Even though there are various source code plagiarism detection approaches, only a few
works which are focused on low-level representation for deducting similarity. Most of
them are only focused on lexical token sequence extracted from source code. In our
point of view, low-level representation is more beneficial than lexical token since its
form is more compact than the source code itself. It only considers semantic-preserving
instructions and ignores many source code delimiter tokens. This paper proposes a
source code plagiarism detection which rely on low-level representation. For a case
study, we focus our work on .NET programming languages with Common Intermediate
Language as its low-level representation. In addition, we also incorporate Adaptive
Local Alignment for detecting similarity. According to Lim et al, this algorithm
outperforms code similarity state-of-the-art algorithm (i.e. Greedy String Tiling) in
term of effectiveness. According to our evaluation which involves various plagiarism
attacks, our approach is more effective and efficient when compared with standard
lexical-token approach.