The choice of pertinent publications in the area of AI-based NIDS is made using a methodical methodology. Future study in this area may focus on proposing an effective NIDS framework with less complicated DL algorithms and detection mechanisms. For #Enquiry: Website URL: https://www.phdassistance.com/blog/systematic-investigation-of-machine-learning-techniques-for-network-intrusion-detection/ India: +91 91769 66446 Email: info@phdassistance.com

1. SYSTEMATIC
INVESTIGATION OF
MACHINE LEARNING
TECHNIQUES FOR
NETWORK INTRUSION
DETECTION
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Phdassistance
Group www.phdassistance.com
Email: info@phdassistance.com

2. Today's Discussion
Introduction
ML algorithms for NIDS
Research challenges
Future trends
Conclusions

3. Introduction
Network security has become a critical research area
due to the current interest in and advancements in
communications and internet technologies over the
past ten years.
It makes use of devices like firewalls, virus protection,
and intrusion detection systems (IDS) to safeguard the
security of a network and all of its connected assets
within a cyberspace. Among these, the network-based
intrusion detection system (NIDS) is the attack
detection method that offers the needed protection by
continuously scanning the network traffic for hostile
and suspicious activity.

4. The researchers have looked into the use of deep learning (DL) and machine learning (ML) approaches
to meet the needs of a successful IDS.
The main goal of ML and DL, which fall under the broad heading of artificial intelligence (AI), is to
extract meaningful information from huge data.
The tremendous growth in network traffic and the related security risks have made it extremely
difficult for NIDS systems to effectively detect malicious intrusions Ahmad et al., (2021).
The study of DL approaches for NIDS is still in its early stages, and there is still a lot of room to analyze
this technique within NIDS to effectively detect network invaders. In order to give a comprehensive
overview of current trends and developments in ML- and DL-based NIDS systems, this research paper
will focus on recent developments in these areas.

6. ML algorithms
for NIDS
One of the fundamental supervised machine learning
(ML) techniques, DT applies a series of judgments to
both classify and predict the dataset (rules). The
structure of the model is that of a typical tree, with
branches, nodes, and leaves. Each node stands for a
characteristic or feature. CART, ID3, and C4.5 are the
three most popular DT models. Numerous decision trees
are used to create many sophisticated learning
algorithms, including XGBoost and Random Forest (RF).
DECISION TREE

7. One of the easiest supervised machine learning
(ML) algorithms, KNN, uses the concept of
"feature similarity" to determine the class of a
given data sample.
It determines a sample's identity based on its
neighbours by figuring out how far away it is
from them. The KNN algorithm's parameter k
has an impact on how well the model performs
Binbusayyis and Vaiyapuri (2021).
K-NEAREST NEIGHBOUR

8. In n-dimensional feature space, the max-margin separation hyper-plane serves as the foundation for
the supervised machine learning method known as SVM.
The two linear and nonlinear issues can be solved using it.
The neurons (nodes) that make up an ANN are the processing units and the connections that link
them. An input layer, numerous hidden layers, and an output layer are how these nodes are arranged.
For the ANN's learning process, the backpropagation method is employed. The ability to execute
nonlinear modelling by training from larger datasets is the fundamental benefit of utilising an ANN
approach.
SUPPORT VECTOR MACHINE
ARTIFICIAL NEURAL NETWORK

9. The fundamental tenet of ensemble methods is that learning should be done collaboratively in order
to benefit from the various classifiers.
Considering that every classifier has its advantages and disadvantages. Some systems may be
effective at spotting a particular kind of attack but perform poorly against other attack types.
Using an ensemble approach, weak classifiers are combined into stronger ones by training many
classifiers, which are then chosen using a voting technique Salih et al., (2021).
ENSEMBLE METHODS

10. The current study brought to light the absence of a current dataset that reflects novel attacks for
contemporary networks.
The systematic creation of a current dataset with sufficient examples of practically all attack types is
one of the research problems for IDS.
The dataset should be regularly updated to reflect the most recent intrusion instances and made
available to the public to aid the research community.
UNAVAILABILITY OF A SYSTEMATIC DATASET
Research challenges

11. LOWER DETECTION ACCURACY DUE TO IMBALANCE DATASET
The majority of the proposed IDS approaches show lower detection accuracies for some attack types
than the model's overall detection accuracies, according to the current study, which is another important
finding.
LOW PERFORMANCE IN REAL-WORLD ENVIRONMENT
The effectiveness of IDS in a real-world setting is another study issue for them. Since the majority of the
suggested approaches are examined and validated in a lab setting utilising openly available datasets
Imrana et al., (2021).

12. RESOURCES CONSUMED BY COMPLEX MODELS
The majority of IDS strategies suggested by the researcher (approximately 80% of methods used were
DL- or DL-ML-based methods) are based on extremely sophisticated models that demand a lot of
processing time and computing resources. The processing unit may experience additional overhead as a
result, which would ultimately have an impact on IDS performance.
LIGHTWEIGHT IDS FOR IOT
IoT networks and the sensor nodes they are connected to can both be secured using an IDS. Sensor
nodes in an IoT system gather a vast amount of vital data that is disseminated online Alzahrani and
Alenazi, (2021).

13. Future trends
The attack characteristics in a dataset should be
updated often by the IDS framework, and the model
should continue to be trained with the upgraded
definitions to enable the model to learn new features.
In the long run, this will help the IDS model detect zero-
day threats more accurately and reduce false alarms.
EFFICIENT NIDS FRAMEWORK

14. The detection accuracy will be almost as accurate when only the essential features are chosen as
when the full collection of features is used. As a result, the model will gradually become less complex
and will require less real-time computer power.
Researchers can also experiment with the hybrid approach of employing ML for classification and DL
for feature extraction. As a result, the proposed model will be simpler.
It is necessary to have an effective and intelligent NIDS that can identify intrusions within networks
that support UAVs. The use of AI in NIDS for UAV-enabled systems has the potential to be a
fascinating study area, but it needs additional exploration and development.
SOLUTION TO COMPLEX MODELS
USE OF DL ALGORITHMS
EFFICIENT NIDS FOR CYBER-PHYSICAL SYSTEMS

15. Conclusions
To offer new researchers access to the most recent information, trends, and advancements in the
area, this paper offers a thorough analysis of network intrusion detection systems based on ML and
DL methodologies.
The choice of pertinent publications in the area of AI-based NIDS is made using a methodical
methodology. Future study in this area may focus on proposing an effective NIDS framework with less
complicated DL algorithms and detection mechanisms.
With the use of this knowledge, we will create a cutting-edge, portable, and effective machine
learning- based NIDS in the future that will successfully identify network intruders.

16. GET IN TOUCH
+44 7537144372
UNITED KINGDOM
+91-9176966446
EMAIL
INDIA
info@phdassistance.com