Bin-Carver: Automatic recovery of binary

executable filesPresented by: Ryan O’Donnell

The process of reassembling files from disk fragments in the absence of metadata.

What is file carving?

•Accidental user deletions•Intentional user deletions•Malware

When would we need file carving?

Using .jpeg file as an example•Find header (FF D8)•Know footer pair (FF D9)•Find all contiguous data

Traditional file carving method

•fragmentation•doesn’t work without exact header

and footer information•doesn’t work with all file types

o focuses on documents of forensic interest

o binary executables not included

Problems with traditional method

•recover Executable Linkable Format (ELF) file e from disk image D

•D strictly consists of file content blocks

•Assume D is an EXT2 file system, block size 4k

Bin-carver overview -1

•file content has not been overwritten•file content is stored in increasing

order•ELF file e has n blocks in the diskWe want to link these n blocks

together utilizing internal graph node logic.

Bin-carver overview -2

Bin-carver overview -3

•Filename recovery is typically not possible without the file system metadata

•Fragmentation

Challenges

•ELF-header scannero scan all possible ELF headers hi using

ELF-file magic value

•block node linkero scans disk image, identifies nodes and links

them

•conflict-node resolvero removes conflict nodes and outputs ELF-file ei

Components

System Overview Diagram

Headers hold a “road map” describing ELF file organization.

Searching for the magic number sequence 7f 45 4c 46 allows us to locate headers, telling us how to traverse all other sections .

Scanner -1

Each header is 52k and contains:•program header table (PHT)o array of program headers

•section header table (SHT)o array of section headers

Scanner -2

•usually located at end of ELF fileo can serve as a footer because of this

•since A(footer) > A(hi) we can start our search at the 0x14 disk block

•gives us a multitude of other constraints that allow us to calculate the location of the footer

Searching SHT

•locates segments that create memory image of the program

•each program header is 32 bytes•usually starts right after ELF headers

o same 4k block

Searching PHT

•from program header, infer vase virtual address of image file

•keep iterating and build our road map

•our goal is to find every fill this road map with content (bi)

Searching PHT

With no fragmentation, our job is done.

But, with any garbage gap, this approach would fail.

So how do we link each individual bi if the disk is fragmented?

Finished?

We have to logically connect bi and bj

We explore the caller-callee relationship:

•fill block place of bcaller and bcallee

o find address

•logically link them togethero function prologue signature (local calls)o PLT instruction sequence (library calls)

Block-node linker -1

On a library call•use PLT block number as an anchor•use this anchor to identify absolute block number of the caller block

On a local call•only determines distance•only works with blocks starting with e8 (CALL opcode)Most cases library calls are used to resolve block numbers

Block-node liner -2

A particular placeholder i could have several candidates.

To eliminate redundant placeholders:•use identified non-conflict nodes•explore logic connections•resolve node•iterate through until a fixed point is reached

Conflict-node resolver -1

Block-node linker only focuses on linking code blocks. Conflict-node resolver handles other data blocks (.data, .debug).

Conflict-node resolver -2

To retrieve data blocks:•treat data sections as a block between the ELF header and the first block of code section•resolvers explores constraints defined in PHT and SHT•worst case scenario: data section does not have identifiable sections and we must use dynamic execution to eliminate bogus permutationso essentially, if the recovered binary file doesn’t

crash, it may have been recovered successfully

Conflict-node resolver -3

Comparisons were intended to be made to other similar tools, both Foremost and Scalpel do not support carving for fragmented ELF binary files.

Evaluation - Comparison

Evaluation -1

•All files are ELF binarieso worst case, high false positive rateso addition of heterogeneous data irrelevant

•performance of algorithm is invariant to size of the disk

•performance relies on number of files to be recovered

Evaluation -2

To evaluate accuracy, need to prove the recovered files are true elf files.

Need to create an MD5 hash of first block and every individual block for each true ELF binary to detect true data in worst case fragmentation scenario.

Evaluation -3

Identification rate:•shows portion that can be identified no matter how fragmented the disk iso must be able to match hash values

Recovery Rate•valid files in the system that were identified and recovered

Effectiveness -1

Overall, very effective. On average:•Identification rate of 96.3%•Recovery rate of 93.1%

Effectiveness -3

Effectiveness -3

All performance slowdowns occur during linker and resolver phases.

Large gaps hurt performance, and the large number of caller-callee instructions cause performance penalties.

Runtime Analysis -1

Runtime Analysis -2