This study explores the use of neural networks as universal models for classifying file fragments. This approach differs from previous work in its lossless feature representation, with fragments’ bits as direct input, and its use of feedforward, recurrent, and convolutional networks as classifiers, whereas previous work has only tested feedforward networks. Due to the study’s exploratory nature, the models were not directly evaluated in a practical setting; rather, easily reproducible experiments were performed to attempt to answer the initial question of whether this approach is worthwhile to pursue further, especially due to its high computational cost. The experiments tested classification of fragments of homogeneous file types as an idealized case, rather than using a realistic set of types, because the types of interest are highly application-dependent. The recurrent networks achieved 98 percent accuracy in distinguishing 4 file types, suggesting that this approach may be capable of yielding models with sufficient performance for practical applications. The potential applications depend mainly on the model performance gains achievable by future work but include binary mapping, deep packet inspection, and file carving.
| Identifer | oai:union.ndltd.org:ETSU/oai:dc.etsu.edu:honors-1523 |
| Date | 01 May 2018 |
| Creators | Hiester, Luke |
| Publisher | Digital Commons @ East Tennessee State University |
| Source Sets | East Tennessee State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Undergraduate Honors Theses |
| Rights | Copyright by the authors., http://creativecommons.org/licenses/by-nc-nd/3.0/ |
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