DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION
Wireless sensor networks (WSNs) are made up of a large number of sensor nodes which collect data and send it to a centralized location. Nevertheless, the WSN has several security difficulties because of resource-constrained nodes, deployment methodologies, and communication channels. So, it is very...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2023-08-01
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| Series: | Proceedings on Engineering Sciences |
| Subjects: | |
| Online Access: | https://pesjournal.net/journal/v5-nS1/13.pdf |
| _version_ | 1827823206014124032 |
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| author | Akash Kumar Bhagat Prashant Kumar Pawan Bhambu Pandey V. K. Om Prakash |
| author_facet | Akash Kumar Bhagat Prashant Kumar Pawan Bhambu Pandey V. K. Om Prakash |
| author_sort | Akash Kumar Bhagat |
| collection | DOAJ |
| description | Wireless sensor networks (WSNs) are made up of a large number of sensor nodes which collect data and send it to a centralized location. Nevertheless, the WSN has several security difficulties because of resource-constrained nodes, deployment methodologies, and communication channels. So, it is very necessary to identify illegal access in order to strengthen the safety measures of WSN. The use of network intrusion detection systems (IDS) to safeguard the network is now standard procedure for any communication system. While deep learning (DL) methods are often utilized in IDS, their efficacy falls short when faced with imbalanced attacks. An IDS based on a novel transfer deep multicolumn convolution neural network (TDMCNN) technique was presented in this study to address this problem and boost performance. The most significant features of the dataset are chosen using a cross-correlation procedure and then included into the suggested methods for detecting intrusions. The accuracy, precision, sensitivity, and specificity are used to conduct the analysis and comparison. The experimental findings verified the effectiveness of the suggested method over the status quo of deep learning models for attack detection. |
| first_indexed | 2024-03-12T02:08:50Z |
| format | Article |
| id | doaj.art-1499caaf5c8f4d4c95b8b7f4e519f24f |
| institution | Directory Open Access Journal |
| issn | 2620-2832 2683-4111 |
| language | English |
| last_indexed | 2024-03-12T02:08:50Z |
| publishDate | 2023-08-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj.art-1499caaf5c8f4d4c95b8b7f4e519f24f2023-09-06T15:49:15ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112023-08-015S110311010.24874/PES.SI.01.013DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATIONAkash Kumar Bhagat0https://orcid.org/0000-0001-8717-764XPrashant Kumar1https://orcid.org/0000-0002-5831-776XPawan Bhambu2https://orcid.org/0000-0001-7163-0163Pandey V. K.3https://orcid.org/0000-0003-3475-8672Om Prakash4https://orcid.org/0000-0001-7599-9873Arka Jain University, Jamshedpur, Jharkhand, IndiaTeerthanker Mahaveer University, Moradabad, Uttar Pradesh, IndiaVivekananda Global University, Jaipur, IndiaNoida Institute Of Engineering and Technology, Greater Noida, Uttar Pradesh, IndiaGalgotias University, Greater Noida, Uttar Pradesh, IndiaWireless sensor networks (WSNs) are made up of a large number of sensor nodes which collect data and send it to a centralized location. Nevertheless, the WSN has several security difficulties because of resource-constrained nodes, deployment methodologies, and communication channels. So, it is very necessary to identify illegal access in order to strengthen the safety measures of WSN. The use of network intrusion detection systems (IDS) to safeguard the network is now standard procedure for any communication system. While deep learning (DL) methods are often utilized in IDS, their efficacy falls short when faced with imbalanced attacks. An IDS based on a novel transfer deep multicolumn convolution neural network (TDMCNN) technique was presented in this study to address this problem and boost performance. The most significant features of the dataset are chosen using a cross-correlation procedure and then included into the suggested methods for detecting intrusions. The accuracy, precision, sensitivity, and specificity are used to conduct the analysis and comparison. The experimental findings verified the effectiveness of the suggested method over the status quo of deep learning models for attack detection.https://pesjournal.net/journal/v5-nS1/13.pdfintrusion detection system (ids)wireless sensor network (wsn)deep learning (dl)transfer deep multicolumn convolution neural network (tdmcnn) |
| spellingShingle | Akash Kumar Bhagat Prashant Kumar Pawan Bhambu Pandey V. K. Om Prakash DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION Proceedings on Engineering Sciences intrusion detection system (ids) wireless sensor network (wsn) deep learning (dl) transfer deep multicolumn convolution neural network (tdmcnn) |
| title | DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION |
| title_full | DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION |
| title_fullStr | DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION |
| title_full_unstemmed | DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION |
| title_short | DEEP LEARNING-BASED INTRUSION DETECTION AND PREVENTION IN WIRELESS COMMUNICATION |
| title_sort | deep learning based intrusion detection and prevention in wireless communication |
| topic | intrusion detection system (ids) wireless sensor network (wsn) deep learning (dl) transfer deep multicolumn convolution neural network (tdmcnn) |
| url | https://pesjournal.net/journal/v5-nS1/13.pdf |
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