Relevant bibliographies by topics / Malware Analysis

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Malware Analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Malware Analysis"

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Bavishi, Ujaliben Kalpesh, and Bhavesh Madanlal Jain. "Malware Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (2018): 27. http://dx.doi.org/10.23956/ijarcsse.v7i12.507.

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Malware, also known as malicious software affects the user’s computer system or mobile devices by exploiting the system’s vulnerabilities. It is a major threat to the security of the computer systems. Some of the types of malwares that are most commonly used are viruses, trojans, worms, etc. Nowadays, there is a widespread use of malware which allows malware author to get sensitive information like bank details, contact information which is a serious threat in the world. Most of the malwares are spread through internet because of its frequent use which can destroy large systems piercing throug
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Chintha, Prithvi, and Kakelli Anil Kumar. "EMERGING MACHINE LEARNING TECHNIQUES IN MALWARE DETECTION AND ANALYSIS: A COMPARATIVE ANALYSIS." International Journal of Advanced Research 8, no. 10 (2020): 771–79. http://dx.doi.org/10.21474/ijar01/11900.

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New types of malware with unique characteristics are being created daily in legion. This exponential increase in malwareis creating a threat to the internet. From the past decade, various techniques of malware analysis and malware detection have been developed to prevent the efficacy of malware. However, due to the fast-growing numbers and complexities in malware, it is getting difficult to detect and analyze the malware manually. Because of the inefficiency in manual malware analysis, automated malware detection and analysis would be a better solution. Thus, malware analysis supported by mach
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Panwala, Harshitkumar R. "A Methodological Study on Malware Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 450–52. http://dx.doi.org/10.22214/ijraset.2021.38416.

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Abstract: Malware is an executable binary that is designed to be malicious. Malware can be used by attackers to carry out a range of malicious operations, such as spying on the victim using keyloggers or remote access tools (RATs) or deleting or encrypting data for "Ransom" payments. Malware is software that is designed to carry out malicious operations, and it comes in a variety of forms. Malware's impact, according to studies, is escalating. There are several tools available for malware analysis. The present study is the analysis of the malware known as “Malware Analysis”. Malware analysis i
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Krishna, T. Shiva Rama. "Malware Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 1847–53. http://dx.doi.org/10.22214/ijraset.2021.35426.

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Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malw
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Srivastava, Prerna, and Mohan Raj. "Feature extraction for enhanced malware detection using genetic algorithm." International Journal of Engineering & Technology 7, no. 2.8 (2018): 444. http://dx.doi.org/10.14419/ijet.v7i2.8.10479.

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The use of internet has affected almost every field today. With the increase in use of internet, the number of malwares affecting the systems has also increased to a great deal. A number of techniques have been developed by the researchers in order to detect these malwares. The Malware Detection consists of two parts, the analysis part and the detection part. Malwares analysis can be categorized into Static analysis, Dynamic analysis and Hybrid Analysis. The Detection techniques can broadly be classified into Signature based techniques and Behaviour based techniques. A brief introduction of Ma
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Suryati, One Tika, and Avon Budiono. "Impact Analysis of Malware Based on Call Network API With Heuristic Detection Method." International Journal of Advances in Data and Information Systems 1, no. 1 (2020): 1–8. http://dx.doi.org/10.25008/ijadis.v1i1.176.

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Malware is a program that has a negative influence on computer systems that don't have user permissions. The purpose of making malware by hackers is to get profits in an illegal way. Therefore, we need a malware analysis. Malware analysis aims to determine the specifics of malware so that security can be built to protect computer devices. One method for analyzing malware is heuristic detection. Heuristic detection is an analytical method that allows finding new types of malware in a file or application. Many malwares are made to attack through the internet because of technological advancements
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Jang, Jae-wook, and Huy Kang Kim. "Function-Oriented Mobile Malware Analysis as First Aid." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/6707524.

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Recently, highly well-crafted mobile malware has arisen as mobile devices manage highly valuable and sensitive information. Currently, it is impossible to detect and prevent all malware because the amount of new malware continues to increase exponentially; malware detection methods need to improve in order to respond quickly and effectively to malware. For the quick response, revealing the main purpose or functions of captured malware is important; however, only few recent works have attempted to find malware’s main purpose. Our approach is designed to help with efficient and effective inciden
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John Oluwafemi Ogun. "Advancements in automated malware analysis: evaluating the efficacy of open-source tools in detecting and mitigating emerging malware threats to US businesses." International Journal of Science and Research Archive 12, no. 2 (2024): 1958–64. http://dx.doi.org/10.30574/ijsra.2024.12.2.1488.

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Malware, short for malicious software, represents a significant and evolving threat to computer systems, targeting individuals, corporations, and governments globally. This paper explores the multifaceted nature of malware, which includes viruses, worms, Trojans, and more, and delves into how they compromise systems by disrupting services, stealing sensitive data, and denying access. Modern malware is increasingly sophisticated, evading traditional detection methods and posing challenges to cybersecurity professionals. This review outlines key methodologies in malware analysis, including MARE
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Muath Alrammal, Muath Alrammal, Munir Naveed Muath Alrammal, Suzan Sallam Munir Naveed, and Georgios Tsaramirsis Suzan Sallam. "A Critical Analysis on Android Vulnerabilities, Malware, Anti-malware and Anti-malware Bypassing." 網際網路技術學刊 23, no. 7 (2022): 1651–61. http://dx.doi.org/10.53106/160792642022122307019.

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<p>Android has become the dominant operating system for portable devices, making it a valuable asset that needs protection. Though Android is very popular; it has several vulnerabilities which attackers use for malicious intents. In this paper, we present a comprehensive study on the threats in Android OS that various malware developers exploit and the different malware functionality based on Android’s threats. Furthermore, we analyze and evaluate the anti-malware approaches implemented to face the malware functionalities. Finally, we analyze and categorize malware developers&a
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Dener, Murat, Gökçe Ok, and Abdullah Orman. "Malware Detection Using Memory Analysis Data in Big Data Environment." Applied Sciences 12, no. 17 (2022): 8604. http://dx.doi.org/10.3390/app12178604.

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Malware is a significant threat that has grown with the spread of technology. This makes detecting malware a critical issue. Static and dynamic methods are widely used in the detection of malware. However, traditional static and dynamic malware detection methods may fall short in advanced malware detection. Data obtained through memory analysis can provide important insights into the behavior and patterns of malware. This is because malwares leave various traces on memories. For this reason, the memory analysis method is one of the issues that should be studied in malware detection. In this st
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Dissertations / Theses on the topic "Malware Analysis"

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Iqbal, Muhammad Shahid, and Muhammad Sohail. "Runtime Analysis of Malware." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2930.

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Context: Every day increasing number of malwares are spreading around the world and infecting not only end users but also large organizations. This results in massive security threat for private data and expensive computer resources. There is lot of research going on to cope up with this large amount of malicious software. Researchers and practitioners developed many new methods to deal with them. One of the most effective methods used to capture malicious software is dynamic malware analysis. Dynamic analysis methods used today are very time consuming and resource greedy. Normally it could ta
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Král, Benjamin. "Forenzní analýza malware." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385910.

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This master's thesis describes methodologies used in malware forensic analysis including methods used in static and dynamic analysis. Based on those methods a tool intended to be used by Computer Security Incident Response Teams (CSIRT) is designed to allow fast analysis and decisions regarding malware samples in security incident investigations. The design of this tool is thorougly described in the work along with the tool's requirements on which the tool design is based on. Based on the design a ForensIRT tool is implemented and then used to analyze a malware sample Cridex to demonstrate its
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Wedum, Petter Langeland. "Malware Analysis; : A Systematic Approach." Thesis, Norwegian University of Science and Technology, Department of Telematics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8948.

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<p>An almost incomprehensible amount of data and information is stored on millions and millions of computers worldwide. The computers, interconnected in local, national and international networks, use and share a high number of various software programs. Individuals, corporations, hospitals, communication networks, authorities among others are totally dependent on the reliability and accessibility of the data and information stored, and on the correct and predictable operation of the soft ware programs, the computers and the networks connecting them. Malware types have different objectives and
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Santoro, Tiziano. "Automatic behavioural analysis of malware." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-64103.

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With malware becoming more and more diused and at the same time more sophisticatedin its attack techniques, countermeasures need to be set up so that new kinds ofthreats can be identied and dismantled in the shortest possible time, before they causeharm to the system under attack. With new behaviour patterns like the one shown bypolymorphic and metamorphic viruses, static analysis is not any more a reliable wayto detect those threats, and behaviour analysis seems a good candidate to ght againstthe next-generation families of viruses. In this project, we describe a methodology toanalyze and cat
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Kim, Ye Kyung. "Framework for Analysis of Android Malware." University of Akron / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=akron1418252974.

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Subramanian, Nandita. "Analysis of Rank Distance for Malware Classification." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1479823187035784.

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Gorugantu, Swetha. "Malware Analysis Skills Taught in University Courses." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527083698607394.

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Cozzi, Emanuele. "Binary Analysis for Linux and IoT Malware." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS197.

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Au cours des deux dernières décennies, la communauté de la sécurité a lutté contre les programmes malveillants pour les systèmes d’exploitation basés sur Windows. Cependant, le nombre croissant de dispositifs embarqués interconnectés et la révolution de l’IoT modifient rapidement le paysage des logiciels malveillants. Les acteurs malveillants ne sont pas restés les bras croisés, mais ont rapidement réagi pour créer des “logiciels malveillants Linux”. Par cette thèse, nous naviguons dans le monde des logiciels malveillants basés sur Linux et mettons en évidence les problèmes que nous devons sur
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Cortellazzi, Jacopo. "Code transplantation for adversarial malware." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17288/.

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In the nefarious fight against attackers, a wide range of smart algorithms have been introduced, in order to block and even prevent new families of malware before their appearance. Machine learning, for instance, recently gained a lot of attention thanks to its ability to use generalization to possibly detect never-before-seen attacks or variants of a known one. During the past years, a lot of works have tested the strength of machine learning in the cybersecurity field, exploring its potentialities and weaknesses. In particular, various studies highlighted its robustness against adversarial a
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Webb, Matthew S. "Evaluating tool based automated malware analysis through persistence mechanism detection." Thesis, Kansas State University, 2018. http://hdl.handle.net/2097/38783.

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Master of Science<br>Department of Computer Science<br>Eugene Vasserman<br>Since 2014 there have been over 120 million new malicious programs registered every year. Due to the amount of new malware appearing every year, analysts have automated large sections of the malware reverse engineering process. Many automated analysis systems are created by re-implementing analysis techniques rather than automating existing tools that utilize the same techniques. New implementations take longer to create and do not have the same proven quality as a tool that evolved alongside malware for many years. Th
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Books on the topic "Malware Analysis"

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Yin, Heng, and Dawn Song. Automatic Malware Analysis. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5523-3.

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Shane, Hartman, Morales Jose Andre, Quintans Manu, and Strazzere Tim, eds. Android malware and analysis. CRC Press, Taylor & Francis Group, 2014.

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Mohanta, Abhijit, and Anoop Saldanha. Malware Analysis and Detection Engineering. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6193-4.

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Yin, Heng. Automatic Malware Analysis: An Emulator Based Approach. Springer New York, 2013.

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Stamp, Mark, Mamoun Alazab, and Andrii Shalaginov, eds. Malware Analysis Using Artificial Intelligence and Deep Learning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62582-5.

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Andrew, Honig, ed. Practical Malware Analysis: The Hands-On Guide to Dissecting Malicious Software. No Starch Press, 2012.

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Oktavianto, Digit, and Iqbal Muhardianto. Cuckoo Malware Analysis. Packt Publishing, Limited, 2013.

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Oktavianto, Digit, and Iqbal Muhardianto. Cuckoo Malware Analysis. Packt Publishing, 2013.

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Advanced Malware Analysis. McGraw-Hill Education, 2015.

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Dunham, Ken, Shane Hartman, Manu Quintans, Jose Andre Morales, and Tim Strazzere. Android Malware and Analysis. Auerbach Publications, 2014. http://dx.doi.org/10.1201/b17598.

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Book chapters on the topic "Malware Analysis"

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Kävrestad, Joakim. "Malware Analysis." In Fundamentals of Digital Forensics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96319-8_18.

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Kävrestad, Joakim. "Malware Analysis." In Fundamentals of Digital Forensics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38954-3_21.

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Patil, Nikita, Amit Kumar Singh, and Sushil Kumar. "Malware Analysis." In Software Engineering Approaches to Enable Digital Transformation Technologies. Routledge, 2023. http://dx.doi.org/10.1201/9781003441601-8.

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Kävrestad, Joakim, Marcus Birath, and Nathan Clarke. "Malware Analysis." In Texts in Computer Science. Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-53649-6_21.

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Kiperberg, Michael, Amit Resh, and Nezer Zaidenberg. "Malware Analysis." In Computational Methods in Applied Sciences. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-91293-2_21.

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Mohanta, Abhijit, and Anoop Saldanha. "Malware Packers." In Malware Analysis and Detection Engineering. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6193-4_7.

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Steffens, Timo. "Analysis of Malware." In Attribution of Advanced Persistent Threats. Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-61313-9_3.

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Kang, Brent Byung Hoon, and Anurag Srivastava. "Dynamic Malware Analysis." In Encyclopedia of Cryptography and Security. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_846.

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Lin, Xiaodong. "Introductory Malware Analysis." In Introductory Computer Forensics. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00581-8_18.

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Kang, Brent Byung Hoon, and Anurag Srivastava. "Dynamic Malware Analysis." In Encyclopedia of Cryptography, Security and Privacy. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-030-71522-9_846.

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Conference papers on the topic "Malware Analysis"

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Prashanthi, Vempaty, and Srinivas Kanakala. "Malware Analysis using Deep Learning." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N). IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895201.

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M, Harini Sri, Harsha Vardhan G, Keerthana R, N. Digvijay, and Rohith Vaidya K. "Graph Based Malware Analysis API." In 2025 8th International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2025. https://doi.org/10.1109/icoei65986.2025.11013332.

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Kunwar, Rakesh Singh, and Priyanka Sharma. "Malware Analysis." In the Second International Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905361.

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Clincy, Victor, and Hossain Shahriar. "IoT Malware Analysis." In 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE, 2019. http://dx.doi.org/10.1109/compsac.2019.00141.

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Andrade, Cesar Augusto Borges de, Claudio Gomes de Mello, and Julio Cesar Duarte. "Malware Automatic Analysis." In 2013 BRICS Congress on Computational Intelligence & 11th Brazilian Congress on Computational Intelligence (BRICS-CCI & CBIC). IEEE, 2013. http://dx.doi.org/10.1109/brics-cci-cbic.2013.119.

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Khalil, Md Yusuf, Vivek, Kumar Anand, Antarlina Paul, and Rahul Grover. "PDF Malware Analysis." In 2022 7th International Conference on Computing, Communication and Security (ICCCS). IEEE, 2022. http://dx.doi.org/10.1109/icccs55188.2022.10079419.

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Chiang, Ken. "FARM - Malware Information Sharing (Automated Malware Analysis Symposium." In Proposed for presentation at the Automated Malware Analysis Symposium held June 8, 2021 in Linthicum, MD. US DOE, 2021. http://dx.doi.org/10.2172/1871930.

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Botacin, Marcus Felipe, Paulo Lício de Geus, and André Grégio. "Hardware-Assisted Malware Analysis." In Anais Estendidos do Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/sbseg_estendido.2018.4136.

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Malicious software (malware) are persistent threats to modern computer systems and the development of countermeasures to them becomes harder each day due to the emergence of anti-analysis and anti-forensics techniques, able to evade software-based monitoring solutions. In this scenario, hardware-assisted solutions are effective alternatives, but still present development gaps. The presented dissertation surveyed the limits of software-based solutions, pinpointed the existing development gaps on hardware-assisted solutions and introduced a lightweight, hardware-based alternative for malware ana
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Botacin, Marcus, Vitor Falcão da Rocha, Paulo Lício de Geus, and André Grégio. "Analysis, Anti-Analysis, Anti-Anti-Analysis: An Overview of the Evasive Malware Scenario." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2017. http://dx.doi.org/10.5753/sbseg.2017.19504.

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Malicious programs are persistent threats to computer systems, and their damages extend from financial losses to critical infrastructure attacks. Malware analysis aims to provide useful information to be used for forensic procedures and countermeasures development. To thwart that, attackers make use of anti-analysis techniques that prevent or difficult their malware from being analyzed. These techniques rely on instruction side-effects and that system&amp;apos;s structure checks are inspection-aware. Thus, detecting evasion attempts is an important step of any successful investigative procedur
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Oh, Tae, Hwa Shin Moon, Young Ho Kim, Jeong Neyo Kim, and Bill Stackpole. "Android malware analysis and conceptual malware mitigation approaches." In 2016 International Conference on Information and Communication Technology Convergence (ICTC). IEEE, 2016. http://dx.doi.org/10.1109/ictc.2016.7763562.

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Reports on the topic "Malware Analysis"

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Peppers, Joseph. Creating a Malware Analysis Lab and Basic Malware Analysis. Iowa State University, 2018. http://dx.doi.org/10.31274/cc-20240624-440.

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Pearce, Lauren. Windows Internals and Malware Behavior: Malware Analysis Day 3. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1457289.

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Pearce, Lauren. Covert Malware Launching and Data Encoding: Malware Analysis Day 5. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1457292.

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Pearce, Lauren. 2018 CyberFire East: Malware Analysis. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1475328.

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Pearce, Lauren. Incident Response and Malware Analysis. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1481123.

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Pearce, Lauren. Malware Analysis in a Nutshell. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1330062.

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Pearce, Lauren. Malware Analysis Report for SolarWinds.Orion.Core.BusinessLayer.dll. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1813804.

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Pearce, Lauren. Malicious Docs: Malware Analysis Day 7. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1457294.

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Amornrattanapong, Weeraphat. Malware Analysis Day 2 - Part 2. Office of Scientific and Technical Information (OSTI), 2019. http://dx.doi.org/10.2172/1574734.

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Takeda, Keiji. Development of Automated Malware Analysis Framework. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada637212.

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