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Relevant bibliographies by topics / Self-modifying code

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Academic literature on the topic 'Self-modifying code'

Author: Grafiati

Published: 4 June 2021

Last updated: 4 February 2022

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Journal articles on the topic "Self-modifying code"

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Behera, Chandan Kumar, and D. Lalitha Bhaskari. "Self-Modifying Code." International Journal of Secure Software Engineering 8, no. 3 (2017): 24–41. http://dx.doi.org/10.4018/ijsse.2017070102.

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This article describes how code obfuscation techniques aim to conceal the functionality of a program by mystifying the code so that it is unreadable or in an incomprehensible format. Since the objective of this article is to make a program obfuscated, the source code shall appear like normal native code and should be perfectly readable. As nobody can guess that the code is an obfuscated one, obviously it will be very tough to analyze. Nowadays, a lot of programs protect themselves from being analyzed by hiding their behaviors or compressing their semantics by using obfuscation techniques. On t

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Cai, Hongxu, Zhong Shao, and Alexander Vaynberg. "Certified self-modifying code." ACM SIGPLAN Notices 42, no. 6 (2007): 66–77. http://dx.doi.org/10.1145/1273442.1250743.

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Đurić, Goran, Časlav Mitrović, Goran Vorotović, Ivan Blagojević, and Miloš Vasić. "Developing self-modifying code model." Istrazivanja i projektovanja za privredu 14, no. 2 (2016): 239–47. http://dx.doi.org/10.5937/jaes14-10931.

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Qiu, Jing, Xiao Hong Su, and Pei Jun Ma. "A New Disassembly Approach for Binary Code Using Dynamic Multiple-Path Exploration and Static Disassembly." Applied Mechanics and Materials 577 (July 2014): 852–55. http://dx.doi.org/10.4028/www.scientific.net/amm.577.852.

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We present a new approach for disassembling executables with self-modifying code. Self-modifying code is very common in malware. Conventional static or dynamic approaches cannot handle self-modifying code very well. We combine static and dynamic analysis to fight against self-modifying code with the multiple-path exploration technique. The evaluation results indicate that our approach works well in disassembling executables with self-modifying code with high precision and code coverage compared with the state-of-art disassembler.

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Mody, R. P. "Functional programming is not self-modifying code." ACM SIGPLAN Notices 27, no. 11 (1992): 13–14. http://dx.doi.org/10.1145/141018.141021.

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Shan, Liang, and Sabu Emmanuel. "Mobile Agent Protection with Self-Modifying Code." Journal of Signal Processing Systems 65, no. 1 (2010): 105–16. http://dx.doi.org/10.1007/s11265-010-0548-8.

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Mavrogiannopoulos, Nikos, Nessim Kisserli, and Bart Preneel. "A taxonomy of self-modifying code for obfuscation." Computers & Security 30, no. 8 (2011): 679–91. http://dx.doi.org/10.1016/j.cose.2011.08.007.

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Blazy, Sandrine, Vincent Laporte, and David Pichardie. "Verified Abstract Interpretation Techniques for Disassembling Low-level Self-modifying Code." Journal of Automated Reasoning 56, no. 3 (2016): 283–308. http://dx.doi.org/10.1007/s10817-015-9359-8.

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Ansel, Jason, Petr Marchenko, Úlfar Erlingsson, et al. "Language-independent sandboxing of just-in-time compilation and self-modifying code." ACM SIGPLAN Notices 47, no. 6 (2012): 355. http://dx.doi.org/10.1145/2345156.1993540.

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Balachandran, Vivek, and Sabu Emmanuel. "Potent and Stealthy Control Flow Obfuscation by Stack Based Self-Modifying Code." IEEE Transactions on Information Forensics and Security 8, no. 4 (2013): 669–81. http://dx.doi.org/10.1109/tifs.2013.2250964.

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Dissertations / Theses on the topic "Self-modifying code"

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Ye, Xin. "Model checking self modifying code." Thesis, Université de Paris (2019-....), 2019. http://www.theses.fr/2019UNIP7010.

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Le code auto-modifiant est un code qui modifie ses propres instructions pendant le temps d'exécution. Il est aujourd'hui largement utilisé, notamment dans les logiciels malveillants pour rendre le code difficile à analyser et à été détecté par les anti-virus. Ainsi, l'analyse de tels programmes d'auto-modifiant est un grand défi. Pushdown System(PDSs) est un modèle naturel qui est largement utilisé pour l'analyse des programmes séquentiels car il permet de modéliser précisément les appels de procédures et de simuler la pile du programme. Dans cette thèse, nous proposons d'étendre le modèle du

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Pan, Yen-Chien, and 潘彥謙. "Self-modifying Code Detection and Protection on Android System." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/37550703715962474447.

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碩士<br>國立臺灣大學<br>資訊工程學研究所<br>102<br>The numbers of Android mobile devices and applications are both increased dramatically these years, but unfortunately, so are malwares. While there are a lot of anti-virus applications on Android systems, malwares usually use various tricks to prevent themselves from being detected. Self-modification is a novel technique on Android system which allows applications to hide its actual code. In this paper, we propose a detection method to help detect this type of malware, and based on the detection result, we further developed a mechanism to protect users from t

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Books on the topic "Self-modifying code"

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Tse, Peter U. Two Types of Libertarian Free Will Are Realized in the Human Brain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190460723.003.0010.

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In Chapter 10, Peter U. Tse describes various developments in neuroscience that reveal how volitional mental events can be causal within a physicalist paradigm and argues that two types of libertarian free will are realized in the human brain. He takes as his foundation a new understanding of the neural code that emphasizes rapid synaptic resetting over the traditional emphasis of neural spiking. Such a neural code is an instance of “criterial causation,” which requires modifying standard interventionist conceptions of causation. This new view of the neural code, Tse argues, also provides a wa

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Book chapters on the topic "Self-modifying code"

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Anckaert, Bertrand, Matias Madou, and Koen De Bosschere. "A Model for Self-Modifying Code." In Information Hiding. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74124-4_16.

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Tschudin, Christian, and Lidia Yamamoto. "Harnessing Self-modifying Code for Resilient Software." In Innovative Concepts for Autonomic and Agent-Based Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11964995_17.

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Blazy, Sandrine, Vincent Laporte, and David Pichardie. "Verified Abstract Interpretation Techniques for Disassembling Low-level Self-modifying Code." In Interactive Theorem Proving. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08970-6_9.

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Armstrong, Alasdair, Brian Campbell, Ben Simner, Christopher Pulte, and Peter Sewell. "Isla: Integrating Full-Scale ISA Semantics and Axiomatic Concurrency Models." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81685-8_14.

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AbstractArchitecture specifications such as Armv8-A and RISC-V are the ultimate foundation for software verification and the correctness criteria for hardware verification. They should define the allowed sequential and relaxed-memory concurrency behaviour of programs, but hitherto there has been no integration of full-scale instruction-set architecture (ISA) semantics with axiomatic concurrency models, either in mathematics or in tools. These ISA semantics can be surprisingly large and intricate, e.g. 100k+ lines for Armv8-A. In this paper we present a tool, Isla, for computing the allowed behaviours of concurrent litmus tests with respect to full-scale ISA definitions, in Sail, and arbitrary axiomatic relaxed-memory concurrency models, in the Cat language. It is based on a generic symbolic engine for Sail ISA specifications, which should be valuable also for other verification tasks. We equip the tool with a web interface to make it widely accessible, and illustrate and evaluate it for Armv8-A and RISC-V. By using full-scale and authoritative ISA semantics, this lets one evaluate litmus tests using arbitrary user instructions with high confidence. Moreover, because these ISA specifications give detailed and validated definitions of the sequential aspects of systems functionality, as used by hypervisors and operating systems, e.g. instruction fetch, exceptions, and address translation, our tool provides a basis for developing concurrency semantics for these. We demonstrate this for the Armv8-A instruction-fetch model and self-modifying code examples of Simner et al.

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Vasudevan, Amit. "Effective Malware Analysis Using Stealth Breakpoints." In Threats, Countermeasures, and Advances in Applied Information Security. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0978-5.ch023.

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Fine-grained malware analysis requires various powerful analysis tools. Chief among them is a debugger that enables runtime binary analysis at the instruction level. One of the important services provided by a debugger is the ability to stop execution of code at arbitrary points during runtime, using breakpoints. Software breakpoints change the code being analyzed so that it can be interrupted during runtime. Most, if not all malware are very sensitive to code modification with self-modifying and/or self-checking capabilities, rendering the use of software breakpoints limited in their scope. Hardware breakpoints on the other hand, use a subset of the CPU registers and exception mechanisms to provide breakpoints that do not entail code modification. However, hardware breakpoints support limited breakpoint ability (typically only 2-4 locations) and are susceptible to various anti-debugging techniques employed by malware. This chapter describes a novel breakpoint technique (called stealth breakpoints) that provides unlimited number of breakpoints which are robust to detection and countering mechanisms. Further, stealth breakpoints retain all the features (code, data and I/O breakpoint abilities) of existing hardware and software breakpoint schemes and enables easy integration with existing debuggers.

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"Deep Learning Theory and Software." In Advances in Computer and Electrical Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1554-9.ch002.

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In the past decade, deep learning has achieved a significant breakthrough in development. In addition to the emergence of convolution, the most important is self-learning of deep neural networks. By self-learning methods, adaptive weights of kernels and built-in parameters or interconnections are automatically modified such that the error rate is reduced along the learning process, and the recognition rate is improved. Emulating mechanism of the brain, it can have accurate recognition ability after learning. One of the most important self-learning methods is back-propagation (BP). The current BP method is indeed a systematic way of calculating the gradient of the loss with respect to adaptive interconnections. The main core of the gradient descent method addresses on modifying the weights negatively proportional to the determined gradient of the loss function, subsequently reducing the error of the network response in comparison with the standard answer. The basic assumption for this type of the gradient-based self-learning is that the loss function is the first-order differential.

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McAnally, Heath B., and Beth Darnall. "The Primacy of Motivation in Preoperative Optimization." In Preoperative Optimization of the Chronic Pain Patient. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780190920142.003.0003.

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Surgical patients with chronic pain frequently neglect basic health behaviors (e.g., maintenance of appropriate diet, sleep hygiene, exercise patterns) while engaging in harmful ones (e.g., tobacco use, alcohol excess, chronic opioid consumption) all of which have been shown to confer poor postoperative outcomes. Effective preoperative optimization of patients suffering with chronic pain requires at least a basic understanding of the complex and heterogeneous motivation behind deliberative (and subconscious) behaviors, and the application of interventions supporting self-determined elimination of toxic cognitive-behavioral patterns and their replacement with healthy ones. This chapter begins with an overview of motivational interviewing and select positive literature reviews addressing the utility of the method in modifying behaviors pertinent to this program. A survey of the development of our understanding of core common elements of human motivation is presented, followed by a biopsychosocial-spiritual framework for considering interpersonal variance; the chapter concludes with a brief consideration of dynamic intrapersonal variables affecting motivation within the individual.

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Conference papers on the topic "Self-modifying code"

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Cai, Hongxu, Zhong Shao, and Alexander Vaynberg. "Certified self-modifying code." In the 2007 ACM SIGPLAN conference. ACM Press, 2007. http://dx.doi.org/10.1145/1250734.1250743.

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Touili, Tayssir, and Xin Ye. "Reachability Analysis of Self Modifying Code." In 2017 22nd International Conference on Engineering of Complex Computer Systems (ICECCS). IEEE, 2017. http://dx.doi.org/10.1109/iceccs.2017.19.

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Debray, Saumya, and Jay Patel. "Reverse Engineering Self-Modifying Code: Unpacker Extraction." In 2010 17th Working Conference on Reverse Engineering (WCRE). IEEE, 2010. http://dx.doi.org/10.1109/wcre.2010.22.

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Touili, Tayssir, and Xin Ye. "LTL Model Checking of Self Modifying Code." In 2019 24th International Conference on Engineering of Complex Computer Systems (ICECCS). IEEE, 2019. http://dx.doi.org/10.1109/iceccs.2019.00008.

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Touili, Tayssir, and Xin Ye. "CTL Model Checking of Self Modifying Code." In 2020 25th International Conference on Engineering of Complex Computer Systems (ICECCS). IEEE, 2020. http://dx.doi.org/10.1109/iceccs51672.2020.00009.

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Kamunyori, Joy. "Handling self-modifying code using software dynamic translation." In the 2007 conference. ACM Press, 2007. http://dx.doi.org/10.1145/1347787.1347807.

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Gnaedig, Isabelle, Matthieu Kaczmarek, Daniel Reynaud, and Stephane Wloka. "Unconditional self-modifying code elimination with dynamic compiler optimizations." In 2010 5th International Conference on Malicious and Unwanted Software (MALWARE). IEEE, 2010. http://dx.doi.org/10.1109/malware.2010.5665795.

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Dawei, Shi, Lv Delong, and Ye Zhibin. "Dynamic Self-modifying Code Detection Based on Backward Analysis." In ICCAE 2018: 2018 10th International Conference on Computer and Automation Engineering. ACM, 2018. http://dx.doi.org/10.1145/3192975.3193016.

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Xianya, Mi, Zhang Yi, Wang Baosheng, and Tang Yong. "A Survey of Software Protection Methods Based on Self-Modifying Code." In 2015 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2015. http://dx.doi.org/10.1109/cicn.2015.121.

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Liu, Anzhan, and Wenqi Wang. "ASCMS: An Accurate Self-Modifying Code Cache Management Strategy in Binary Translation." In 2013 International Conference on Information Science and Cloud Computing Companion (ISCC-C). IEEE, 2013. http://dx.doi.org/10.1109/iscc-c.2013.52.

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