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      Key-area cyberspace mimic defense against data-oriented attacks

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          Abstract

          As modern systems widely deploy protective measures for control data in memory, such as Control-Flow Integrity (CFI), attackers’ ability to manipulate control data is greatly restricted. Consequently, attackers are turning to opportunities to manipulate non-control data in memory (known as Data-Oriented Attacks, or DOAs), which have been proven to pose significant security threats to memory. However, existing techniques to mitigate DOAs often introduce significant overhead due to the indiscriminate protection of a large range of data objects. To address this challenge, this paper adopts a Cyberspace Mimic Defense (CMD) strategy, a generic framework for addressing endogenous security vulnerabilities, to prevent attackers from executing DOAs using known or unknown security flaws. Specifically, we introduce a formalized expression algorithm that assesses whether DOA attackers can construct inputs to exploit vulnerability points. Building on this, we devise a key-area CMD strategy that modifies the coded pathway from input to the vulnerability point, thereby effectively thwarting the activation of the vulnerability. Finally, our experiments on real-world applications and simulation demonstrate that the key-area CMD strategy can effectively prevent DOAs by selectively diversifying parts of the program code.

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          SOK: (State of) The Art of War: Offensive Techniques in Binary Analysis

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            Driller: Augmenting Fuzzing Through Selective Symbolic Execution

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              Just-In-Time Code Reuse: On the Effectiveness of Fine-Grained Address Space Layout Randomization

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                Author and article information

                Contributors
                Journal
                sands
                https://sands.edpsciences.org
                Security and Safety
                Security and Safety
                EDP Sciences and CSPM
                2826-1275
                25 February 2025
                2025
                30 January 2025
                30 January 2025
                : 4
                : ( publisher-idID: sands/2025/01 )
                : 2024015
                Affiliations
                [1 ] Institute of BigData, Fudan University, , Shanghai, 200437, China,
                [2 ] Purple Mountain Laboratories, , Nanjing, 211111, China,
                [3 ] School of Computer Science, Fudan University, , Shanghai, 200433, China,
                Author notes
                [* ]Corresponding authors (email: pchen@ 123456fudan.edu.cn )
                Article
                sands20240008
                10.1051/sands/2024015
                7839670f-7c01-4dbf-a7a8-e831daf6723b
                © The Author(s) 2025. Published by EDP Sciences and China Science Publishing & Media Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 07 May 2024
                : 20 August 2024
                : 15 October 2024
                Page count
                Figures: 10, Tables: 4, Equations: 1, References: 50, Pages: 19
                Funding
                Funded by: 2022YFB3102800
                Categories
                Research Article
                Other Fields
                Custom metadata
                Security and Safety, Vol. 4, 2024015 (2025)
                2025
                2025
                2025
                yes

                Cyberspace mimic defense,Data-oriented attacks,Large language model

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