Description: Malware dataset is collected for malware confinement prediction. There are three sets of IoT nodes at different amounts (20, 40 and 60) encompassing temperature sensors connected with Intel ATLASEDGE Board and Beagle Boards (BeagleBone Blue), communicating via Bluetooth protocol. Benign and malware activities are executed on these devices to generate the initial attacked networks as the input graphs. Benign activities include MiBench and SPEC2006, Linux system programs, and word processors. The nodes represent devices and node attribute is a binary value referring to whether the device is compromised or not. Edge represents the connection of two devices and the edge attribute is a continuous value reflecting the distance of two devices. The real target graphs are generated by the classical malware confinement methods: stochastic controlling with malware detection. We collect 334 pairs of input and target graphs with different contextual parameters (infection rate, recovery rate, and decay rate) for each of the three datasets. In this dataset, there are both nodes attributes and edge attributes considered.
Statistics:
Name
Type
#Graphs
#Nodes
#Edges
Attributed
Directed
Weighted
Signed
Homogeneous
Spatial
Temporal
Labels
IoTNet
IoT Networks
343
20/40/60
~220/~630/~800
YES
NO
YES
NO
YES
NO
NO
YES
Acknowlegement: Guo X, Zhao L, Nowzari C, Rafatirad S, Homayoun H, Dinakarrao SM. Deep Multi-attributed Graph Translation with Node-Edge Co-evolution. In the 19th International Conference on Data Mining (ICDM 2019).