Relevant bibliographies by topics / Malware fingerprint

Contents

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

Academic literature on the topic 'Malware fingerprint'

Author: Grafiati
Published: 28 June 2021
Last updated: 29 July 2025

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Journal articles on the topic "Malware fingerprint"

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Botas, Alvaro, Ricardo J. Rodríguez, Vicente Matellan, Juan F. Garcia, M. T. Trobajo, and Miguel V. Carriegos. "On Fingerprinting of Public Malware Analysis Services." Logic Journal of the IGPL 28, no. 4 (2019): 473–86. http://dx.doi.org/10.1093/jigpal/jzz050.

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Abstract Automatic public malware analysis services (PMAS, e.g. VirusTotal, Jotti or ClamAV, to name a few) provide controlled, isolated and virtual environments to analyse malicious software (malware) samples. Unfortunately, malware is currently incorporating techniques to recognize execution onto a virtual or sandbox environment; when an analysis environment is detected, malware behaves as a benign application or even shows no activity. In this work, we present an empirical study and characterization of automatic PMAS, considering 26 different services. We also show a set of features that al
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Ahmed, Hashem El Fiky. "Visual Detection for Android Malware using Deep Learning." International Journal of Innovative Technology and Exploring Engineering (IJITEE) 10, no. 1 (2020): 152–56. https://doi.org/10.35940/ijitee.A8132.1110120.

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The most serious threats to the current mobile internet are Android Malware. In this paper, we proposed a static analysis model that does not need to understand the source code of the android applications. The main idea is as most of the malware variants are created using automatic tools. Also, there are special fingerprint features for each malware family. According to decompiling the android APK, we mapped the Opcodes, sensitive API packages, and high-level risky API functions into three channels of an RGB image respectively. Then we used the deep learning technique convolutional neural netw
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Singh, Jaiteg, Deepak Thakur, Farman Ali, Tanya Gera, and Kyung Sup Kwak. "Deep Feature Extraction and Classification of Android Malware Images." Sensors 20, no. 24 (2020): 7013. http://dx.doi.org/10.3390/s20247013.

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The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl
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Zhao, Jianming, Ziwen Jin, Peng Zeng, Chuan Sheng, and Tianyu Wang. "An Anomaly Detection Method for Oilfield Industrial Control Systems Fine-Tuned Using the Llama3 Model." Applied Sciences 14, no. 20 (2024): 9169. http://dx.doi.org/10.3390/app14209169.

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The device anomaly detection in an industrial control system (ICS) is essential for identifying devices with abnormal operating states or unauthorized access, aiming to protect the ICS from unauthorized access, malware, operational errors, and hardware failures. This paper addresses the issues of numerous manufacturers, complex models, and incomplete information by proposing a fingerprint extraction method based on ICS protocol communication models, applied to an anomaly detection model fine-tuned using the Llama3 model. By considering both hardware and software characteristics of ICS devices,
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Thomson, Amanda, Leandros Maglaras, and Naghmeh Moradpoor. "A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping." Future Internet 17, no. 3 (2025): 120. https://doi.org/10.3390/fi17030120.

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Malicious domains are part of the landscape of the internet but are becoming more prevalent and more dangerous both to companies and to individuals. They can be hosted on various technologies and serve an array of content, including malware, command and control and complex phishing sites that are designed to deceive and expose. Tracking, blocking and detecting such domains is complex, and very often it involves complex allowlist or denylist management or SIEM integration with open-source TLS fingerprinting techniques. Many fingerprinting techniques, such as JARM and JA3, are used by threat hun
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Klopper, Christiaan, and Jan Eloff. "Fingerprinting Network Sessions for the Discovery of Cyber Threats." International Conference on Cyber Warfare and Security 18, no. 1 (2023): 171–80. http://dx.doi.org/10.34190/iccws.18.1.1027.

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A
 rtificial intelligence (AI) assisted cyber-attacks, within the network cybersecurity domain, have evolved to be more successful at every phase of the cyber threat lifecycle. This involves, amongst other tasks, reconnaissance, weaponisation, delivery, exploitation, installation, command & control, and actions. The result has been AI-enhanced attacks, such as DeepLocker, self-learning malware and MalGan, which are highly targeted and undetectable, and automatically exploit vulnerabilities in existing cyber defence systems
 . Countermeasures would require significant improvements
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Białczak, Piotr, and Wojciech Mazurczyk. "Hfinger: Malware HTTP Request Fingerprinting." Entropy 23, no. 5 (2021): 507. http://dx.doi.org/10.3390/e23050507.

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Malicious software utilizes HTTP protocol for communication purposes, creating network traffic that is hard to identify as it blends into the traffic generated by benign applications. To this aim, fingerprinting tools have been developed to help track and identify such traffic by providing a short representation of malicious HTTP requests. However, currently existing tools do not analyze all information included in the HTTP message or analyze it insufficiently. To address these issues, we propose Hfinger, a novel malware HTTP request fingerprinting tool. It extracts information from the parts
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Negera, Worku Gachena, Friedhelm Schwenker, Taye Girma Debelee, Henock Mulugeta Melaku, and Degaga Wolde Feyisa. "Lightweight Model for Botnet Attack Detection in Software Defined Network-Orchestrated IoT." Applied Sciences 13, no. 8 (2023): 4699. http://dx.doi.org/10.3390/app13084699.

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The Internet of things (IoT) is being used in a variety of industries, including agriculture, the military, smart cities and smart grids, and personalized health care. It is also being used to control critical infrastructure. Nevertheless, because the IoT lacks security procedures and lack the processing power to execute computationally costly antimalware apps, they are susceptible to malware attacks. In addition, the conventional method by which malware-detection mechanisms identify a threat is through known malware fingerprints stored in their database. However, with the ever-evolving and dr
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Rafrastara, Fauzi Adi, Catur Supriyanto, Afinzaki Amiral, Syafira Rosa Amalia, Muhammad Daffa Al Fahreza, and Foez Ahmed. "Performance Comparison of k-Nearest Neighbor Algorithm with Various k Values and Distance Metrics for Malware Detection." JURNAL MEDIA INFORMATIKA BUDIDARMA 8, no. 1 (2024): 450. http://dx.doi.org/10.30865/mib.v8i1.6971.

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Malware could evolve and spread very quickly. By these capabilities, malware becomes a threat to anyone who uses a computer, both offline and online. Therefore, research on malware detection is still a hot topic today, due to the need to protect devices or systems from the dangers posed by malware, such as loss/damage of data, data theft, account hacking, and the intrusion of hackers who can control the entire system. Malware has evolved from traditional (monomorphic) to modern forms (polymorphic, metamorphic, and oligomorphic). Conventional antivirus systems cannot detect modern types of viru
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Alrabaee, Saed, Mourad Debbabi, and Lingyu Wang. "A Survey of Binary Code Fingerprinting Approaches: Taxonomy, Methodologies, and Features." ACM Computing Surveys 55, no. 1 (2023): 1–41. http://dx.doi.org/10.1145/3486860.

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Binary code fingerprinting is crucial in many security applications. Examples include malware detection, software infringement, vulnerability analysis, and digital forensics. It is also useful for security researchers and reverse engineers since it enables high fidelity reasoning about the binary code such as revealing the functionality, authorship, libraries used, and vulnerabilities. Numerous studies have investigated binary code with the goal of extracting fingerprints that can illuminate the semantics of a target application. However, extracting fingerprints is a challenging task since a s
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Dissertations / Theses on the topic "Malware fingerprint"

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Varga, Adam. "Identifikace a charakterizace škodlivého chování v grafech chování." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442388.

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Za posledné roky je zaznamenaný nárast prác zahrňujúcich komplexnú detekciu malvéru. Pre potreby zachytenia správania je často vhodné pouziť formát grafov. To je prípad antivírusového programu Avast, ktorého behaviorálny štít deteguje škodlivé správanie a ukladá ich vo forme grafov. Keďže sa jedná o proprietárne riešenie a Avast antivirus pracuje s vlastnou sadou charakterizovaného správania bolo nutné navrhnúť vlastnú metódu detekcie, ktorá bude postavená nad týmito grafmi správania. Táto práca analyzuje grafy správania škodlivého softvéru zachytené behavioralnym štítom antivírusového program
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Books on the topic "Malware fingerprint"

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VERMA, Priyanka, and Komaljeet KAUR. Analysis of Fingerprints and Palmprints Pattern of Identical Twins in Malwa Population, Punjab. Independently Published, 2018.

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Book chapters on the topic "Malware fingerprint"

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Botas, Álvaro, Ricardo J. Rodríguez, Vicente Matellán, and Juan F. García. "Empirical Study to Fingerprint Public Malware Analysis Services." In International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67180-2_57.

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Botvinnik, Marina, Tomer Laor, Thomas Rokicki, Clémentine Maurice, and Yossi Oren. "The Finger in the Power: How to Fingerprint PCs by Monitoring Their Power Consumption." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35504-2_2.

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Pfister, Moritz, Robert Michael, Max Boll, Cosima Körfer, Konrad Rieck, and Daniel Arp. "Listening Between the Bits: Privacy Leaks in Audio Fingerprints." In Detection of Intrusions and Malware, and Vulnerability Assessment. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64171-8_10.

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Hoffmann, Johannes, Stephan Neumann, and Thorsten Holz. "Mobile Malware Detection Based on Energy Fingerprints — A Dead End?" In Research in Attacks, Intrusions, and Defenses. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41284-4_18.

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Erichson, N. Benjamin, Dane Taylor, Qixuan Wu, and Michael W. Mahoney. "Noise-Response Analysis of Deep Neural Networks Quantifies Robustness and Fingerprints Structural Malware." In Proceedings of the 2021 SIAM International Conference on Data Mining (SDM). Society for Industrial and Applied Mathematics, 2021. http://dx.doi.org/10.1137/1.9781611976700.12.

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Conference papers on the topic "Malware fingerprint"

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Matoušek, Petr, Ondřej Ryšavý, and Ivana Burgetová. "Experience Report: Using JA4+ Fingerprints for Malware Detection in Encrypted Traffic." In 2024 20th International Conference on Network and Service Management (CNSM). IEEE, 2024. https://doi.org/10.23919/cnsm62983.2024.10814358.

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Xiaofang, Ban, Chen Li, Hu Weihua, and Wu Qu. "Malware variant detection using similarity search over content fingerprint." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6852216.

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Tao, Lei, and Liang Gu. "An improved fingerprint matching algorithm to detect malware encrypted traffic based on weighted Bayes." In International Conference on Cryptography, Network Security, and Communication Technology (CNSCT 2022), edited by Xiaohao Cai and Wei Wei. SPIE, 2022. http://dx.doi.org/10.1117/12.2635865.

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Journal articles
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