Semi-Supervised Techniques for Detecting Previously Unseen Radar Behaviors
The rapid advance in multifunction radars (MFRs) whose behavior model can be promptly reprogrammed complicates the task of electrical warfare (EW) systems. It is crucial that an EW system be able to detect the change in a radar’s behavior when it happens. In this article, two contextual a...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10177924/ |
| _version_ | 1797772575247433728 |
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| author | Jayson Rook Chi-Hao Cheng |
| author_facet | Jayson Rook Chi-Hao Cheng |
| author_sort | Jayson Rook |
| collection | DOAJ |
| description | The rapid advance in multifunction radars (MFRs) whose behavior model can be promptly reprogrammed complicates the task of electrical warfare (EW) systems. It is crucial that an EW system be able to detect the change in a radar’s behavior when it happens. In this article, two contextual anomaly detection techniques based on Hidden Markov Models (HMM) and Long Short-Term Memories (LSTM) are applied to detect an MFR’s behavior change. Both of them are trained based on known radar modes and indicate the presence of an anomaly radar mode when the signal sequence emitted by the radar does not match the EW system’s prediction based on known modes. This topic is important as the EW systems relying on libraries of knowns radar signals would be in a dangerous situation when encountering an unknown radar behavior in practice and knowing the existence of such a radar mode can increase the system’s survivability. The results demonstrate the great potential of these techniques when applied in EW applications. However, the HMM holds the advantage over the LSTM if the MFR changes its mode more frequently. |
| first_indexed | 2024-03-12T21:52:51Z |
| format | Article |
| id | doaj.art-1b32c1524bc34e7dbfc3290b7ee6abc4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T21:52:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1b32c1524bc34e7dbfc3290b7ee6abc42023-07-25T23:00:36ZengIEEEIEEE Access2169-35362023-01-0111703687037610.1109/ACCESS.2023.329426810177924Semi-Supervised Techniques for Detecting Previously Unseen Radar BehaviorsJayson Rook0Chi-Hao Cheng1https://orcid.org/0000-0001-8104-3114Department of Electrical and Computer Engineering, Miami University, Oxford, OH, USADepartment of Electrical and Computer Engineering, Miami University, Oxford, OH, USAThe rapid advance in multifunction radars (MFRs) whose behavior model can be promptly reprogrammed complicates the task of electrical warfare (EW) systems. It is crucial that an EW system be able to detect the change in a radar’s behavior when it happens. In this article, two contextual anomaly detection techniques based on Hidden Markov Models (HMM) and Long Short-Term Memories (LSTM) are applied to detect an MFR’s behavior change. Both of them are trained based on known radar modes and indicate the presence of an anomaly radar mode when the signal sequence emitted by the radar does not match the EW system’s prediction based on known modes. This topic is important as the EW systems relying on libraries of knowns radar signals would be in a dangerous situation when encountering an unknown radar behavior in practice and knowing the existence of such a radar mode can increase the system’s survivability. The results demonstrate the great potential of these techniques when applied in EW applications. However, the HMM holds the advantage over the LSTM if the MFR changes its mode more frequently.https://ieeexplore.ieee.org/document/10177924/Anomaly detectionelectronic warfare (EW)hidden Markov model (HMM)long short-term memory (LSTM)multifunction radar (MFR) |
| spellingShingle | Jayson Rook Chi-Hao Cheng Semi-Supervised Techniques for Detecting Previously Unseen Radar Behaviors IEEE Access Anomaly detection electronic warfare (EW) hidden Markov model (HMM) long short-term memory (LSTM) multifunction radar (MFR) |
| title | Semi-Supervised Techniques for Detecting Previously Unseen Radar Behaviors |
| title_full | Semi-Supervised Techniques for Detecting Previously Unseen Radar Behaviors |
| title_fullStr | Semi-Supervised Techniques for Detecting Previously Unseen Radar Behaviors |
| title_full_unstemmed | Semi-Supervised Techniques for Detecting Previously Unseen Radar Behaviors |
| title_short | Semi-Supervised Techniques for Detecting Previously Unseen Radar Behaviors |
| title_sort | semi supervised techniques for detecting previously unseen radar behaviors |
| topic | Anomaly detection electronic warfare (EW) hidden Markov model (HMM) long short-term memory (LSTM) multifunction radar (MFR) |
| url | https://ieeexplore.ieee.org/document/10177924/ |
| work_keys_str_mv | AT jaysonrook semisupervisedtechniquesfordetectingpreviouslyunseenradarbehaviors AT chihaocheng semisupervisedtechniquesfordetectingpreviouslyunseenradarbehaviors |