Implementation of Intrusion Detection System for Internet of Things Using Machine Learning Techniques

2021 ◽  
pp. 217-237
Author(s):  
Jatinder Manhas ◽  
Shallu Kotwal
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Learning Techniques

A robust intrusion detection system using machine learning techniques for MANET

2020 ◽  
Vol 24 (3) ◽  
pp. 253-260
Author(s):  
N. Ravi ◽  
G. Ramachandran
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Mobile Computing ◽  
Mobile Networks ◽  
Intrusion Detection System ◽  
Detection System ◽  
Mobile Network ◽  
Machine Learning Techniques ◽  
Test Bed ◽  
Learning Techniques

Recent advancement in technologies such as Cloud, Internet of Things etc., leads to the increase usage of mobile computing. Present day mobile computing are too sophisticated and advancement are reaching great heights. Moreover, the present day mobile network suffers due to external and internal intrusions within and outside networks. The existing security systems to protect the mobile networks are incapable to detect the recent attacks. Further, the existing security system completely depends on the traditional signature and rule based approaches. Recent attacks have the property of not fluctuating its behaviour during attack. Hence, a robust Intrusion Detection System (IDS) is desirable. In order to address the above mentioned issue, this paper proposed a robust IDS using Machine Learning Techniques (MLT). The key of using MLT is to utilize the power of ensembles. The ensembles of classifier used in this paper are Random Forest (RF), KNN, Naïve Bayes (NB), etc. The proposed IDS is experimentally tested and validated using a secure test bed. The experimental results also confirms that the proposed IDS is robust enough to withstand and detect any form of intrusions and it is also noted that the proposed IDS outperforms the state of the art IDS with more than 95% accuracy.

scholarly journals Highly accurate and efficient two phase-intrusion detection system (TP-IDS) using distributed processing of HADOOP and machine learning techniques

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Abhijit Dnyaneshwar Jadhav ◽  
Vidyullatha Pellakuri
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Phase Ii ◽  
Intrusion Detection System ◽  
Detection System ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Network Connections ◽  
Learning Techniques

AbstractNetwork security and data security are the biggest concerns now a days. Every organization decides their future business process based on the past and day to day transactional data. This data may consist of consumer’s confidential data, which needs to be kept secure. Also, the network connections when established with the external communication devices or entities, a care should be taken to authenticate these and block the unwanted access. This consists of identification of the malicious connection nodes and identification of normal connection nodes. For that, we use a continuous monitoring of the network input traffic to recognize the malicious connection request called as intrusion and this type of monitoring system is called as an Intrusion detection system (IDS). IDS helps us to protect our network and data from insecure and malicious network connections. Many such systems exists in the real time scenario, but they have critical issues of performance like accuracy and efficiency. These issues are addressed as a part of this research work of IDS using machine learning techniques and HDFS. The TP-IDS is designed in two phases for increasing accuracy. In phase I of TP-IDS, Support Vector Machine (SVM) and k Nearest Neighbor (kNN) are used. In phase II of TP-IDS, Decision Tree (DT) and Naïve Bayes (NB) are used, where phase II is the validation phase of the system for increasing accuracy. Also, both the phases are having Hadoop distributed file system underlying data storage and processing architecture, which allows parallel processing to increase the speed of the system and hence achieve the efficiency in TP-IDS.

scholarly journals Building a Intrusion Detection System for IoT Environment using Machine Learning Techniques

2020 ◽  
Vol 171 ◽  
pp. 2372-2379 ◽  
Author(s):  
K.V.V.N.L. Sai Kiran ◽  
R.N. Kamakshi Devisetty ◽  
N. Pavan Kalyan ◽  
K. Mukundini ◽  
R. Karthi
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Review on Intrusion Detection System using Artificial Intelligence Approach

2018 ◽  
Vol 4 (8) ◽  
pp. 6
Author(s):  
Apoorva Deshpande
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Computational Intelligence ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Research Progress ◽  
High Detection Rate ◽  
Learning Techniques

Today, intrusion detection system using the neural network is an interested and considerable area for the research community. The computational intelligence systems are defined on the basis of the following parameters: fault tolerance and adaptation; adaptable the requirements of make a better intrusion detection model. In this paper, provide an overview of the research progress using computational intelligence to the problem of intrusion detection. The goal of this paper summarized and compared research contributions of Intrusion detection system using computational intelligence and neural network, define existing research challenges and anticipated solution of machine learning. Research showed that application of machine learning techniques in intrusion detection could achieve high detection rate. Machine learning and classification algorithms help to design "Intrusion Detection Models" which can classify the network traffic into intrusive or normal traffic. This paper discusses some commonly used machine learning techniques in Intrusion Detection System and also reviews some of the existing machine learning IDS proposed by researchers at different times.

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