A novel online CEP learning engine for MANET IDS

Petersen, Erick; To, Marco Antonio; Maag, Stéphane

doi:10.1109/latincom.2017.8240196

Cited by 5 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides, if these IDSs are centralized, IDSs based on previous attack signatures are less effective. Peterson et al [15] based on adding to the detection engine a recent Machine Learning technique that identifies attack traffic online (not to be processed and evaluated after), rewriting IDS rules on the fly [15]. A two-level monitoring method for detecting malicious nodes in MANETs is being proposed by Amouri et al dedicated sniffers operating in promiscuous mode are installed at the first stage.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

Abdan

Seno

2021

Preprint

View full text Add to dashboard Cite

A wormhole attack is a type of attack on the network layer which reflects the issue of routing protocols. The classification is performed with several methods of machine learning consisting of K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Linear Discrimination Analysis (LDA), Naive Bayes (NB), and Convolutional neural network (CNN). Moreover, for feature extraction, we used the properties of nodes, especially nodes speed in the MANET. We have collected 3997 distinct (normal 3781 and malicious 216) samples that comprise normal and malicious samples. Results of the classification show that the accuracy of KNN, SVM, DT, LDA, NB, and CNN methods are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, and 96.4%, respectively. Based on our findings, the DT method's accuracy is 98.9% and higher than other methods. In the next priority, SVM, KNN, CNN, LDA, and NB indicate high accuracy, respectively.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…The findings have shown that the negligence by IDS algorithms of the reputed packet falling nodes is a significant problem and harms network performance [19] (see Table 1). The approach is that the overhead routing is significantly minimized Petersen et al [15] The online CEP learning engine…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

Abdan

Seno

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Similarly, there are many literatures that emphasize that learning-based approaches offers more edge to the security feature in MANET. The work of Peterson et al [64] has used machine learning for presence of intruder on online It is also known that presence of fault also invites various security threats. This logic has been investigated by Sargolzaei et al [65] where the emphasis is towards developing a decision system for supporting identification of system fault in cyber-physical system of vehicular network.…”

Section: Iii) Machine Learning Based Security Solutionmentioning

confidence: 99%

“…Dependent upon training dataset Peterson et al [64] Intrusion detection Support Vector Machine Simplified classification Not resistive towards dynamic attack Sargolzaei et al [65] Fault detection Decision support system Reliable and robust System complexity not assessed.…”

Section: Supports Optimizationmentioning

confidence: 99%

Insights on Security Improvements and Implications of Artificial Intelligence in MANET

T.M.¹,

Shivakumar²

2019

CAE

View full text Add to dashboard Cite

Mobile Adhoc Network (MANET) is claimed to be an integral part of all futuristic network and communication system e.g. cloud, reconfigurable network, Internet-of-Things (IoT), interdomain routing, etc. However, the security problems of MANET is not yet been mitigated irrespective of archives of security approaches and solution. For a MANET system to be integrated with upcoming technologies it is required that their routing system should offer higher level of resistance against all potential threats as well as they should also offer a better flexibility to tailor the security scheme based on demand of the environment, where the mobile nodes are operating. It is also seen that Artificial Intelligence is another big boon which is currently being proliferating at faster speed and there are some segment of research where it is also claimed to contribute towards security system in MANET. Therefore, this paper offers insights to the current state of security system using conventional as well as using Artificial Intelligence to explore about its research gap.

show abstract

“…The Manets in a general medium, altering topology, short of the infrastructure along with the restricted power supply they are also susceptible depending upon the elementary properties. In this Paper the author has discussed about detecting a malevolent node in MANET's by Secure Intrusion Detection System which uses Dynamic Source Routing Protocol, DSRP can be used to avoid the Forged Acknowledgement [11]. In one of the proposed method Dynamic Source Routing procedure is used which is DSRP.…”

Section: Literature Reviewmentioning

confidence: 99%

A Literature Survey on Intrusion Detection System in Manets using Machine Learning Techniques

Fouad¹

2019

IJCA

View full text Add to dashboard Cite

Since, a decade of time Mobile Ad hoc Networks (MANETs) have come with wireless networking technology. Due to its dynamic in nature of MANETs, these are vulnerable to various attacks in all the OSI layers but research shows that in Network layer the attacks are effectively done by intruders. In this survey many of the attacks at Network layer are identified in MANETs by most of the researchers which is outlined in this paper. Mostly, AODV routing protocols and other protocols are used for transferring packets in the direction of the destination. These transferred packets data is then deposited within the log files, to surveillance these routing of packets from these Log files, the techniques used in MANETs are Data mining, Support Vector Machines (SVM), Genetic algorithms (GA) and other Machine learning approaches. Further, the methodologies and techniques proposed for detecting and predicting these attacks from various kinds of intrusions within the MANETS is discussed.

show abstract

A novel online CEP learning engine for MANET IDS

Cited by 5 publications

References 16 publications

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad-Hoc Network (MANET)

Insights on Security Improvements and Implications of Artificial Intelligence in MANET

A Literature Survey on Intrusion Detection System in Manets using Machine Learning Techniques

Contact Info

Product

Resources

About