Detection and Prevention of Flooding Attack Using SVM

Patel, M S Sunitha; Sharma, Sanjay; Sharan, Deep

doi:10.1109/csnt.2013.116

Cited by 17 publications

(8 citation statements)

References 7 publications

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“…In [21], Patel proposed the use of support vector machine (SVM) algorithm for detecting and preventing flooding attacks. The behavior of every node is collected and passes to the support vector machine to decide if a node is malicious based on a threshold limit.…”

Section: On Machine Learning Approachmentioning

confidence: 99%

FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks

Ngoc

Tú

Hoang

2019

Wireless Communications and Mobile Computing

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Request route flooding attack is one of the main challenges in the security of Mobile Ad Hoc Networks (MANETs) as it is easy to initiate and difficult to prevent. A malicious node can launch an attack simply by sending an excessively high number of route request (RREQ) packets or useless data packets to nonexistent destinations. As a result, the network is rendered useless as all its resources are used up to serve this storm of RREQ packets and hence unable to perform its normal routing duty. Most existing research efforts on detecting such a flooding attack use the number of RREQs originated by a node per unit time as the threshold to classify an attacker. These algorithms work to some extent; however, they suffer high misdetection rate and reduce network performance. This paper proposes a new flooding attacks detection algorithm (FADA) for MANETs based on a machine learning approach. The algorithm relies on the route discovery history information of each node to capture similar characteristics and behaviors of nodes belonging to the same class to decide if a node is malicious. The paper also proposes a new flooding attacks prevention routing protocol (FAPRP) by extending the original AODV protocol and integrating FADA algorithm. The performance of the proposed solution is evaluated in terms of successful attack detection ratio, packet delivery ratio, and routing load both in normal and under RREQ attack scenarios using NS2 simulation. The simulation results show that the proposed FAPRP can detect over 99% of RREQ flooding attacks for all scenarios using route discovery frequency vector of sizes larger than 35 and performs better in terms of packet delivery ratio and routing load compared to existing solutions for RREQ flooding attacks.

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Section: On Machine Learning Approachmentioning

confidence: 99%

FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks

Ngoc

Tú

Hoang

2019

Wireless Communications and Mobile Computing

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“…Meenakshi patel et al [22] described, detecting and preventing flooding nodes in SVM. That classifies the nodes as par behavioral metrics of particular node cross the threshold limit which is predefined.…”

Section: Machine Lementioning

confidence: 99%

Detecting packet dropping nodes using machine learning techniques in Mobile ad-hoc network: A survey

Patel¹,

Jhaveri²

2015

2015 International Conference on Signal Processing and Communication Engineering Systems

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Mobile ad-hoc networks hav different types of packet dropping attacks. T strong mechanism to detect these malevol classify normal and abnormal nodes as pe nodes. Machine learning techniques disting quickly and accurately provide classificat behavior of those nodes in the network. In th various machine learning techniques as network, support vector machine, decision Bayesian network for identifying the malic techniques are able to detect black hole, g attacks and other packet dropping attack misbehaving nodes are identified and futur nodes are predicted with supervised reinforcement machine learning techniques.

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“…Ping Yi, Zhoulin Dai, Shiyong Zhang, Yiping Zhong in their paper [11] have designed a generic defense against the Adhoc Flooding Attack in mobile Adhoc Network. Their designed mechanism aims at recording the rate of route_request message.…”

Section: Related Papersmentioning

confidence: 99%

Security Measure to Detect and Avoid Flooding Attacks using Multi-Agent System in MANETS

Mahapatra¹,

Patnaik²

2017

IJECE

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Security is considered as one of the major challenge when it comes to infrastructure less and self dependent network without any centralized control. The vulnerability of Adhoc Network makes it susceptible to external attacks like flooding of hello messages or propagating fake routing messages etc. Such attacks generates a variety of problems like disturbing the network by flooding messages that results in waste of battery which is a vital resource to maintain the life span of the network. Most importantly cause agents to die when unable to reach destination due to fake routing messages causing a heavy loss on part of the nodes generating them to maintain the route knowledge. The paper proposes a novel technique to identify the flooding attack and measure to overcome them using Multi-Agent system Keyword:AdHoc network system External attack in MANET Flooding attacks Optimal agents in multi-agent system

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Detection and Prevention of Flooding Attack Using SVM

Cited by 17 publications

References 7 publications

FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks

FAPRP: A Machine Learning Approach to Flooding Attacks Prevention Routing Protocol in Mobile Ad Hoc Networks

Detecting packet dropping nodes using machine learning techniques in Mobile ad-hoc network: A survey

Security Measure to Detect and Avoid Flooding Attacks using Multi-Agent System in MANETS

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