Click on the Generate button. Adding a Fuzzy Hash to a Content Once you've created a fuzzy hash definition, you can add it to a Content Examination definition. Log on to the Administration Console. Select a Folder in the hierarchy. Definitions cannot be placed in the "Root" folder. Either click on the: Definition to be changed. New Content Definition button to create a definition.
Click on the Select link to the left of the fuzzy has- h you wish to use. Line Score Specify a value to assign to the fuzzy hash. This is measured against the definition's activation score.
If selected, the fuzzy hash is added to the bottom of the list. If disabled, the fuzzy hash is added to the top of the list. Click the Save and Exit button. Click on the Fuzzy Hash Setting field to specify a similarity percentage value. Click on the Save and Exit button. See Also Article Properties Article Created Date. URL Name. Last Published Date. Gateway Policies Configuring Your Account. Can't find what you're looking for? Don't see what you're looking for?
Ask a Question. Below are screenshots that illustrate this principle. The word change in the text file and the resulting change in the MD5 hash represent the effect of changes in binary content of other files:.
Fuzzy hashing breaks the aforementioned cryptographic diffusion while still hiding the relationship between entity and hash. In doing so, this method provides similar resulting hashes when given similar inputs. Fuzzy hashing is the key to finding new malware that looks like something we have seen previously.
Like cryptographic hashes, there are several algorithms to calculate a fuzzy hash. Note how observably similar these hashes are because there is only a one-word difference in the text:. The main benefit of fuzzy hashes is similarity. Since these hashes can be calculated on several parts or the entirety of a file, we can focus on hash sequences that are like one another. This is important in determining the maliciousness of a previously undetected file and in categorizing malware according to type, family, malicious behavior, or even related threat actor.
Deep learning in its many applications has recently been remarkable at modeling natural human language. For example, convolutional architectures, recursive architectures like Gated Recurrent Units GRUs or Long Short Term Memory networks LSTMs , and most recently attention-based networks like all the variants of Transformers have been proven to be state-of-the-art in tackling human language tasks like sentiment analysis, question answering, or machine translation.
As such, we explored if similar techniques can be applied to computer languages like binary code, with fuzzy hashing as an intermediate step to reduce sequence complexity and length of the original space. A common deep learning approach in dealing with words is to use word embeddings.
However, because fuzzy hashes are not exactly natural language, we could not simply use pre-trained models. Instead, we needed to train our embeddings from scratch to identify malicious indicators. Once with these embeddings, we attempted to do most things with a language deep neural network.
We explored different architectures using standard techniques from literature, explored convolutions over these embeddings, attempted with multilayer perceptrons , and tried traditional sequential models like the previously-mentioned LSTM and GRU and attention-based networks Transformers.
Figure 4. Architecture overview of the deep learning model using fuzzy hashes. We got fairly good results with most techniques. However, to deploy and enable this model to the Microsoft Defender, we looked into other factors like inference times and the number of parameters in the network.
Inference time ruled out the sequential models because even though they were the best in terms of precision or recall, they are the slowest to run inference on. Meanwhile, the Transformers we experimented on also yielded excellent results but had several million parameters. Such parameters will be too costly to deploy at scale. That left us with the convolutional approach and multilayer perceptron. The perceptron yielded slightly better results between these two because the spatial adjacency intrinsically provided by the convolutional filters does not properly capture the relationship among the embeddings.
If two files have a relation indicated by their fuzzy hashes, there's less certainty. Moreover, it's hard to identify what differences there may be unless each and every byte in the two files is compared, which is extremely time consuming and may prove fruitless if the amount of similarity indicated by the fuzzy hash comparison is relatively low. Analyzing fuzzy hashes, therefore, becomes more expensive and less precise. Another problem is that ssdeep was derived from a technique used to detect spam in email messages.
As a result, the ssdeep hash generation and hash comparison algorithms have some properties that make sense when applied to generative textual content i. For example, the ssdeep hash comparison algorithm is dependent on the block sizes for the hashes of two binary files, which are derived from the overall size of the file being hashed. It'strivial to make the size of a particular executable file far larger than another that shares identical header and section data by simply appending data to the end of the file, which can force the hashing algorithm to adopt a different block size and thus prevent meaningful comparison.
Such an attack would not alter the execution of such a modified program. While ssdeep is a valuable tool for malware analysis, published literature on this approach makes it clear that an examination of this technique would benefit the community of malware analysts. Working with William Casey , also a senior scientist at CERT , I plan to identify and describe the particular cases in which fuzzy hashing is applicable in malware analysis and what significance hashes play in those cases.
Conversely, we also plan to identify the instances in which fuzzy hashes do not work and should not be applied in malware analysis. We are interested in comparing fuzzy hash techniques in the broader context of approximate string matching and in discovering best practices. We will publish these findings in a document and share it with the broader malware analysis community.
As part of our research, we will collaborate with author of ssdeep, Jesse Kornblum , and work with him to improve the accuracy and effectiveness of fuzzy hashing for malware analysis. This research is one of eight exploratory research projects funded in fiscal year by the SEI. The results will help determine what areas of work should become priorities for future SEI research and development.
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