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ABSTRACT
Cyber-attack, via cyberspace, targeting an enterprise’s use of cyberspace for the
purpose of disrupting, disabling, destroying, or maliciously controlling a computing
environment/infrastructure; or destroying the integrity of the data or stealing controlled
information. The state of the cyberspace portends uncertainty for the future Internet
and its accelerated number of users. New paradigms add more concerns with big data
collected through device sensors divulging large amounts of information, which can be
used for targeted attacks. Though a plethora of extant approaches, models and
algorithms have provided the basis for cyber-attack predictions, there is the need to
consider new models and algorithms, which are based on data representations other
than task-specific techniques. However, its non-linear information processing
architecture can be adapted towards learning the different data representations of
network traffic to classify type of network attack. In this paper, we model cyber-attack
prediction as a classification problem, Networking sectors have to predict the type of
Network attack from given dataset using machine learning techniques. The analysis of
dataset by supervised machine learning technique (SMLT) to capture several
information’s like, variable identification, uni-variate analysis, bi-variate and multi-
variate analysis, missing value treatments etc. A comparative study between machine
learning algorithms had been carried out in order to determine which algorithm is the
most accurate in predicting the type cyber-Attacks. We classify four types of attacks
are DOS Attack, R2L Attack, U2R Attack, Probe attack. The results show that the
effectiveness of the proposed machine learning algorithm technique can be compared
with best accuracy with entropy calculation, precision, Recall, F1 Score, Sensitivity,
Specificity and Entropy.
