  Static and dynamic binary analysis techniques are actively used to reverse
engineer software's behavior and to detect its vulnerabilities, even when only
the binary code is available for analysis. To avoid analysis errors due to
misreading op-codes for a wrong CPU architecture, these analysis tools must
precisely identify the Instruction Set Architecture (ISA) of the object code
under analysis. The variety of CPU architectures that modern security and
reverse engineering tools must support is ever increasing due to massive
proliferation of IoT devices and the diversity of firmware and malware
targeting those devices. Recent studies concluded that falsely identifying the
binary code's ISA caused alone about 10\% of failures of IoT firmware analysis.
The state of the art approaches to detect ISA for arbitrary object code look
promising - their results demonstrate effectiveness and high-performance.
However, they lack the support of publicly available datasets and toolsets,
which makes the evaluation, comparison, and improvement of those techniques,
datasets, and machine learning models quite challenging (if not impossible).
This paper bridges multiple gaps in the field of automated and precise
identification of architecture and endianness of binary files and object code.
We develop from scratch the toolset and datasets that are lacking in this
research space. As such, we contribute a comprehensive collection of open data,
open source, and open API web-services. We also attempt experiment
reconstruction and cross-validation of effectiveness, efficiency, and results
of the state of the art methods. When training and testing classifiers using
solely code-sections from executable binary files, all our classifiers
performed equally well achieving over 98\% accuracy. The results are consistent
and comparable with the current state of the art, hence supports the general
validity of the algorithms
