Abstract: There is provided a system for computing connectivity of medical imaging network nodes, comprising: at least one hardware processor executing a code for: monitoring packets transmitted over a network connecting a plurality of network nodes, analyzing the monitored packets to identify packets associated with a Digital Imaging and Communications in Medicine (DICOM) protocol, analyzing the packets associated with the DICOM protocol to identify medical imaging network nodes implementing the DICOM protocol, designating a respective medical imaging type for each node of the medical imaging network nodes, and computing a data structure storing connectivity of the medical imaging network nodes each designated with the respective medical imaging type.

Abstract: There is provided a system for computing connectivity of medical imaging network nodes, comprising: at least one hardware processor executing a code for: monitoring packets transmitted over a network connecting a plurality of network nodes, analyzing the monitored packets to identify packets associated with a Digital Imaging and Communications in Medicine (DICOM) protocol, analyzing the packets associated with the DICOM protocol to identify medical imaging network nodes implementing the DICOM protocol, designating a respective medical imaging type for each node of the medical imaging network nodes, and computing a data structure storing connectivity of the medical imaging network nodes each designated with the respective medical imaging type.

Abstract: There is provided a system for computing connectivity of medical imaging network nodes, comprising: at least one hardware processor executing a code for: monitoring packets transmitted over a network connecting a plurality of network nodes, analyzing the monitored packets to identify packets associated with a Digital Imaging and Communications in Medicine (DICOM) protocol, analyzing the packets associated with the DICOM protocol to identify medical imaging network nodes implementing the DICOM protocol, designating a respective medical imaging type for each node of the medical imaging network nodes, and computing a data structure storing connectivity of the medical imaging network nodes each designated with the respective medical imaging type.

Abstract: There is provided a system for computing connectivity of medical imaging network nodes, comprising: at least one hardware processor executing a code for: monitoring packets transmitted over a network connecting a plurality of network nodes, analyzing the monitored packets to identify packets associated with a Digital Imaging and Communications in Medicine (DICOM) protocol, analyzing the packets associated with the DICOM protocol to identify medical imaging network nodes implementing the DICOM protocol, designating a respective medical imaging type for each node of the medical imaging network nodes, and computing a data structure storing connectivity of the medical imaging network nodes each designated with the respective medical imaging type.

Abstract: Use machine learning to train a classifier to classify entities to increase confidence with respect to an entity being part of a distributed denial of service attack. The method includes training a classifier to use a first classification method, to identify probabilities that entities from a set of entities are performing denial of service attacks. The method further includes identifying a subset of entities meeting a threshold probability of performing a denial of service attack. The method further includes using a second classification method, identifying similarity of entities in the subset of entities. The method further includes based on the similarity, classifying individual entities.

Abstract: Use machine learning to train a classifier to classify entities to increase confidence with respect to an entity being part of a distributed denial of service attack. The method includes training a classifier to use a first classification method, to identify probabilities that entities from a set of entities are performing denial of service attacks. The method further includes identifying a subset of entities meeting a threshold probability of performing a denial of service attack. The method further includes using a second classification method, identifying similarity of entities in the subset of entities. The method further includes based on the similarity, classifying individual entities.