Identifying malicious activity using data complexity anomalies
Examples relate to identifying malicious activity using data complexity anomalies. In one example, a computing device may: receive a byte stream that includes a plurality of bytes; determine, for a least one subset of the byte stream, a measure of complexity of the subset; determine that the measure of complexity meets a predetermined threshold measure of complexity for a context associated with the byte stream; and in response to determining that the measure of complexity meets the threshold, provide an indication that the byte stream complexity is anomalous.
1. A non-transitory machine-readable storage medium encoded with instructions executable by a hardware processor of a computing device, the machine-readable storage medium comprising instructions to cause the hardware processor to:
receive a byte stream that includes a plurality of bytes;
determine a measure of complexity of the byte stream, the byte stream being associated with a particular context of a plurality of contexts, the particular context being based on a type of information in the byte stream;
select, based on the particular context, a predetermined threshold measure of complexity from among a plurality of predetermined threshold measures of complexity;
compare the measure of complexity to the predetermined threshold measure of complexity to determine whether the byte stream is malicious; and
in response to determining that the measure of complexity meets the predetermined threshold measure of complexity, provide an indication that the byte stream is malicious.
2. The storage medium of claim 1 , wherein the hardware processor determines measures of complexity for each of a plurality of proper subsets of the byte stream.
3. The storage medium of claim 1 , wherein each the measure of complexity is based on a measure of compressibility.
4. The storage medium of claim 1 , wherein the threshold measure of complexity is a distribution threshold.
5. A method for identifying malicious activity using data complexity anomalies, implemented by a hardware processor, the method comprising:
receiving a byte stream,
the byte stream being associated with a particular context of a plurality of contexts, the particular context being based on a type of information in the byte stream;
selecting, based on the particular context, a predetermined threshold measure of complexity from among a plurality of predetermined threshold measures of complexity;
determining a measure of complexity for the byte stream;
comparing the measure of complexity to the predetermined threshold measure of complexity to determine whether the byte stream is malicious; and
determining that the byte stream is malicious in response to determining that the measure of complexity meets the predetermined threshold measure of complexity.