Watchdog malicious node detection and isolation using deep learning for secured communication in MANET
Mobile Ad-hoc Networks (MANETs) are wireless networks formed dynamically by connecting or leaving nodes to and from the network without any fixed infrastructure. These categories of wireless networks are susceptible to different attacks based on their dynamic topological structure. Due to this, secu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-10-01
|
| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2241766 |
| _version_ | 1797245084095217664 |
|---|---|
| author | Narmadha A. S. Maheswari S. Deepa S. N. |
| author_facet | Narmadha A. S. Maheswari S. Deepa S. N. |
| author_sort | Narmadha A. S. |
| collection | DOAJ |
| description | Mobile Ad-hoc Networks (MANETs) are wireless networks formed dynamically by connecting or leaving nodes to and from the network without any fixed infrastructure. These categories of wireless networks are susceptible to different attacks based on their dynamic topological structure. Due to this, security is a primary constraint in MANETs to preserve communication between mobile nodes. A Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) technique is proposed and modelled in this paper to improve the security feature of MANETs. The newly proposed DNLPPR-WMNDI technique initially selects the neighbouring nodes by applying the projection pursuit regression function. In multicasting, the route paths are established through the intermediate node with the help of control commands named RREQ and RREP. After then, Watchdog Malicious Node Detection and Isolation (WMNDI) technique is applied to detect malicious nodes based on the data packet forwarding time. Basically, a malicious node is affected by a node isolation attack. For better communication, a malicious node is isolated from the network and multicast routing is carried out by selecting the next neighbouring node and this improves the communication security. Simulation is done for the developed technique based on different performance metrics. |
| first_indexed | 2024-03-10T15:57:17Z |
| format | Article |
| id | doaj.art-da9fc0282da04894ad5801114e986103 |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-24T19:21:16Z |
| publishDate | 2023-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-da9fc0282da04894ad5801114e9861032024-03-25T18:18:03ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-10-01644996100910.1080/00051144.2023.2241766Watchdog malicious node detection and isolation using deep learning for secured communication in MANETNarmadha A. S.0Maheswari S.1Deepa S. N.2Department of ECE, Hindusthan Institute of technology, Coimbatore, IndiaDepartment of EEE, Kongu Engineering College, Erode, IndiaDepartment of Electrical Engineering, National Institute of Technology, Arunachal Pradesh, Jote, IndiaMobile Ad-hoc Networks (MANETs) are wireless networks formed dynamically by connecting or leaving nodes to and from the network without any fixed infrastructure. These categories of wireless networks are susceptible to different attacks based on their dynamic topological structure. Due to this, security is a primary constraint in MANETs to preserve communication between mobile nodes. A Deep Neural Learned Projective Pursuit Regression-based Watchdog Malicious Node Detection and Isolation (DNLPPR-WMNDI) technique is proposed and modelled in this paper to improve the security feature of MANETs. The newly proposed DNLPPR-WMNDI technique initially selects the neighbouring nodes by applying the projection pursuit regression function. In multicasting, the route paths are established through the intermediate node with the help of control commands named RREQ and RREP. After then, Watchdog Malicious Node Detection and Isolation (WMNDI) technique is applied to detect malicious nodes based on the data packet forwarding time. Basically, a malicious node is affected by a node isolation attack. For better communication, a malicious node is isolated from the network and multicast routing is carried out by selecting the next neighbouring node and this improves the communication security. Simulation is done for the developed technique based on different performance metrics.https://www.tandfonline.com/doi/10.1080/00051144.2023.2241766MANETsecure communicationdeep neural network learningprojection pursuit regression functionwatchdog malicious node detectionisolation |
| spellingShingle | Narmadha A. S. Maheswari S. Deepa S. N. Watchdog malicious node detection and isolation using deep learning for secured communication in MANET Automatika MANET secure communication deep neural network learning projection pursuit regression function watchdog malicious node detection isolation |
| title | Watchdog malicious node detection and isolation using deep learning for secured communication in MANET |
| title_full | Watchdog malicious node detection and isolation using deep learning for secured communication in MANET |
| title_fullStr | Watchdog malicious node detection and isolation using deep learning for secured communication in MANET |
| title_full_unstemmed | Watchdog malicious node detection and isolation using deep learning for secured communication in MANET |
| title_short | Watchdog malicious node detection and isolation using deep learning for secured communication in MANET |
| title_sort | watchdog malicious node detection and isolation using deep learning for secured communication in manet |
| topic | MANET secure communication deep neural network learning projection pursuit regression function watchdog malicious node detection isolation |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2241766 |
| work_keys_str_mv | AT narmadhaas watchdogmaliciousnodedetectionandisolationusingdeeplearningforsecuredcommunicationinmanet AT maheswaris watchdogmaliciousnodedetectionandisolationusingdeeplearningforsecuredcommunicationinmanet AT deepasn watchdogmaliciousnodedetectionandisolationusingdeeplearningforsecuredcommunicationinmanet |