Intrusion Detection in Ad Hoc Network Using Machine Learning Technique

2020 ◽  
pp. 60-71
Author(s):  
Mahendra Prasad ◽  
Sachin Tripathi ◽  
Keshav Dahal
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Machine Learning Technique ◽  
Learning Technique

A New Intrusion Detection Method Based on Machine Learning in Mobile Ad Hoc NETwork

2014 ◽  
Vol 548-549 ◽  
pp. 1304-1310
Author(s):  
Lai Cheng Cao ◽  
Wei Han ◽  
Sheng Dong
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Ad Hoc Network ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
False Negative ◽  
False Negative Rate ◽  
Area Of Interest ◽  
Negative Rate ◽  
Mobile Ad Hoc

In a Mobile Ad hoc NETwork (MANET), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder (s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. Unfortunately, many anomaly intrusion detection systems (IDS) take on higher false alarm rate (FAR) and false negative rate (FNR). In this paper, we propose and implement a new intrusion-detection system using Adaboost, a prevailing machine learning algorithm, and its detecting model adopts a dynamic load-balancing algorithm, which can avoid packet loss and false negatives in high-performance severs with handling heavy traffic loads in real-time and can enhance the efficiency of detecting work. Compared to contemporary approaches, our system demonstrates an especially low false positive rate and false negative rate in certain circumstances while does not greatly affect the network performance.

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.

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%.

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