Engineering:AI-assisted reverse engineering
AI-assisted reverse engineering (AIARE) is a branch of computer science that leverages artificial intelligence (AI), notably machine learning (ML) strategies, to augment and automate the process of reverse engineering. The latter involves breaking down a product, system, or process to comprehend its structure, design, and functionality. AIARE was primarily introduced in the early years of the 21st century, witnessing substantial advancements from the mid-2010s onwards.
Overview
Conventionally, reverse engineering is conducted by specialists who dismantle a system to grasp its working principles, often for the purposes of reproduction, modification, enhancement of compatibility, or forensic examination. This method, while efficient, can be laborious and time-intensive, particularly when dealing with intricate software or hardware systems.[1][2][3]
AIARE integrates machine learning algorithms to either partially automate or augment this process.[4][5] It is capable of detecting patterns, relationships, structures, and potential vulnerabilities within the analyzed system, frequently surpassing human experts in speed and accuracy. This has rendered AIARE a critical tool in numerous fields, including cybersecurity, software development, and hardware design and analysis.[6]
Techniques
AIARE encompasses several AI methodologies:
Supervised learning
Supervised learning employs tagged data to train models to recognize system components, their operations, and their interconnections. This method is particularly helpful in software analysis to discover vulnerabilities or enhance compatibility.[3][7][8]
Unsupervised learning
Unsupervised learning is utilized to detect concealed patterns and structures in untagged data. It proves beneficial in comprehending complex systems where there's no evident labeling or mapping of components.[1][9]
Reinforcement learning
Reinforcement learning is employed to build models that progressively refine their system understanding through a process of trial and error. This method is often implemented when deciphering a system's functionality under various circumstances or configurations.[1][5][10]
Deep learning
Deep learning is employed for analysis of high-dimensional data. For instance, deep learning techniques can aid in examining the layout and connections of integrated circuits (ICs), substantially reducing the manual effort required for reverse engineering.[3][4]
References
- ↑ 1.0 1.1 1.2 Neukart, Florian (2017). Reverse engineering the mind: consciously acting machines and accelerated evolution. AutoUni – Schriftenreihe. Wiesbaden: Springer. ISBN 978-3-658-16175-0.
- ↑ Bayern, Shawn (2022-12-13), "Reverse engineering (by) artificial intelligence", Research Handbook on Intellectual Property and Artificial Intelligence (Edward Elgar Publishing): pp. 391–404, doi:10.4337/9781800881907.00029, ISBN 978-1-80088-190-7, https://www.elgaronline.com/edcollchap/book/9781800881907/book-part-9781800881907-29.xml, retrieved 2023-07-06
- ↑ 3.0 3.1 3.2 Ethier, Stephen P. (2023). Using Functional Genomics and Artificial Intelligence to Reverse Engineer Human Cancer Cells. Cambridge Scholars. ISBN 978-1-5275-9230-8.
- ↑ 4.0 4.1 Eilam, Eldad (2005). Reversing: secrets of reverse engineering (Nachdr. ed.). Indianapolis, Ind: Wiley. ISBN 978-0-7645-7481-8.
- ↑ 5.0 5.1 Horváth, Imre, ed (2014). Tools and methods of competitive engineering: proceedings of the Tenth International Symposium on Tools and Methods of Competitive Engineering - TMCE 2014, May 19 - 23, Budapest, Hungary. Delft: Faculty of Industrial Design Engineering, Delft University of Technology. ISBN 978-94-6186-177-1.
- ↑ Eilam, Eldad (2005). Reversing: secrets of reverse engineering (Nachdr. ed.). Indianapolis, Ind: Wiley. ISBN 978-0-7645-7481-8.
- ↑ Alexandru C., Telea (2012). Reverse Engineering - Recent Advances and Applications. InTech. ISBN 978-9535101581.
- ↑ Tonella, Paolo; Torchiano, Marco; Du Bois, Bart; Systä, Tarja (2007-09-20). "Empirical studies in reverse engineering: state of the art and future trends" (in en). Empirical Software Engineering 12 (5): 551–571. doi:10.1007/s10664-007-9037-5. ISSN 1382-3256. http://link.springer.com/10.1007/s10664-007-9037-5.
- ↑ Abbott, Ryan, ed (2022). Research handbook on intellectual property and artificial intelligence. Research handbooks in intellectual property. Cheltenham Northampton, MA: Edward Elgar Publishing. ISBN 978-1-80088-189-1.
- ↑ "Machine Learning". https://aristeksystems.com/blog/ml-guide-2024/. Retrieved 2024-01-23.
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