the machine learning is that it
predicts value that is called the leaves of a tree, from a feature set which is called as the
branches that lead the leaves, can be defined as the decision tree classifier [3]. Through
segregating set of the sources into one subset of the set of attribute, the tree is being learned
that is divided further into one more subset in a manner that is recursive. The major
advantage of this method is, the part of the visualization.
Anomalies→ Test ↓ Present Absent
Positive True Positive False Positive
Negative False Negative False Positive