Packet Drop Attack Detection In Mobile Adhoc Networks using ANN

Innocent Mapanga

ISBN 10: 613982723X / ISBN 13: 9786139827237

Edité par LAP LAMBERT Academic Publishing Apr 2018, 2018

A propos de cet article

Neuware -Intrusion Detection Systems uses multiple methods to detect and prevent network attacks. A good IDS should be designed to reduce false positives and to ensure that only actual malicious traffic is detected and stopped .This study focuses on detecting the presence of malicious nodes that selectively or randomly drop packets intended for other destination nodes in Mobile Ad-hoc Networks(MANETs) ,it further classifies each packet drop attack, according to its attack type by observing and analyzing how each packet drop attack affect the network characteristics. Network security monitoring is based on the principle that prevention eventually fails. In the current threat landscape, no matter how much you try, motivated attackers will eventually find their way into your network. At that point, your ability to detect and respond to that intrusion can be the difference between a small incident and a major disaster. This study follows a three-stage cycle: i. data collection ii. data analysis iii. detection and classification. This syudy motivates the intelligent use of artificial neural networks that makes use of local data collected at each node in detecting malicious activities.Books on Demand GmbH, Überseering 33, 22297 Hamburg 84 pp. Englisch.

N° de réf. du vendeur 9786139827237

Détails bibliographiques

• Titre: Packet Drop Attack Detection In Mobile Adhoc Networks using ANN
• Éditeur: LAP LAMBERT Academic Publishing Apr 2018
• Date de parution: 2018
• Langue: anglais
• ISBN à 10 chiffres: 613982723X
• ISBN à 13 chiffres: 9786139827237
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 220 L x 150 H x 6 P (en millimètres) 220 millimètres de largeur par 150 millimètres de hauteur par 6 millimètres de profondeur
• Poids: 143 grammes

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Synopsis

Intrusion Detection Systems uses multiple methods to detect and prevent network attacks. A good IDS should be designed to reduce false positives and to ensure that only actual malicious traffic is detected and stopped .This study focuses on detecting the presence of malicious nodes that selectively or randomly drop packets intended for other destination nodes in Mobile Ad-hoc Networks(MANETs) ,it further classifies each packet drop attack, according to its attack type by observing and analyzing how each packet drop attack affect the network characteristics. Network security monitoring is based on the principle that prevention eventually fails. In the current threat landscape, no matter how much you try, motivated attackers will eventually find their way into your network. At that point, your ability to detect and respond to that intrusion can be the difference between a small incident and a major disaster. This study follows a three-stage cycle: i. data collection ii. data analysis iii. detection and classification. This syudy motivates the intelligent use of artificial neural networks that makes use of local data collected at each node in detecting malicious activities.