Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques
This thesis presents a research whose objective is to design an intrusion detection model for Mobile Ad hoc NETworks (MANET). MANET is an autonomous system consisting of a group of mobile nodes with no infrastructure support. The MANET environment is particularly vulnerable because of the characteri...
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| Format: | Thesis |
| Language: | en en |
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2011
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| Online Access: | https://etd.uum.edu.my/2540/1/Farhan_Abdel-Fattah_Ahmad_Farhan.pdf https://etd.uum.edu.my/2540/2/1.Farhan_Abdel-Fattah_Ahmad_Farhan.pdf https://etd.uum.edu.my/2540/ |
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| author | Farhan, Farhan Abdel-Fattah Ahmad |
| author_facet | Farhan, Farhan Abdel-Fattah Ahmad |
| author_sort | Farhan, Farhan Abdel-Fattah Ahmad |
| building | UUM Library |
| collection | Institutional Repository |
| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| description | This thesis presents a research whose objective is to design an intrusion detection model for Mobile Ad hoc NETworks (MANET). MANET is an autonomous system consisting of a group of mobile nodes with no infrastructure support. The MANET environment is particularly vulnerable because of the characteristics of mobile ad hoc networks such as open medium, dynamic topology, distributed cooperation, and constrained capability. Unfortunately, the traditional
mechanisms designed for protecting networks are not directly applicable to MANETs without modifications. In the past decades, machine learning methods have been successfully used in several intrusion detection methods
because of their ability to discover and detect novel attacks. This research investigates the use of a promising technique from machine learning to designing the most suitable intrusion detection for this challenging network type. The proposed algorithm employs a combined model that uses two different measures (nonconformity metric measures and Local Distance-based Outlier Factor (LDOF)) to improve its detection ability. Moreover, the algorithm can provide a graded confidence that indicates the reliability of the classification. In machine learning algorithm, choosing the most relevant features for each attack is a very important requirement, especially in mobile ad hoc networks where the network topology dynamically changes. Feature selection is undertaken to select the relevant subsets of features to build an efficient prediction model and improve intrusion detection performance by removing irrelevant features. The transductive conformal prediction and outlier detection have been employed for feature selection algorithm. Traditional intrusion detection techniques have had trouble dealing with dynamic environments. In particular, issues such as collects real time attack related audit data and cooperative global detection. Therefore, the researcher is motivated to design a new intrusion detection architecture which involves new detection technique to efficiently detect the abnormalities in the ad hoc networks. The proposed model has distributed and cooperative hierarchical architecture, where nodes communicate with their region gateway node to make decisions. To validate the research, the researcher presents case study using GLOMOSIM simulation platform with AODV ad hoc routing protocols. Various active attacks are implemented. A series of experimental results demonstrate that the proposed intrusion detection model can effectively detect anomalies with low false positive rate, high detection rate and achieve high detection accuracy. |
| format | Thesis |
| id | my.uum.etd-2540 |
| institution | Universiti Utara Malaysia |
| language | en en |
| publishDate | 2011 |
| record_format | eprints |
| spelling | my.uum.etd-25402016-04-27T03:11:03Z https://etd.uum.edu.my/2540/ Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques Farhan, Farhan Abdel-Fattah Ahmad TK5101-6720 Telecommunication This thesis presents a research whose objective is to design an intrusion detection model for Mobile Ad hoc NETworks (MANET). MANET is an autonomous system consisting of a group of mobile nodes with no infrastructure support. The MANET environment is particularly vulnerable because of the characteristics of mobile ad hoc networks such as open medium, dynamic topology, distributed cooperation, and constrained capability. Unfortunately, the traditional mechanisms designed for protecting networks are not directly applicable to MANETs without modifications. In the past decades, machine learning methods have been successfully used in several intrusion detection methods because of their ability to discover and detect novel attacks. This research investigates the use of a promising technique from machine learning to designing the most suitable intrusion detection for this challenging network type. The proposed algorithm employs a combined model that uses two different measures (nonconformity metric measures and Local Distance-based Outlier Factor (LDOF)) to improve its detection ability. Moreover, the algorithm can provide a graded confidence that indicates the reliability of the classification. In machine learning algorithm, choosing the most relevant features for each attack is a very important requirement, especially in mobile ad hoc networks where the network topology dynamically changes. Feature selection is undertaken to select the relevant subsets of features to build an efficient prediction model and improve intrusion detection performance by removing irrelevant features. The transductive conformal prediction and outlier detection have been employed for feature selection algorithm. Traditional intrusion detection techniques have had trouble dealing with dynamic environments. In particular, issues such as collects real time attack related audit data and cooperative global detection. Therefore, the researcher is motivated to design a new intrusion detection architecture which involves new detection technique to efficiently detect the abnormalities in the ad hoc networks. The proposed model has distributed and cooperative hierarchical architecture, where nodes communicate with their region gateway node to make decisions. To validate the research, the researcher presents case study using GLOMOSIM simulation platform with AODV ad hoc routing protocols. Various active attacks are implemented. A series of experimental results demonstrate that the proposed intrusion detection model can effectively detect anomalies with low false positive rate, high detection rate and achieve high detection accuracy. 2011-03 Thesis NonPeerReviewed application/pdf en https://etd.uum.edu.my/2540/1/Farhan_Abdel-Fattah_Ahmad_Farhan.pdf application/pdf en https://etd.uum.edu.my/2540/2/1.Farhan_Abdel-Fattah_Ahmad_Farhan.pdf Farhan, Farhan Abdel-Fattah Ahmad (2011) Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques. PhD. thesis, Universiti Utara Malaysia. |
| spellingShingle | TK5101-6720 Telecommunication Farhan, Farhan Abdel-Fattah Ahmad Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques |
| title | Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques |
| title_full | Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques |
| title_fullStr | Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques |
| title_full_unstemmed | Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques |
| title_short | Intrusion Detection in Mobile Ad Hoc Networks Using Transductive Machine Learning Techniques |
| title_sort | intrusion detection in mobile ad hoc networks using transductive machine learning techniques |
| topic | TK5101-6720 Telecommunication |
| url | https://etd.uum.edu.my/2540/1/Farhan_Abdel-Fattah_Ahmad_Farhan.pdf https://etd.uum.edu.my/2540/2/1.Farhan_Abdel-Fattah_Ahmad_Farhan.pdf https://etd.uum.edu.my/2540/ |
| url_provider | http://etd.uum.edu.my/ |