|dc.description.abstract||Worldwide, the Internet is connected across the country. There are threats of network
attacks in this Internet environment. The risk of integrity and con dentiality
has also increased with the density of information and global reach. Security breach
has become too easy. In these days the improvement of the network security is
therefore highlighted. Protection of the Network allow the unintended interference
to some form to network and avoid it. It consists of software for network intrusion
detection that track the network. NIDS is positioned in the network in a strategic
location to track tra c inside the network from source to destination apps.
The machine would optimally screen both inbound and outbound tra c, but that
would create a congestion that would hinder the system's overall pace. Finally,
these methods include machine learning algorithms that render the device
and deliver reliable performance. Intrusion activities leave evidence in the auditing
data, so it is possible to learn and distinguish the pattern of ordinary and malicious
activities with machine learning algorithms.
Machine training techniques can learn normal, anomalous patterns from training
data, and create classi ers for computer system attacks. In the area of intrusion
detection for our research works, machine learning methods, such as logistic regression,
Naive Bayes, K-Nearest Neighbor and Decision Trees were used. The research
provides a predictive computational approach to optimize intrusion detection in the
Network Tra c Data along with implementing di erent methodologies for the evaluation
of the best accuracy from the Classi cation and Deep - Learning Algorithms.
A new intrusion intrusion detection system for smart networks has been developed
using a two-stage distinction (Anomaly-Misuse) and a deep methodology for learning.
As detection methods to identify tra c disruption that could be attacked,
the Decision Tree, logistic regression, KNN and Naive Bayes were used and Deeplearning
used an ANN method that would recognize the attacks as they exist. The
analysis has used the complete 42 dimensional features of the training data set. The
ndings indicate that the high accuracy values are 100% with 0.10 recall levels at
stage 1 of the appraisal, and 99.5% with 0.99 at stage 2 of the validation. Experimental
ndings indicate that the design of the decision tree contributes to high
precision in contrast with other algorithm.||en