packet drop attack detection di mapanga innocent (5 risultati)

Taschenbuch. Condizione: Neu. 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.

Taschenbuch. Condizione: Neu. Packet Drop Attack Detection In Mobile Adhoc Networks using ANN | Innocent Mapanga | Taschenbuch | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139827237 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 84 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Mapanga InnocentInnocent Mapanga was born in Zimbabwe. He received his B.Sc. Honors degree in Computer Science from Bindura University of Science Education, Zimbabwe, in 2008 and an MTech in Computer Science & Engineering from Delhi .

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.