Design of genetic algorithm based IDS for MANET

Sujatha, K.; Vydeki, D.; Bhuvaneswaran, R. S.

doi:10.1109/icrtit.2012.6206796

Cited by 29 publications

(13 citation statements)

References 2 publications

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“…al. [3] explained genetic algorithm based IDS. IDS transform the network in population by considering various networking parameters like packet drop, request forwarding rate, request receive rate, etc.…”

Section: Related Workmentioning

confidence: 99%

A Fortify Approach to Secure AODV Protocol against Black Hole Attacks

Lohi¹,

Sharma²

2015

IJCA

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The advent of mobile and miniature devices in wireless technology gives rise to new paradigm called Mobile Ad-hoc Network (MANET). MANETs are self-maintaining, self administered dynamic network. MANETs are vulnerabilities in the MANET due to its intrinsic characteristic that make it insecure against various security threats.The black hole attack is forthcoming among them that are launched on AODV protocol. In these attacks, a malicious node disrupts data transmission by sending false routing information containing very high sequence numbers. To deal with these attacks, we proposed an incentive mechanism that protects network against this attack. This mechanism is integrated into route decision making process of the AODV protocol to defend the black-hole attack.

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Section: Related Workmentioning

confidence: 99%

A Fortify Approach to Secure AODV Protocol against Black Hole Attacks

Lohi¹,

Sharma²

2015

IJCA

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“…Daniel [8] has proposed multi-class cancer classification using gene expression profiling and probabilistic neural networks. Sujatha et al proposed [9] genetic algorithm based IDS for MANETs.…”

Section: Related Workmentioning

confidence: 99%

Multi-class Intrusion Detection System for MANETs

Pavani¹,

Damodaram²

2015

JACN

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Abstract-As MANETs change their topology dynamically, intrusion detection in these networks is a challenging task. These networks are more liable to the security attacks because of the properties such as node mobility, lack of concentration points where aggregated traffic can be analyzed, intermittent wireless communications and limited band width. We present a multiclass intrusion detection system that addresses these challenges. In this paper we propose a neural network method based on MLP (multi-layer perceptron) for detecting normal and attacked behavior of the system. The method was tested for Black Hole and Gray Hole attacks. We have implemented these attacks using NS2 simulator. The method successfully detected these attacks. We compared the results with KNN (K-Nearest Neighborhood) which is another classifier used for classification. Finally, Re sampling methods were also applied to assess the performance of classifier. This paper presents a graphical representation of the results.Index Terms-Intrusion detection system, Black Hole attack, Gray Hole attack, multi-layer perceptron, K-nearest neighborhood.

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“…After each memory updating, a random integer in [5,10] is generated to decide the next memory updating time t M which is calculated by using t M = t + rand (5,10). According to the population, the memory is also updated before the environmental change.…”

Section: ) P′′(t) := Select For Reproduction (P′(t)) // Standard Gementioning

confidence: 99%

“…Reliable genetic algorithm 2 used in routing during route discovery to get an optimum solution and route maintenance. Cluster based routing provides a optimal result in a network topology structure 3,5 . Various techniques have been introduced such shortest path routing 11,12 , energy-efficient QoS multicast routing 1 , energy-efficient multi-metric QoS routing scheme 16 , and Genetic based optimum routing 8 to enhance routing performance in the network.…”

Section: Introductionmentioning

confidence: 99%

Load Balanced Clustering Technique in MANET using Genetic Algorithms

Kaliappan¹,

E²,

Prakash³

et al. 2016

Def. Sc. Jl.

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Mobile adhoc network (MANET) has characteristics of topology dynamics due to factors such as energy conservation and node movement that leads to dynamic load-balanced clustering problem (DLBCP). Load-balancing and reliable data transfer between all the nodes are essential to prolong the lifetime of the network. MANET can also be partitioned into clusters for maintaining the network structure. Generally, Clustering is used to reduce the size of topology and to accumulate the topology information. It is necessary to have an effective clustering algorithm for adapting the topology change. In this, we used energy metric in genetic algorithm (GA) to solve the DLBCP. It is important to select the energy-efficient cluster head for maintaining the cluster structure and balance the load effectively. In this work, we used genetic algorithms such as elitism based immigrants genetic algorithm (EIGA) and memory enhanced genetic algorithm (MEGA) to solve DLBCP. These schemes select an optimal cluster head by considering the distance and energy parameters. We used EIGA to maintain the diversity level of the population and MEGA to store the old environments into the memory. It promises the load -balancing in cluster structure to increase the lifetime of the network. Experimental results show that the proposed schemes increases the network lifetime and reduces the total energy consumption. The simulation results show that MEGA and EIGA give a better performance in terms of load-balancing.

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Design of genetic algorithm based IDS for MANET

Cited by 29 publications

References 2 publications

A Fortify Approach to Secure AODV Protocol against Black Hole Attacks

A Fortify Approach to Secure AODV Protocol against Black Hole Attacks

Multi-class Intrusion Detection System for MANETs

Load Balanced Clustering Technique in MANET using Genetic Algorithms

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