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Analysis of Network Intrusion Detection System with Machine Learning Algorithms


Analysis of Network Intrusion Detection System with Machine Learning Algorithms

The aim of this research paper is to give the approaches to identify the network intrusion attack to prevent the attacks with the use of Machine Learning algorithm.

The Network Intrusion Detection System must be able in identifying the traffic and the abnormal patterns using the detecting, monitoring and responding technique to the unauthorized activities in the system. In this context the unbalanced and huge dataset, the intrusion detection system using machine learning algorithm will encounter the issues with the data processing.

Intrusion Detection System is the software application for detecting the network intrusion using the machine learning algorithm. The intrusion detection system will monitor the system or the network for the malicious attacks and this also protects the computer network from the unauthorized access of the users, which includes the insider threat. Patgiri et al. (2018), built the predictive model using the intrusion detector with the help of Machine Learning, which will be capable of differentiating between the good connections and the bad connections such as attacks or intrusion.

Several types of researches are there, which introduced the intrusion detection system. With the emerge of the machine learning, the old technique has become obsolete and complex in dealing with the big data. Using the machine learning algorithm for producing the accurate and high-speed intrusion detection system can be easy. Dang (2019) has showed in his research that the issue of the intrusion detection with the CICIDS2012 is solved with the ensemble machine learning algorithm effectively with the small training dataset. He also shows that by utilizing the sample detection technique based on the machine learning algorithm can improve the predictive performance of the simple learning algorithm such as Naïve Bayes.

Janagam and Hossen (2018), implemented the NIDS, which is placed outside of the firewall of the system, where the external traffic is monitored by detecting and sensing the malicious activity. While being in the complex network, they connected the device to the 1000 nodes. Because of the network complexity, this came out of the effective method, for opting for the NIDS for keeping the track of modifying and changing of the network environment that belongs to the effectiveness as the IDS in the network that can compromise of the sensitive or confidential data.

By the traditional network intrusion detection and prevention method such as encryption, access control and firewall, have some limitations to protect the system and the network from the advance network attacks. The system developed based on the techniques, which may suffer from the high false negative and high positive detection rates and there can be lack of the adapting to the malicious behaviours (Almseidin et al. 2017). However, the machine learning algorithm can be applied to the issues of the network intrusion detection with the improvement of the adaptability and detection rates.

Problem Statement

Machine learning algorithm can be used for keeping the network attacks knowledge based comprehensive and up-to-dated (Gao et al. 2019). The goal of this research project is to provide the efficiency and the performance of the machine learning algorithm that can be used for redirecting the approaches for learning about the accuracy of the organizational network under certain circumstances.

The objectives of this research study are:

  • To analyse the multiple types of the network attacks
  • To implement the detection strategy using the Machine Learning algorithm
  • To improve the rate of the detection accuracy with the dataset type by the Machine Learning algorithm

For this research study, the secondary research method will be used as the secondary research method involves the existing data related to the research topic, therefore, for conducting this study, not much time will be required. The existing data collected from the online articles, journals, books and PDFs will be summarized and organized for increasing the effectiveness of the research. With the help of secondary research, the research will be easy and the research team will be able to collect the relevant data from various authentic resources (Snyder 2019).

This method is chosen because this method is more cost effective than the primary research as the research team will make use of the existing research data. For this study, also primary data collection method will be used using the practical environment that is Python. Using Python, the analysis of the intrusion detection system will be done and real time data will be collected. In the design stage of the intrusion detection system using machine leaning algorithm, the effective design will be planned with the required steps and will be implemented for achieving the objectives of this project as this has the complex and practical environment.

The technical model of this system will be designed for providing the overview of all the features and the functionalities. This model will be described with the necessary diagrams and pseudo code for helping the users to understand the architecture of the project. Using Python, the reinforcement algorithms will be created as well as combined for working together with the network intrusion detection system for obtaining the optimal policy as well as detecting the intrusion of the network and for saving the resources of the system (Mishra et al.  2018).

The technical model of the system with machine learning will be described in some required steps such as system monitoring and decision with the change in network intrusion detection system such as updating and recording the attributes and variables. With the machine learning algorithm, this will be vital to find out the cause of the network intrusion. In this way, this is very significant for monitoring the resources parameters of the system like RAM and CPU for finding out the metric of the resources, which could be more vulnerable, which will make the packets for being intruded or dropped that can be the active attack and can cause the fabrication, modification and interruption (Wahyudi, Ramli and Murfi 2018). In addition to this, the passive attacks such as traffic analysis and release of the message content can also be created.


The most significant prerequisites, which should be in order before executing the implementation of the machine learning algorithms will be finding the resource parameter of the current system for monitoring the deployment and the other parts of underlying infrastructure. The signature pattern matching to the system will be the intrusion detection method in the software where the data of the network will be analysed and compared with the familiar attacks, which will be saved in the database (Sharma, Kalita and Borah 2016). In the pursuance of developing the environment using machine learning algorithm as well as implementing the testing process, wide range of tools will be used and will be combined such as configuring and installing the environment such as Python, which will be used as a scripting language to automate the experiment as well as extract the data.

Using the machine learning algorithm in Python, the database of the system will be designed and then the registration part will be developed, where the user will register themselves for accessing the system, then the retrieval process of the data will be developed. After all these processes, the validation of the system will be done and will be monitored and tested to check whether the system is working properly or not.

Various articles and journals are covered while collecting information for conducting this research that is relevant to network intrusion detection using machine learning technique. All the articles and journals chosen for this research study focused on the qualitative and quantitative both methods. However, for this particular study, the research team has chosen the qualitative method as there is no mathematical calculation related to this research in this study. All the chosen articles, books and journals are original and genuine and provided the proper and adequate data for conducting the research study.

Various sources have been used inn this study, where adequate information and knowledge have been provided related to the network intrusion detection and machine learning. For this study, some data have been collected from the “International Conference on Future Data and Security Engineering” by Springer Cham, which is one of the most authentic and genuine sources of getting data. In this research study, IEEE resource has been used as this is the another most popular resource for getting knowledge.

As the research study is conducted by the analysis of the intrusion detection system with the help of machine learning there are various connectivity and resources have been used. This can be improper to hold the system and the data used in the protected network. Therefore, this will be suitable for using the proper algorithm for transiting the data gathered from the network and the system.

This is very significant for this research study to determine the scope before conducting the research and collecting the data. Those data should be used for the project purpose only and should be removed from the system after completion of the project.

This research can be conducted by the target organization and there can be potential conflict among the needs of the organization. Therefore, this research study should be objective and independent (Clark et al.  2019). For this research study, the research team must have to adhere and obey the ethical aspects of the software MS Project where the project timeline has been created. ‘

While gathering the data for the research, the research team needs to keep in mind about the copyrights and the trademark law, which should not be impinged while collecting the data. In addition to this several security concerns can be there such as loss of data and data breach attack, which should be prevented using proper method and techniques and there must be security measures, which should be followed by every team member.

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