Data Mining And Machine Learning For Reverse Engineering
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Data Mining and Reverse Engineering
Searching for Semantics: Data Mining, Reverse Engineering Stefano Spaccapietra Fred M aryanski Swiss Federal Institute of Technology University of Connecticut Lausanne, Switzerland Storrs, CT, USA REVIEW AND FUTURE DIRECTIONS In the last few years, database semantics research has turned sharply from a highly theoretical domain to one with more focus on practical aspects. The DS- 7 Working Conference held in October 1997 in Leysin, Switzerland, demon strated the more pragmatic orientation of the current generation of leading researchers. The papers presented at the meeting emphasized the two major areas: the discovery of semantics and semantic data modeling. The work in the latter category indicates that although object-oriented database management systems have emerged as commercially viable prod ucts, many fundamental modeling issues require further investigation. Today's object-oriented systems provide the capability to describe complex objects and include techniques for mapping from a relational database to objects. However, we must further explore the expression of information regarding the dimensions of time and space. Semantic models possess the richness to describe systems containing spatial and temporal data. The challenge of in corporating these features in a manner that promotes efficient manipulation by the subject specialist still requires extensive development.
Data Mining and Machine Learning for Reverse Engineering
"Reverse engineering is fundamental for understanding the inner workings of new malware, exploring new vulnerabilities in existing systems, and identifying patent infringements in the distributed executables. It is the process of getting an in-depth understanding of a given binary executable without its corresponding source code. Reverse engineering is a manually intensive and time-consuming process that relies on a thorough understanding of the full development stack from hardware to applications. It requires a much steeper learning curve than programming. Given the unprecedentedly vast amount of data to be analyzed and the significance of reverse engineering, the overall question that drives the studies in this thesis is how can data mining and machine learning technologies make cybersecurity practitioners more productive to uncover the provenance, understand the intention, and discover the issues behind the data in a scalable way. In this thesis, I focus on two data-driven solutions to help reverse engineers analyzing binary data: assembly clone search and behavioral summarization. Assembly code clone search is emerging as an Information Retrieval (IR) technique that helps address security problems. It has been used for differing binaries to locate the changed parts, identifying known library functions such as encryption, searching for known programming bugs or zero-day vulnerabilities in existing software or Internet of Things (IoT) devices firmware, as well as detecting software plagiarism or GNU license infringements when the source code is unavailable. However, designing an effective search engine is difficult, due to varieties of compiler optimization and obfuscation techniques that make logically similar assembly functions appear to be dramatically different. By working closely with reverse engineers, I identify three different scenarios of reverse engineering and develop novel data mining and machine learning models for assembly clone search to address the respective challenges. By developing an intelligent assembly clone search platform, I optimize the process of reverse engineering by addressing the information needs of reverse engineers. Experimental results suggest that Kam1n0 is accurate, efficient, and scalable for handling a large volume of data.The second part of the thesis goes beyond optimizing an information retrieval process for reverse engineering. I propose to automatically and statically characterize the behaviors of a given binary executable. Behavioral indicators denote those potentially high-risk malicious behaviors exhibited by malware, such as unintended network communications, file encryption, keystroke logging, abnormal registry modifications, sandbox evasion, and camera manipulation. I design a novel neural network architecture that models the different aspects of an executable. It is able to predict over 139 suspicious and malicious behavioral indicators, without running the executable. The resulting system can be used as an additional binary analytic layer to mitigate the issues of polymorphism, metamorphism, and evasive techniques. It also provides another behavioral abstraction of malware to security analysts and reverse engineers. Therefore, it can reduce the data to be manually analyzed, and the reverse engineers can focus on the binaries that are of their interest. In summary, this thesis presents four original research projects that not only advance the knowledge in reverse engineering and data mining, but also contribute to the overall safety of our cyber world by providing open-source award-winning binary analysis systems that empower cybersecurity practitioners"--
Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Author: Elena Marchiori
language: en
Publisher: Springer Science & Business Media
Release Date: 2007-04-02
This book constitutes the refereed proceedings of the 5th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2007, held in Valencia, Spain, April 2007. Coverage brings together experts in computer science with experts in bioinformatics and the biological sciences. It presents contributions on fundamental and theoretical issues along with papers dealing with different applications areas.