scholarly journals Intrusion Detection System using Machine Learning

2020 ◽  
pp. 61-71
Author(s):  
Jayesh Zala ◽  
Aditya Panchal ◽  
Advait Thakkar ◽  
Bhagirath Prajapati ◽  
Priyanka Puvar
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Technique ◽  
Software Application ◽  
Malicious Activity ◽  
The Past ◽  
Learning Technique

Intrusion Detection System (IDS) is a tool, or software application, that monitors network or system activity and detects malicious activity occurring. The protected evolution of the network must incorporate new threats and related approaches to avoid these threats. The key role of the IDS is to secure resources against the attacks. Several approaches, methods and algorithms of the intrusion detection help to detect a plethora of attacks. The main objective of this paper is to provide a complete system to detect intruding attacks using the Machine Learning technique which identifies the unknown attacks using the past information gained from the known attacks. The paper explains preprocessing techniques, model comparisons for training as well as testing, and evaluation technique.

scholarly journals Flow Based Intrusion Detection System for Software Defined Networking using Hybrid Machine Learning Technique

2019 ◽  
Vol 9 (2S2) ◽  
pp. 1026-1033
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Software Defined Networking ◽  
Machine Learning Technique ◽  
Security Issues ◽  
Learning Technique ◽  
Flow Information ◽  
Hybrid Machine

Software Defined Networking and OpenFlow protocol have been recently emerged as dynamic and promising framework for future networks. Even though, programmable features and logically centralized controller leads to large number of security issues. To address the security problems, we have to impose Intrusion Detection System module to continuously keep track of the network traffic and to detect the malicious activities in the SDN environment. In this paper, we have implemented flow-based IDS with the help of hybrid machine learning technique. By collecting the flow information from the controller, we classify the traffic, extract the essential features and classify the attack using machine learning based classifier module. For classifier, we have developed hybrid machine learning model with the help of Modified K-Means and C4.5 algorithm. Our proposed work is compared with single machine learning classifier and our experimental results show that, proposed work can classify the normal and attack instances with accuracy of 97.66%.

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

scholarly journals Explanation Framework for Intrusion Detection

2020 ◽  
pp. 83-91
Author(s):  
Nadia Burkart ◽  
Maximilian Franz ◽  
Marco F. Huber
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
False Positives ◽  
Human Reasoning ◽  
Malicious Activity ◽  
Specific Policy ◽  
Decision Boundaries

AbstractMachine learning and deep learning are widely used in various applications to assist or even replace human reasoning. For instance, a machine learning based intrusion detection system (IDS) monitors a network for malicious activity or specific policy violations. We propose that IDSs should attach a sufficiently understandable report to each alert to allow the operator to review them more efficiently. This work aims at complementing an IDS by means of a framework to create explanations. The explanations support the human operator in understanding alerts and reveal potential false positives. The focus lies on counterfactual instances and explanations based on locally faithful decision-boundaries.

A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks

2021 ◽  
pp. 209-230
Author(s):  
Keshav Sinha
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Detection System ◽  
Supervised Machine Learning ◽  
The Internet ◽  
Machine Learning Technique ◽  
Network Intrusion ◽  
Learning Technique ◽  
Enormous Amount ◽  
Static Checking

During this time, COVID-19 has affected the lifestyles of many individuals; in the meantime, an enormous amount of users are connected with the internet. This will also increase the chance of network intrusion due to congestion and overloading of the server. So, to cope with this problem, the authors proposed an automated intrusion detection system (IDS) which helps in monitoring the traffic and service request. The model is used to identify the illegal access and counterparts with static checking capabilities of the firewall. The classical KDDCup 99 dataset is used for training and testing purposes.

The Role of Data Mining in Intrusion Detection Technology

2008 ◽  
pp. 463-473 ◽  
Author(s):  
Amalia Agathou ◽  
Theodoros Tzouramanis
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Real Time ◽  
Computer System ◽  
Intrusion Detection System ◽  
Detection System ◽  
The Internet ◽  
The Past ◽  
Detection Technology

Over the past few years, the Internet has changed computing as we know it. The more possibilities and opportunities develop, the more systems are subject to attack by intruders. Thus, the big question is about how to recognize and handle subversion attempts. One answer is to undertake the prevention of subversion itself by building a completely secure system. However, the complete prevention of breaches of security does not yet appear to be possible to achieve. Therefore these intrusion attempts need to be detected as soon as possible (preferably in real time) so that appropriate action might be taken to repair the damage. This is what an intrusion detection system (IDS) does. IDSs monitor and analyze the events occurring in a computer system in order to detect signs of security problems. However, intrusion detection technology has not yet reached perfection. This fact has provided data mining with the opportunity to make several important contributions and improvements to the field of IDS technology (Julisch, 2002).

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