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Using AI Technologies to Solve Softw...

Wang, Haizhou,

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Using AI Technologies to Solve Software Security Challenges /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Using AI Technologies to Solve Software Security Challenges // Haizhou Wang.
作者:	Wang, Haizhou,
面頁冊數:	1 electronic resource (122 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-10, Section: B.
Contained By:	Dissertations Abstracts International86-10B.
標題:	Deep learning. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31897579
ISBN:	9798311915229

Using AI Technologies to Solve Software Security Challenges /
Wang, Haizhou,

Using AI Technologies to Solve Software Security Challenges /Haizhou Wang. - 1 electronic resource (122 pages)

Source: Dissertations Abstracts International, Volume: 86-10, Section: B.

Artificial intelligence (AI) has been a trending topic in recent years due to the technical breakthroughs in computer vision (CV) and natural language processing (NLP) where many tasks are nearly impossible to be solved by traditional engineering and algorithmic efforts, due their difficulties in generalizing patterns. In the field of software security, such tasks are not uncommon. For example, many tasks require high-level semantic program comprehension, such as code clone detection and logic bug detection. These tasks are similar to NLP tasks to a certain extent, so that existing methods such as static or dynamic program analysis are very ineffective in understanding the programs in terms of the business logic. For another example, information and data available in many software security tasks such as reverse engineering are not human-friendly, which may lead to generating explicit rules or heuristics extremely challenging. Therefore, it is crucial to explore what tasks in software security could be solved or solved better using modern AI technologies.In this dissertation, we aim to adopt AI technologies in three software security subfields: exploitation defense, vulnerability analysis and reverse engineering. In particular, we have researched three problems: 1) defending return-oriented-programming (ROP) attacks using deep learning, 2) finding user privilege related (UPR) variables using LLM workflow, and 3) pinpoint the implementation of anti dynamic analysis techniques in the binary program using LLM. The common characteristics of all three problems are the fuzziness of input and the difficulty to generalize common patterns, which are the key motivations of us adopting AI technologies and data driven methods.

English

ISBN: 9798311915229Subjects--Topical Terms:

3554982
Deep learning.

Using AI Technologies to Solve Software Security Challenges /
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520 $a Artificial intelligence (AI) has been a trending topic in recent years due to the technical breakthroughs in computer vision (CV) and natural language processing (NLP) where many tasks are nearly impossible to be solved by traditional engineering and algorithmic efforts, due their difficulties in generalizing patterns. In the field of software security, such tasks are not uncommon. For example, many tasks require high-level semantic program comprehension, such as code clone detection and logic bug detection. These tasks are similar to NLP tasks to a certain extent, so that existing methods such as static or dynamic program analysis are very ineffective in understanding the programs in terms of the business logic. For another example, information and data available in many software security tasks such as reverse engineering are not human-friendly, which may lead to generating explicit rules or heuristics extremely challenging. Therefore, it is crucial to explore what tasks in software security could be solved or solved better using modern AI technologies.In this dissertation, we aim to adopt AI technologies in three software security subfields: exploitation defense, vulnerability analysis and reverse engineering. In particular, we have researched three problems: 1) defending return-oriented-programming (ROP) attacks using deep learning, 2) finding user privilege related (UPR) variables using LLM workflow, and 3) pinpoint the implementation of anti dynamic analysis techniques in the binary program using LLM. The common characteristics of all three problems are the fuzziness of input and the difficulty to generalize common patterns, which are the key motivations of us adopting AI technologies and data driven methods.
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650 4 $a Malware. $3 3562952
650 4 $a Software upgrading. $3 3680542
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