Table 4 Sample feature data
From: Detecting malicious accounts in permissionless blockchains using temporal graph properties
tsfresh identified | Feature names (as used in code) | # Features | Value | |
|---|---|---|---|---|
59 | 36 | |||
indegreeTimeInv | ✓ | ✓ | − 0.0086 | |
outdegreeTimeInv | ✓ | − 0.0178 | ||
degreeTimeInv | ✓ | − 0.0128 | ||
numberOfburstTemporalInOut | ✓ | − 0.0045 | ||
longestBurstTemporalInOut | ✓ | − 0.1089 | ||
numberOfburstTemporalIn | ✓ | ✓ | − 0.0035 | |
longestBurstTemporalIn | ✓ | ✓ | − 0.0375 | |
numberOfburstTemporalOut | ✓ | ✓ | − 0.0079 | |
longestBurstTemporalOut | ✓ | ✓ | − 0.1289 | |
numberOfburstDegreeInOut | ✓ | − 0.0077 | ||
longestBurstDegreeInOutAtTime | ✓ | − 0.0728 | ||
numberOfburstDegreeIn | ✓ | ✓ | − 0.0062 | |
longestBurstDegreeInAtTime | ✓ | ✓ | − 0.047 | |
numberOfburstDegreeOut | ✓ | ✓ | − 0.0074 | |
longestBurstDegreeOutAtTime | ✓ | ✓ | − 0.0569 | |
zeroTransactions | ✓ | ✓ | 0.0 | |
totalBal | ✓ | ✓ | − 8.6e−5 | |
transactedFirst | ✓ | ✓ | − 0.1942 | |
transactedLast | ✓ | ✓ | − 0.8879 | |
activeDuration | ✓ | − 0.3908 | ||
averagePerInBal | ✓ | − 0.0222 | ||
uniqueIn | ✓ | ✓ | − 0.0061 | |
lastActiveSince | ✓ | ✓ | 0.0 | |
✓ | indegree__index_mass_quantile__q_0.1 | ✓ | ✓ | 0.3977 |
✓ | indegree__energy_ratio_by_chunks_num_segments_10__segment_focus_0 | ✓ | − 0.3957 | |
✓ | indegree__linear_trend__attr_“pvalue” | ✓ | ✓ | 1.1364 |
✓ | ittime__quantile__q_0.7 | ✓ | ✓ | − 0.2804 |
✓ | ittime__fft_coefficient__coeff_0__attr_“real” | ✓ | ✓ | − 0.3908 |
✓ | ittime__median | ✓ | − 0.2861 | |
✓ | outdegree__energy_ratio_by_chunks__num_segments_10__segment_focus_0 | ✓ | ✓ | − 0.4218 |
✓ | outdegree__enegy_ratio_by_chunks__num_segments_10__segment_focus_1 | ✓ | ✓ | 0.9724 |
✓ | outdegree__fft_coefficient__coeff_0__attr_“real” | ✓ | ✓ | − 0.0178 |
✓ | gasPrice__quantile__q_0.2 | ✓ | − 0.3171 | |
✓ | gasPrice__quantile__q_0.1 | ✓ | ✓ | − 0.3036 |
✓ | gasPrice__cwt_coefficients__widths_(2, 5,10, 20)__coeff_0__w_20 | ✓ | ✓ | − 0.0015 |
✓ | attractiveness__median | ✓ | ✓ | 0.7886 |
✓ | attractiveness__quantile__q_0.4 | ✓ | 0.6248 | |
✓ | attractiveness__mean | ✓ | 0.9815 | |
✓ | balanceOut__quantile__q_0.1 | ✓ | ✓ | − 0.0098 |
✓ | balanceOut__quantile__q_0.3 | ✓ | − 0.0118 | |
✓ | balanceOut__cwt_coefficients__widths_(2, 5,10, 20)__coeff_0__w_2 | ✓ | − 0.0128 | |
✓ | balanceIn__quantile__q_0.4 | ✓ | ✓ | − 0.0189 |
✓ | balanceIn__cwt_coefficients__widths_(2, 5,10, 20)__coeff_0__w_20 | ✓ | ✓ | − 0.0231 |
✓ | balanceIn__quantile__q_0.3 | ✓ | − 0.0183 | |
✓ | maxInPayment__quantile__q_0.3 | ✓ | − 0.0188 | |
✓ | maxInPayment__quantile__q_0.2 | ✓ | − 0.0183 | |
✓ | maxInPayment__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_5 | ✓ | ✓ | − 0.0173 |
✓ | maxOutPayment__quantile__q_0.6 | ✓ | − 0.0162 | |
✓ | maxOutPayment__quantile__q_0.1 | ✓ | − 0.0098 | |
✓ | maxOutPayment__cwt_coefficients__widths_(2, 5, 10, 20)__coeff_0__w_2 | ✓ | ✓ | − 0.0128 |
clusteringCoeff | ✓ | ✓ | − 0.0807 | |
burstCount_gasPrice | ✓ | − 0.0091 | ||
burstCount_balanceIn | ✓ | − 0.1115 | ||
burstCount_balanceOut | ✓ | − 0.0073 | ||
burstInstance_indegree | ✓ | ✓ | − 0.0108 | |
burstInstance_outdegree | ✓ | ✓ | − 0.0150 | |
burstInstance_maxInPayment | ✓ | ✓ | − 0.1136 | |
burstInstance_maxOutPayment | ✓ | ✓ | − 0.0073 | |
burstInstance_gasPrice | ✓ | ✓ | − 0.0121 | |