RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach
In some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-08-01
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| Series: | Journal of Cybersecurity and Privacy |
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| Online Access: | https://www.mdpi.com/2624-800X/1/3/23 |
| _version_ | 1797518698802577408 |
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| author | Pooria Madani Natalija Vlajic |
| author_facet | Pooria Madani Natalija Vlajic |
| author_sort | Pooria Madani |
| collection | DOAJ |
| description | In some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use of RSSI-based profiling as a means of detecting MAC-layer spoofing attacks are largely theoretical and thus fall short of providing insights and result that could be applied in the real world. Our work aims to fill this gap and examine the use of RSSI-based device profiling in dynamic real-world environments/networks with moving objects. The main contributions of our work and this paper are two-fold. First, we demonstrate that in dynamic real-world networks with moving objects, RSSI readings corresponding to one fixed transmitting node are neither stationary nor i.i.d., as generally has been assumed in the previous literature. This implies that in such networks, building an RSSI-based profile of a wireless device using a single statistical/ML model is likely to yield inaccurate results and, consequently, suboptimal detection performance against adversaries. Second, we propose a novel approach to MAC-layer spoofing detection based on RSSI profiling using multi-model Long Short-Term Memory (LSTM) autoencoder—a form of deep recurrent neural network. Through real-world experimentation we prove the performance superiority of this approach over some other solutions previously proposed in the literature. Furthermore, we demonstrate that a real-world defense system using our approach has a built-in ability to self-adjust (i.e., to deal with unpredictable changes in the environment) in an automated and adaptive manner. |
| first_indexed | 2024-03-10T07:33:23Z |
| format | Article |
| id | doaj.art-f11e57ddd39c418290462f8cfc80d1a9 |
| institution | Directory Open Access Journal |
| issn | 2624-800X |
| language | English |
| last_indexed | 2024-03-10T07:33:23Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Cybersecurity and Privacy |
| spelling | doaj.art-f11e57ddd39c418290462f8cfc80d1a92023-11-22T13:42:37ZengMDPI AGJournal of Cybersecurity and Privacy2624-800X2021-08-011345346910.3390/jcp1030023RSSI-Based MAC-Layer Spoofing Detection: Deep Learning ApproachPooria Madani0Natalija Vlajic1Electrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, CanadaElectrical Engineering and Computer Science, York University, Toronto, ON M3J 1P3, CanadaIn some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use of RSSI-based profiling as a means of detecting MAC-layer spoofing attacks are largely theoretical and thus fall short of providing insights and result that could be applied in the real world. Our work aims to fill this gap and examine the use of RSSI-based device profiling in dynamic real-world environments/networks with moving objects. The main contributions of our work and this paper are two-fold. First, we demonstrate that in dynamic real-world networks with moving objects, RSSI readings corresponding to one fixed transmitting node are neither stationary nor i.i.d., as generally has been assumed in the previous literature. This implies that in such networks, building an RSSI-based profile of a wireless device using a single statistical/ML model is likely to yield inaccurate results and, consequently, suboptimal detection performance against adversaries. Second, we propose a novel approach to MAC-layer spoofing detection based on RSSI profiling using multi-model Long Short-Term Memory (LSTM) autoencoder—a form of deep recurrent neural network. Through real-world experimentation we prove the performance superiority of this approach over some other solutions previously proposed in the literature. Furthermore, we demonstrate that a real-world defense system using our approach has a built-in ability to self-adjust (i.e., to deal with unpredictable changes in the environment) in an automated and adaptive manner.https://www.mdpi.com/2624-800X/1/3/23IoT securityspoofingMAC authenticationintrusion detection systemLSTM autoencoders |
| spellingShingle | Pooria Madani Natalija Vlajic RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach Journal of Cybersecurity and Privacy IoT security spoofing MAC authentication intrusion detection system LSTM autoencoders |
| title | RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach |
| title_full | RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach |
| title_fullStr | RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach |
| title_full_unstemmed | RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach |
| title_short | RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach |
| title_sort | rssi based mac layer spoofing detection deep learning approach |
| topic | IoT security spoofing MAC authentication intrusion detection system LSTM autoencoders |
| url | https://www.mdpi.com/2624-800X/1/3/23 |
| work_keys_str_mv | AT pooriamadani rssibasedmaclayerspoofingdetectiondeeplearningapproach AT natalijavlajic rssibasedmaclayerspoofingdetectiondeeplearningapproach |