Leveraging Artificial Intelligence on Binary Code Comprehension

• URL: http://arxiv.org/abs/2210.05103v1
• Date: Tue, 11 Oct 2022 02:39:29 GMT
• Title: Leveraging Artificial Intelligence on Binary Code Comprehension
• Authors: Yifan Zhang
• Abstract summary: We propose to develop Artificial Intelligence (AI) models that aid human comprehension of binary code. Specifically, we propose to incorporate domain knowledge from large corpora of source code (e.g., variable names, comments) to build AI models that capture a generalizable representation of binary code. Lastly, we will investigate metrics to assess the performance of models that apply to binary code by using human studies of comprehension.
• Score: 5.236023714727536
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding binary code is an essential but complex software engineering task for reverse engineering, malware analysis, and compiler optimization. Unlike source code, binary code has limited semantic information, which makes it challenging for human comprehension. At the same time, compiling source to binary code, or transpiling among different programming languages (PLs) can provide a way to introduce external knowledge into binary comprehension. We propose to develop Artificial Intelligence (AI) models that aid human comprehension of binary code. Specifically, we propose to incorporate domain knowledge from large corpora of source code (e.g., variable names, comments) to build AI models that capture a generalizable representation of binary code. Lastly, we will investigate metrics to assess the performance of models that apply to binary code by using human studies of comprehension.

Related papers

• CodeGRAG: Bridging the Gap between Natural Language and Programming Language via Graphical Retrieval Augmented Generation [58.84212778960507]
  We propose CodeGRAG, a Graphical Retrieval Augmented Code Generation framework to enhance the performance of LLMs. CodeGRAG builds the graphical view of code blocks based on the control flow and data flow of them to fill the gap between programming languages and natural language. Various experiments and ablations are done on four datasets including both the C++ and python languages to validate the hard meta-graph prompt, the soft prompting technique, and the effectiveness of the objectives for pretrained GNN expert.
  arXiv  Detail & Related papers  (2024-05-03T02:48:55Z)
• Unsupervised Binary Code Translation with Application to Code Similarity Detection and Vulnerability Discovery [2.022692275087205]
  Cross-architecture binary code analysis has become an emerging problem. Deep learning-based binary analysis has shown promising success. For some low-resource ISAs, an adequate amount of data is hard to find.
  arXiv  Detail & Related papers  (2024-04-29T18:09:28Z)
• How Far Have We Gone in Binary Code Understanding Using Large Language Models [51.527805834378974]
  We propose a benchmark to evaluate the effectiveness of Large Language Models (LLMs) in binary code understanding. Our evaluations reveal that existing LLMs can understand binary code to a certain extent, thereby improving the efficiency of binary code analysis.
  arXiv  Detail & Related papers  (2024-04-15T14:44:08Z)
• CLAP: Learning Transferable Binary Code Representations with Natural Language Supervision [22.42846252594693]
  We present CLAP (Contrastive Language-Assembly Pre-training), which employs natural language supervision to learn better representations of binary code. At the core, our approach boosts superior transfer learning capabilities by effectively aligning binary code with their semantics explanations. We have generated 195 million pairs of binary code and explanations and trained a prototype of CLAP.
  arXiv  Detail & Related papers  (2024-02-26T13:49:52Z)
• Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit [63.82016263181941]
  Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora. Currently, there is already a thriving research community focusing on code intelligence.
  arXiv  Detail & Related papers  (2023-12-30T17:48:37Z)
• CP-BCS: Binary Code Summarization Guided by Control Flow Graph and Pseudo Code [79.87518649544405]
  We present a control flow graph and pseudo code guided binary code summarization framework called CP-BCS. CP-BCS utilizes a bidirectional instruction-level control flow graph and pseudo code that incorporates expert knowledge to learn the comprehensive binary function execution behavior and logic semantics.
  arXiv  Detail & Related papers  (2023-10-24T14:20:39Z)
• Pre-Training Representations of Binary Code Using Contrastive Learning [13.570375923483452]
  We propose a COntrastive learning Model for Binary cOde Analysis, or COMBO, that incorporates source code and comment information into binary code during representation learning. COMBO is the first language representation model that incorporates source code, binary code, and comments into contrastive code representation learning.
  arXiv  Detail & Related papers  (2022-10-11T02:39:06Z)
• Semantic-aware Binary Code Representation with BERT [27.908093567605484]
  A wide range of binary analysis applications, such as bug discovery, malware analysis and code clone detection, require recovery of contextual meanings on a binary code. Recently, binary analysis techniques based on machine learning have been proposed to automatically reconstruct the code representation of a binary. In this paper, we propose DeepSemantic utilizing BERT in producing the semantic-aware code representation of a binary code.
  arXiv  Detail & Related papers  (2021-06-10T03:31:29Z)
• COSEA: Convolutional Code Search with Layer-wise Attention [90.35777733464354]
  We propose a new deep learning architecture, COSEA, which leverages convolutional neural networks with layer-wise attention to capture the code's intrinsic structural logic. COSEA can achieve significant improvements over state-of-the-art methods on code search tasks.
  arXiv  Detail & Related papers  (2020-10-19T13:53:38Z)
• Incorporating External Knowledge through Pre-training for Natural Language to Code Generation [97.97049697457425]
  Open-domain code generation aims to generate code in a general-purpose programming language from natural language (NL) intents. We explore the effectiveness of incorporating two varieties of external knowledge into NL-to-code generation: automatically mined NL-code pairs from the online programming QA forum StackOverflow and programming language API documentation. Our evaluations show that combining the two sources with data augmentation and retrieval-based data re-sampling improves the current state-of-the-art by up to 2.2% absolute BLEU score on the code generation testbed CoNaLa.
  arXiv  Detail & Related papers  (2020-04-20T01:45:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.