MANETs (Mobile Ad-hoc NETworks) are an example of Peer-to-Peer (P2P) mobile networks in which security attacks, as black-hole ones, may cause serious dangers to the whole system. The watchdog is a well-known sensor usually adopted for detecting black-holes in such networks, but typical watchdogs are characterized by a relatively high number of false positive and negative cases, which can affect the effectiveness and efficiency to deal with intrusions.This paper proposes a novel approach for detecting black-hole attacks and selfish nodes in mobile P2P networks by using a watchdog sensor and a bayesian filtering. We demonstrate the validity of the approach through testing.

Black-Hole Attacks in P2P Mobile Networks Discovered through Bayesian Filters / Hortelano, Jorge; Carlos T., Calafate; Juan Carlos, Cano; DE LEONI, Massimiliano; Pietro, Manzoni; Mecella, Massimo. - 6428:(2010), pp. 543-552. [10.1007/978-3-642-16961-8_77]

Black-Hole Attacks in P2P Mobile Networks Discovered through Bayesian Filters

DE LEONI, MASSIMILIANO;MECELLA, Massimo
2010

Abstract

MANETs (Mobile Ad-hoc NETworks) are an example of Peer-to-Peer (P2P) mobile networks in which security attacks, as black-hole ones, may cause serious dangers to the whole system. The watchdog is a well-known sensor usually adopted for detecting black-holes in such networks, but typical watchdogs are characterized by a relatively high number of false positive and negative cases, which can affect the effectiveness and efficiency to deal with intrusions.This paper proposes a novel approach for detecting black-hole attacks and selfish nodes in mobile P2P networks by using a watchdog sensor and a bayesian filtering. We demonstrate the validity of the approach through testing.
2010
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Black-Hole Attacks in P2P Mobile Networks Discovered through Bayesian Filters / Hortelano, Jorge; Carlos T., Calafate; Juan Carlos, Cano; DE LEONI, Massimiliano; Pietro, Manzoni; Mecella, Massimo. - 6428:(2010), pp. 543-552. [10.1007/978-3-642-16961-8_77]
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