networks attack detection using (28 resultados)

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Taschenbuch. Condición: 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. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Master's Thesis from the year 2017 in the subject Computer Science - General, grade: 8.63, , course: M. Tech - Cognitive Radio Networks, language: English, abstract: Cognitive Radio Network (CRN) Technology makes the efficient utilization of scarce spectrum resources by allowing the unlicensed users to opportunistically use the licensed spectrum bands. Cognitive Radio Technology has gained a lot of attention from the researchers over the years however insufficient research has been done related to its security. Cognitive Radio Network due to its flexible and open nature is vulnerable to a number of security attacks. In this thesis we recognize different types of attacks at different layers of protocol stack. This thesis is mainly concerned with one of the physical layer attack called Primary User Emulation Attack and its detection. PUE Attack adversely affects the CRN performance and can also sometimes lead to Denial of Service to CR networks. In Primary User Emulation Attack the attacker imitates the signal characteristics of the Primary User. This thesis first provides introduction to CRN, its architecture and sensing techniques and also discusses various attacks and their counter measures. Then it mainly focuses on PUE attack and examines its mitigation techniques. This thesis solves the problem of PUE attack by localization technique based on TDOA measurements with reduced error in location estimation using a Novel Bat Algorithm (NBA). A number of cooperative secondary users are used for detecting the PUEA by comparing its estimated position with the known position of incumbent. The main goal of Novel Bat Algorithm (NBA) is to minimize two fitness functions namely non-linear least square (NLS) and the maximum likelihood (ML) in order to optimize the error in estimation.After evaluation, simulation results clearly demonstrates that NBA results in reduced estimation error as compared to Taylor Series Estimation (TSE) and Particle Swarm Optimization (PSO) and it also needs smaller number of secondary users for cooperation. Also maximum likelihood function performs better than non-linear least square function.

Taschenbuch. Condición: Neu. Neuware -Master's Thesis from the year 2017 in the subject Computer Science - General, grade: 8.63, , course: M. Tech - Cognitive Radio Networks, language: English, abstract: Cognitive Radio Network (CRN) Technology makes the efficient utilization of scarce spectrum resources by allowing the unlicensed users to opportunistically use the licensed spectrum bands. Cognitive Radio Technology has gained a lot of attention from the researchers over the years however insufficient research has been done related to its security. Cognitive Radio Network due to its flexible and open nature is vulnerable to a number of security attacks. In this thesis we recognize different types of attacks at different layers of protocol stack. This thesis is mainly concerned with one of the physical layer attack called Primary User Emulation Attack and its detection. PUE Attack adversely affects the CRN performance and can also sometimes lead to Denial of Service to CR networks. In Primary User Emulation Attack the attacker imitates the signal characteristics of the Primary User. This thesis first provides introduction to CRN, its architecture and sensing techniques and also discusses various attacks and their counter measures. Then it mainly focuses on PUE attack and examines its mitigation techniques. This thesis solves the problem of PUE attack by localization technique based on TDOA measurements with reduced error in location estimation using a Novel Bat Algorithm (NBA). A number of cooperative secondary users are used for detecting the PUEA by comparing its estimated position with the known position of incumbent. The main goal of Novel Bat Algorithm (NBA) is to minimize two fitness functions namely non-linear least square (NLS) and the maximum likelihood (ML) in order to optimize the error in estimation. After evaluation, simulation results clearly demonstrates that NBA results in reduced estimation error as compared to Taylor Series Estimation (TSE) and Particle Swarm Optimization (PSO) and it also needs smaller number of secondary users for cooperation. Also maximum likelihood function performs better than non-linear least square function.BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt 88 pp. Englisch.

Taschenbuch. Condición: Neu. Detection of Primary User Emulation Attack in Cognitive Radio Networks based on TDOA using Novel Bat Algorithm | Aasia Rehman | Taschenbuch | 92 S. | Englisch | 2019 | GRIN Verlag | EAN 9783668943407 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: 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.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Master's Thesis from the year 2017 in the subject Computer Science - General, grade: 8.63, , course: M. Tech - Cognitive Radio Networks, language: English, abstract: Cognitive Radio Network (CRN) Technology makes the efficient utilization of scarce spectrum resources by allowing the unlicensed users to opportunistically use the licensed spectrum bands. Cognitive Radio Technology has gained a lot of attention from the researchers over the years however insufficient research has been done related to its security. Cognitive Radio Network due to its flexible and open nature is vulnerable to a number of security attacks. In this thesis we recognize different types of attacks at different layers of protocol stack. This thesis is mainly concerned with one of the physical layer attack called Primary User Emulation Attack and its detection. PUE Attack adversely affects the CRN performance and can also sometimes lead to Denial of Service to CR networks. In Primary User Emulation Attack the attacker imitates the signal characteristics of the Primary User. This thesis first provides introduction to CRN, its architecture and sensing techniques and also discusses various attacks and their counter measures. Then it mainly focuses on PUE attack and examines its mitigation techniques. This thesis solves the problem of PUE attack by localization technique based on TDOA measurements with reduced error in location estimation using a Novel Bat Algorithm (NBA). A number of cooperative secondary users are used for detecting the PUEA by comparing its estimated position with the known position of incumbent. The main goal of Novel Bat Algorithm (NBA) is to minimize two fitness functions namely non-linear least square (NLS) and the maximum likelihood (ML) in order to optimize the error in estimation.After evaluation, simulation results clearly demonstrates that NBA results in reduced estimation error as compared to Taylor Series Estimation (TSE) and Particle Swarm Optimization (PSO) and it also needs smaller number of secondary users for cooperation. Also maximum likelihood function performs better than non-linear least square function. 92 pp. Englisch.

Condición: 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. Condición: 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.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dr. Sagar Dhanraj PandeAssistant Professor, Department of Computer Science and Engineering, School of Engineering and Technology, Pimpri Chinchwad University (PCU), Pune, Maharashtra, India.Dr. Sagar Dhanraj Pande has exper.

Buch. Condición: Neu. Networks Attack Detection on 5G Networks using Data Mining Techniques | Sagar Dhanraj Pande (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2024 | CRC Press | EAN 9781032530185 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book will look at cutting-edge Network Attacks and Security solutions that employ intelligent data processing and Machine Learning (ML) methods.