def getNearestFunction(self, gdbSession, codeAddr, pid=None):
if not self.addrBelongsToImage(gdbSession, codeAddr, pid):
return None
funcs = self.getFunctionsRebased(gdbSession, pid)
funcs.sort()
i = findItemInList(funcs, codeAddr)[0]
return funcs[i]
def a2(traces, referenceTrace):
"""
Assumes the trace for the invalid command as fixed-point. Consists of the following steps:
1) Identify all nodes that are present in all graphs.
2) For each of those nodes identify all neighbors that are present in all traces.
3) Calculate a score for each node in the following way:
For each neighbor in each trace that is not present in all traces a common function gets one point.
4) The score reflects the likelihood of each function to contain the command-dispatcher
"""
dgReference = referenceTrace.getDigraph()
nodesReference = dgReference.nodes()
nodesCommon = sets.Set(nodesReference)
nodesCommon.discard(referenceTrace.getVirtualStartingNode())
# first get nodes present in all graphs
graphs = []
for trace in traces:
dgCmd = trace.getDigraph()
nodesCommon = nodesCommon.intersection(dgCmd.nodes())
graphs.append(dgCmd)
# now get the common neighbors for all common nodes
neighborsCommon = {}
for node in nodesCommon:
neighborsCommon[node] = sets.Set(dgReference.neighbors(node))
for dgCmd in graphs:
for node in neighborsCommon:
neighborsCommon[node] = neighborsCommon[node].intersection(dgCmd.neighbors(node))
# finally calculate the 'variance of neighbors' for each node
variances = {node:0 for node in neighborsCommon}
for dgCmd in graphs:
for node in neighborsCommon:
neighborsCmd = sets.Set(dgCmd.neighbors(node))
neighborsDiff = neighborsCmd.difference(neighborsCommon[node])
variances[node] += len(neighborsDiff)
from tools.algorithms import findItemInList
values = []
keys = []
for node in variances:
value = variances[node]
i = findItemInList(values, value)[0]
values = values[:i+1] + [value] + values[i+1:]
keys = keys[:i+1] + [node] + keys[i+1:]
return keys[::-1]
