Detection of Wormhole, Blackhole and DDOS Attack in MANET using Trust Estimation under Fuzzy Logic Methodology

Khare, Ashish; Rana, J. L.; Jain, Rachit

doi:10.5815/ijcnis.2017.07.04

Cited by 18 publications

(4 citation statements)

References 13 publications

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“…Accordingly, DDoS attack detection technology solutions are required to be well adapted and have relatively low system resources and occupation when deployed. Based on the above considerations, it is feasible to construct a multi-agent system based solution to accomplish DDoS attack detection [8][9][10].…”

Section: Multi-agent-based Ddos Attack Detection Methodsmentioning

confidence: 99%

A Multi-agent System-based Method of Detecting DDoS Attacks

Zhang¹,

Zhang²,

Altaf³

et al. 2018

IJCNIS

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Abstract-Distributed denial of service attacks are the acts aiming at the exhaustion of the limited service resources within a target host and leading to the rejection of the valid user service request. During a DDoS attack, the target host is attacked by multiple, coordinated attack programs, often with disastrous results. Therefore, the effective detection, identification, treatment, and prevention of DDoS attacks are of great significance. Based on the research of DDoS attack principles, features and methods, combined with the possible scenarios of DDoS attacks, a Multi-Agent System-based DDoS attack detection method is proposed in this paper to implement DDoS attack detection for high-load communication scenarios. In this paper, we take the multi-layer communication protocols into consideration to carry out categorizing and analyzing DDoS attacks. Especially given the high-load communication scenarios, we make an effort to exploring a possible DDoS attack detection method with employing a target-driven multi-agent modeling methodology to detect DDoS attacks relying on considering the inherent characteristics of DDoS attacks. According to the experiments verification, the proposed DDoS attack detection method plays a better detection performance and is less relevant with the data unit granularity. Meanwhile, the method can effectively detect the target attacks after the sample training. The detection scheme based on the agent technology can reasonably perform the pre-set behaviors and with good scalability to meet the follow-further requirements of designing and implementing the prototype software.

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Section: Multi-agent-based Ddos Attack Detection Methodsmentioning

confidence: 99%

A Multi-agent System-based Method of Detecting DDoS Attacks

Zhang¹,

Zhang²,

Altaf³

et al. 2018

IJCNIS

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show abstract

“…The Fuzzy logic methodology [17] uses the AODV protocol to evaluate different kinds of attacks. Among the attacks, the DDoS attack is identified, based on the transfer speed of data packets, loss of data packets and the delivery ratio.…”

Section: Related Workmentioning

confidence: 99%

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Pajila¹,

Julie

Robinson

2021

Wireless Pers Commun

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Wireless sensor networks (WSN) is considering as one of the exploring technology.WSN has a large number of sensor nodes, which sense the environment and collect the data. The collected data are sending to the sink through the intermediate nodes. Since the sensors node data are exposed to the internet, there is a possibility of vulnerability in the WSN. The common attack that affects most of the sensor nodes is the DDoS attack. In this paper aims to identify the DDoS attack quickly and to recover sensors using the fuzzy logic mechanism. In the Fuzzy based DDoS attack Detection and Recovery mechanism (FBDR) method uses type 1 fuzzy-logic to detect the occurrence of DDoS attack in a node. Similarly fuzzy-type 2 is used for recovery DDoS attack.Both the type 1 fuzzy-based rule and type 2 fuzzy-based rule perform well in terms of identifying the DDoS attack and recover the DDoS attack. It also helps to reduce the energy consumption of each node and improves the lifetime of the network. The proposed FBDR scheme is compared with other related schemes. The experimental results represent that the FBDR method works better than other similar schemes.

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“…The victim device acts as an attacker and can infect another device. In many mobile networks, this type of attack targets the routing protocols to break routes between nodes in mobile ad hoc network (MANET) networks [18].…”

Section: ) Distributed Denial Of Service(ddos)mentioning

confidence: 99%

Exploring Deep Neural Network Capability for Intrusion Detection Using Different Mobile Phones Platforms

Alharbi¹,

Hewahi²

2021

IJCDS

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Network intrusion activities started since the network typologies become available for public, the target devices were computers, servers, switches, routers and so on, but with the fever of smartphones spreading among the public, these smartphones have become a target for hacker attacks. Network Intrusion Detection System (IDS) is a device, software or both used by network administrator to monitor the network security and recognize any malicious activity or organization's network policy violation. Deep neural network is the famous technology in deep learning where it is an artificial neural network with multiple hidden layers, and it simulates the human brain and it's the central nervous system in thinking and detecting pattern. Mobile phones are widely used today, with different platforms which known now as smartphones, these smartphones belong to many manufacturing and require operating system. This research explores deep neural networks capability for intrusion detection using different mobile phones platforms, the research experiments five deep neural networks approaches, Fully Connected Deep Neural Networks (FCDNN), Convolutional Neural Networks (CNN), Convolutional Neural Networks followed by Fully Connected Neural Networks (CNN&FCDNN), Convolutional Neural Networks followed by Fully Connected Neural Networks and then followed by Convolution Neural Network (CNN&FCDNN&CNN), and finally Fully Connected Deep Neural Networks followed by Convolution Neural Networks (FCDNN&CNN).Two data sets have been used for testing the approaches, Android data set and NSL-KDD data set. The proposed approaches have been compared with each other's and also compared with traditional Machine Learning (ML) approaches. The results show that CNN&FCDNN&CNN is the best deep learning approach where it achieved accuracy of 0.863 and 0.997 for the two data sets Android and NSL-KDD respectively. The accuracy results also show that the best approach is the random forest where it achieved 0.88 and 0.998 for Android and NSL-KDD data sets respectively. Deep neural networks show that they are good machine learning candidates for problems similar to mobile phones intrusion detection systems.

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Detection of Wormhole, Blackhole and DDOS Attack in MANET using Trust Estimation under Fuzzy Logic Methodology

Cited by 18 publications

References 13 publications

A Multi-agent System-based Method of Detecting DDoS Attacks

A Multi-agent System-based Method of Detecting DDoS Attacks

FBDR-Fuzzy Based DDoS Attack Detection and Recovery Mechanism for Wireless Sensor Networks

Exploring Deep Neural Network Capability for Intrusion Detection Using Different Mobile Phones Platforms

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