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Jamming, whether intentional or not, threatens stable wireless communications by impeding a transmitted signal. New jamming technologies are regularly developed and deployed for use, which behave differently from their predecessors in order to bypass defense mechanisms. This makes existing jammer classifiers difficult to implement in fast changing dynamic environments, since human intervention is needed every time a new jamming technology is introduced. Improper maintenance will result in misclassification of the technology or allow jammers to pass through defenses. These scenarios will greatly reduce the performance of wireless networks and increase the response time for recovering from these attacks. As 5G continues to become more widespread, and other faster networks are released, wireless data rates will continue to grow. This furthers the need for a faster and more reliable jammer classifier, as shorter interruptions in service will cause even more data loss to occur. Incremental learning (IL) is a technique in machine learning that allows the introduction of new information to a previously trained network. Using IL, it is possible to create classifiers that can grow in number of classes without the need to retrain a new network from nothing. This allows remote devices to learn to adapt in dynamic environments with far lower memory cost. In this paper, we developed an IL-based jammer classifier using software defined radio (SDR) to detect when a jammer is present and classify the type and learn to classify new technologies when the type has not been encountered before. |
