Abstract
In this article, we investigate the influence of mobility models, node speed and attack duration on the MANET vulnerability to bandwidth attacks, which are performed as distributed denial of service (DDoS) attacks. We propose the attack model and present a comprehensive simulation study, carried out by the network simulator ns-2 and its associated tools for mobile scenario generation, network animation and trace files analysis. The following mobility models have been compared: Random Waypoint, Manhattan Grid, Gauss-Markov, and Reference Point Group Mobility. Simulation results indicate that MANET inherent features, such as node mobility pattern and speed, strongly affect the degree of vulnerability to DDoS attacks. Possible countermeasures against this type of attack have also been discussed. Ill. 6, bibl. 17, tabl. 2 (in English; abstracts in English and Lithuanian). DOI: http://dx.doi.org/10.5755/j01.eee.119.3.1358
Highlights
A mobile ad hoc network (MANET) is a multi-hop, wireless, self-configuring network that can be formed without the need of any pre-established infrastructure or centralized administration
When a distributed DoS (DDoS) attack occurs in MANET, the attacker compromises a number of mobile nodes, which can follow different mobile patterns and have different speeds
The attacker has to control more than one node to generate the attack traffic, i.e., such attacks are usually DDoS attacks
Summary
A mobile ad hoc network (MANET) is a multi-hop, wireless, self-configuring network that can be formed without the need of any pre-established infrastructure or centralized administration. Node mobility and lack of central administration make MANETs far more vulnerable to security attacks than conventional networks [1, 2]. The attacks become extremely dangerous and hard to prevent if a group of attackers coordinate in DoS. We explain the attack model and present the results of a comprehensive simulation study, carried out by the network simulator ns-2 and its associated tools for mobile scenario generation, network animation and trace files analysis. Possible countermeasures against this type of attack have been outlined
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