FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD
I am precisely thankful to Almighty for his unceasing and immense blessings without which my thesis completion would remain scattered and incomplete. I express my heartiest gratitude, profound indebtedness and deep respect to my supervisor, Dr. Md. Mahbubur Rahman, Professor, Department of CSE, M...
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| Natura: | Tesi |
| Lingua: | inglese |
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DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY
2019
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| Accesso online: | http://hdl.handle.net/123456789/394 |
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| _version_ | 1868227023354200064 |
|---|---|
| author | DEY, SAMRAT KUMAR |
| author_browse | DEY, SAMRAT KUMAR |
| author_facet | DEY, SAMRAT KUMAR |
| author_sort | DEY, SAMRAT KUMAR |
| collection | DSpace |
| description | I am precisely thankful to Almighty for his unceasing and immense blessings without
which my thesis completion would remain scattered and incomplete. I express my
heartiest gratitude, profound indebtedness and deep respect to my supervisor, Dr. Md.
Mahbubur Rahman, Professor, Department of CSE, Military Institute of Science and
Technology, for his constant supervision, affectionate guidance and great encouragement
and motivation. His keen interest on the topic and valuable advices throughout the study
was of great help in completing thesis.
I am especially grateful to the Department of Computer Science and Engineering of
Military Institute of Science and Technology (MIST) for providing their all out support
during the thesis work.
Finally, I would like to thank my parents, family members and friends for their
appreciable assistance, patience and suggestions during the course of my thesis. |
| format | Thesis |
| id | oai:localhost:123456789-394 |
| institution | My University |
| language | English |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY |
| publisherStr | DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY |
| record_format | dspace |
| spelling | oai:localhost:123456789-3942019-01-14T03:45:47Z FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD DEY, SAMRAT KUMAR I am precisely thankful to Almighty for his unceasing and immense blessings without which my thesis completion would remain scattered and incomplete. I express my heartiest gratitude, profound indebtedness and deep respect to my supervisor, Dr. Md. Mahbubur Rahman, Professor, Department of CSE, Military Institute of Science and Technology, for his constant supervision, affectionate guidance and great encouragement and motivation. His keen interest on the topic and valuable advices throughout the study was of great help in completing thesis. I am especially grateful to the Department of Computer Science and Engineering of Military Institute of Science and Technology (MIST) for providing their all out support during the thesis work. Finally, I would like to thank my parents, family members and friends for their appreciable assistance, patience and suggestions during the course of my thesis. Software Defined Networking (SDN) has come to prominence in recent years and demonstrates an enormous potential in shaping the future of networking by separating control plane from data plane. As a newly emerged technology, SDN has its inherent security threats that can be mitigated by securing the OpenFlow controller that manages flow control in SDN. On the other hand, Recurrent Neural Networks (RNN) show a remarkable result in sequence learning, particularly in architectures with gated unit structures such as Long Short-term Memory (LSTM). In recent years, several permutations of LSTM architecture have been proposed mainly to overcome the computational complexity of LSTM. Therefore, in this dissertation, a novel study is presented that will empirically investigate and evaluate flow-based anomaly detection method in OpenFlow controller using LSTM architecture variants such as Gated Recurrent Unit (GRU). Hence, in this exploration, we propose a combined Gated Recurrent Unit Long Short-Term Memory (GRU-LSTM) Network intrusion detection architecture. In order to improve the classifier performance, an appropriate ANOVA FTest and Recursive feature Elimination (RFE) (ANOVA F-RFE) feature selection method also have been applied. The proposed approach is tested using the benchmark dataset NSL-KDD. A subset of complete dataset with convenient feature selection ensures the highest accuracy of 87% with GRU-LSTM Model. DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY 2019-01-14T03:45:47Z 2019-01-14T03:45:47Z 2018-12 Thesis http://hdl.handle.net/123456789/394 en application/pdf DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY |
| spellingShingle | DEY, SAMRAT KUMAR FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD |
| title | FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD |
| title_full | FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD |
| title_fullStr | FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD |
| title_full_unstemmed | FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD |
| title_short | FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD |
| title_sort | flow based anomaly detection in software defined networking a deep learning approach with feature selection method |
| url | http://hdl.handle.net/123456789/394 |
| work_keys_str_mv | AT deysamratkumar flowbasedanomalydetectioninsoftwaredefinednetworkingadeeplearningapproachwithfeatureselectionmethod |