Relevant bibliographies by topics / Malware recognition

Contents

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

Academic literature on the topic 'Malware recognition'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Malware recognition"

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Ibrahim, Laheeb M., Maisirreem Atheeed Kamal, and AbdulSattar A. Al-Alusi. "Hancitor malware recognition using swarm intelligent technique." Computer Science and Information Technologies 2, no. 3 (2020): 103–12. http://dx.doi.org/10.11591/csit.v2i3.p103-112.

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Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. Swarm Intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks.
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Laheeb M. Ibrahim, Maisirreem Atheeed Kamal, and AbdulSattar A. Al-Alusi. "Hancitor malware recognition using swarm intelligent technique." Computer Science and Information Technologies 2, no. 3 (2021): 103–12. http://dx.doi.org/10.11591/csit.v2i3.pp103-112.

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Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. swarm intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tasks.
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Laheeb, M. Ibrahim, Atheeed Kamal Maisirreem, and A. Al-Alusi AbdulSattar. "Hancitor malware recognition using swarm intelligent technique." Computer Science and Information Technologies 2, no. 3 (2021): 103–12. https://doi.org/10.11591/csit.v2i3.p103-112.

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Malware is a global risk rife designed to destroy computer systems without the owner's knowledge. It is still regarded as the most popular threat that attacks computer systems. Early recognition of unknown malware remains a problem. swarm intelligence (SI), usually customer societies, communicate locally with their domain and with each other. Clients use very simple rules of behavior and the interactions between them lead to smart appearance, noticeable, individual behavior and optimized solution of problem and SI has been successfully applied in many fields, especially for malware ion tas
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Mamoona Rafique Khan, Rana Muhammad Nadeem, Sadia Latif, and Rabia Tariq. "AN INVESTIGATION INTO THE APPLICATION OF DEEP CONVOLUTIONAL NEURAL NETWORKS FOR MALWARE DETECTION." Kashf Journal of Multidisciplinary Research 2, no. 04 (2025): 216–34. https://doi.org/10.71146/kjmr409.

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Cyber security is facing a huge threat from malwares motivator and their mass production due to its mutation factors, which results in enormous production of these binaries in short time. Moreover, the domain of malicious intents is also progressing with the increase of compute intensive resources. To detect and highlight these malicious binaries, classification plays a vital rule in listing these malwares as malware by nominating interesting features and trends among them. In this situation, we investigated the application of transfer learning using the EfficientNetV2 architecture for automat
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Syam, Gopi, Susan Jacob Evelyn, John Joel, Rajeev Raynell, and Alex Steve. "Malware Classification using Image Analysis." International Journal on Emerging Research Areas (IJERA) 05, no. 01 (2025): 178–82. https://doi.org/10.5281/zenodo.15289798.

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Abstract—Malware detection and classification have evolved significantly with the integration of pattern recognition and image classification techniques. A pioneering study by Nataraj et al. (2011) [1] demonstrated that malware binaries could be visualized as grayscale images, revealing structural and textural similarities among malware families. Inspired by this approach, this research explores the effectiveness of deep learning-based architectures, specifically the hybrid CoatNet model, in improving malware classification accuracy. Using the MalImg dataset, we investigate the performan
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Prof. C. Ranjeeth Kumar. "Malware Detection Using Remedimorbus Application." International Journal of New Practices in Management and Engineering 9, no. 01 (2020): 08–15. http://dx.doi.org/10.17762/ijnpme.v9i01.82.

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As a huge number of new malware tests rise each day, traditional malware recognition strategies are not sufficient. Static examination strategies, for example, report signature, fail to recognize obscure projects. Dynamic examination techniques have low execution and over the top bogus positive charge. A discovery method that could adjust to the quickly changing malware condition is required. The paper introduced a spic and span malware identification approach the utilization of machine picking up information on. This paper proposes an answer where some of the gadget contemplating calculations
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Jing Li, Jing Li, and Xueping Luo Jing Li. "Malware Family Classification Based on Vision Transformer." 電腦學刊 34, no. 1 (2023): 087–99. http://dx.doi.org/10.53106/199115992023023401007.

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<p>Cybersecurity worries intensify as Big Data, the Internet of Things, and 5G technologies develop. Based on code reuse technologies, malware creators are producing new malware quickly, and new malware is continually endangering the effectiveness of existing detection methods. We propose a vision transformer-based approach for malware picture identification because, in contrast to CNN, Transformer’s self-attentive process is not constrained by local interactions and can simultaneously compute long-range mine relationships. We use ViT-B/16 weights pre-trained on the ImageNet21k dataset t
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Bruno, L. Sena Guilherme C. Paternezi Rhian Bottura Vinicius de Carvalho Bastos, Marise Miranda Dra, Americo Talarico Dr, and Domingos Sanches Esp. "WannaCry Malware Detection Using Bayesian Network Modeling." Sptech World Journal Vol 3, Edition 003 (2023): 21. https://doi.org/10.5281/zenodo.10511602.

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In recent years, the increasing number of cyberattacks targeting organizations of various sizes and industries has led companies to allocate significant portions of their budgets to the Information Security sector, with the objective of preventing and mitigating these attacks and thus reducing the potential financial losses associated with them. Various defense mechanisms and security practices can be applied at different layers of business infrastructure, and one of those practices is related to the concept of Cyber Threat Intelligence (CTI). The purpose of a CTI application is to acquire val
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Dube, Thomas E., Richard A. Raines, Michael R. Grimaila, Kenneth W. Bauer, and Steven K. Rogers. "Malware Target Recognition of Unknown Threats." IEEE Systems Journal 7, no. 3 (2013): 467–77. http://dx.doi.org/10.1109/jsyst.2012.2221913.

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Urzay, Iñaki. "Collective intelligence approaches to malware recognition." Network Security 2008, no. 5 (2008): 14–16. http://dx.doi.org/10.1016/s1353-4858(08)70065-5.

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Dissertations / Theses on the topic "Malware recognition"

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Redfern, Cory. "Malware Recognition by Properties of Executables." ScholarWorks@UNO, 2009. http://scholarworks.uno.edu/td/1013.

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This thesis explores what patterns, if any, exist to differentiate non-malware from malware, given only a sequence of raw bytes composing either a received file or a fixed-length initial segment of a received file. If any such patterns are found, their effectiveness as filtering criteria is investigated.
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Dietrich, Christian [Verfasser], and Felix [Akademischer Betreuer] Freiling. "Identification and Recognition of Remote-Controlled Malware / Christian Dietrich. Betreuer: Felix Freiling." Mannheim : Universitätsbibliothek Mannheim, 2012. http://d-nb.info/1034490672/34.

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Prasse, Paul [Verfasser], and Tobias [Akademischer Betreuer] Scheffer. "Pattern recognition for computer security : discriminative models for email spam campaign and malware detection / Paul Prasse ; Betreuer: Tobias Scheffer." Potsdam : Universität Potsdam, 2016. http://d-nb.info/1218793066/34.

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Milkovič, Marek. "Systém pro detekci vzorů v binárních souborech." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2017. http://www.nusl.cz/ntk/nusl-363783.

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Malicious software spreads really fast in the age of the Internet and it harms users and their data. Therefore, it is necessary to improve methods of how we deal with its analysis, so we can protect potential victims. This thesis deals with design and implementation of system for generating patterns out of executable files in cooperation with AVG Technologies. The goal of this work is to create a tool that generates a detection pattern from the set of binary files. This work further proposes new types of analyses for extraction of information out of executable files. Designed and implemented s
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Book chapters on the topic "Malware recognition"

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Kaur, Upinder, Xin Ma, Richard M. Voyles, and Byung-Cheol Min. "Malware Detection Using Pseudo Semi-Supervised Learning." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09282-4_31.

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Makandar, Aziz, and Anita Patrot. "Trojan Malware Image Pattern Classification." In Proceedings of International Conference on Cognition and Recognition. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5146-3_24.

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Sun, Li, Steven Versteeg, Serdar Boztaş, and Trevor Yann. "Pattern Recognition Techniques for the Classification of Malware Packers." In Information Security and Privacy. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14081-5_23.

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Gautam, Aparna, Rajesh Gopakumar, and G. Deepa. "Artificial Neural Network and Partial Pattern Recognition to Detect Malware." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3125-5_1.

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Białczak, Piotr, and Wojciech Mazurczyk. "Malware Classification Using Open Set Recognition and HTTP Protocol Requests." In Computer Security – ESORICS 2023. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51476-0_12.

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Moskalenko, Viacheslav. "Robust Model and Training Method for Malware Recognition in IoT Devices." In Advancements in Cybersecurity. CRC Press, 2025. https://doi.org/10.1201/9781003546153-13.

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Krupski, Jacek, Damian Rybicki, Waldemar Graniszewski, and Marcin Iwanowski. "Deep Learning vs. Traditional Approaches to Malware Traffic Classification – A Comparative Study." In Progress in Image Processing, Pattern Recognition and Communication Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81523-3_20.

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de los Santos, Sergio, Antonio Guzmán, and Carmen Torrano. "Android Malware Pattern Recognition for Fraud Detection and Attribution: A Case Study." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4614-7163-9_110173-1.

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de los Santos, Sergio, Antonio Guzmán, and Carmen Torrano. "Android Malware Pattern Recognition for Fraud Detection and Attribution: A Case Study." In Encyclopedia of Social Network Analysis and Mining. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7131-2_110173.

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Torky, Mohamed, Norhan Elnady, and Fahad Hanash Alzahrani. "Android Malware Recognition Using Machine Learning and Neural Networks Framework: A Practical Comparison Study." In Studies in Systems, Decision and Control. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84636-6_57.

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Conference papers on the topic "Malware recognition"

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Zhernova, Ksenia. "Visual Malware Recognition Using Artificial Neural Networks." In 2024 International Russian Automation Conference (RusAutoCon). IEEE, 2024. http://dx.doi.org/10.1109/rusautocon61949.2024.10693982.

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Devika, P., V. Surya Narayana Reddy, Dhulipalla Ramya Krishna, Chiranjeevi Rampilla, and B. Simha Charan Yadav. "Malware Classification Using Deep Learning." In 2024 2nd International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR). IEEE, 2024. https://doi.org/10.1109/icaitpr63242.2024.10960019.

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Zulmeika, Annisa Rifky, Marcellinus David Arel Bagjasantosa, Setia Juli Irzal Ismail, and Hendrawan. "Prevention Methods of Virtual Machine Environment Recognition by Malware." In 2024 18th International Conference on Telecommunication Systems, Services, and Applications (TSSA). IEEE, 2024. https://doi.org/10.1109/tssa63730.2024.10863873.

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Vaishnavi, V. Siri, Sravani Reddy, Garshith Reddydy, Ramachandro Majji, Anudeep Medha, and A. Raja Shekar. "Malware Detection Using Long Short Term Memory and Recurrent Neural Network." In 2024 2nd International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR). IEEE, 2024. https://doi.org/10.1109/icaitpr63242.2024.10960175.

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Bao, Pham Thai, Do Thi Thu Hien, Nguyen Tan Cam, and Van-Hau Pham. "A multimodal Windows malware detection method based on hybrid analysis and graph representations." In 2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2024. http://dx.doi.org/10.1109/mapr63514.2024.10660853.

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Mohan, Mili, and Sabitha S. "Stegomalware: A Comprehensive Survey on Creation and Detection of Malware in Images." In 2024 2nd International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR). IEEE, 2024. https://doi.org/10.1109/icaitpr63242.2024.10960081.

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Zhang, Yuxin, Shuilin Li, Gaolei Li, et al. "MKPL: Multi-dimensional Knowledge-embedded Prompt Learning for Few-shot Malware Family Recognition." In 2024 IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2024. https://doi.org/10.1109/hpcc64274.2024.00145.

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Vokorokos, Liberios, Jan Hurtuk, and Branislav Mados. "Malware categorization and recognition problem." In 2014 18th International Conference on Intelligent Engineering Systems (INES). IEEE, 2014. http://dx.doi.org/10.1109/ines.2014.6909350.

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Fedler, Rafael, Marcel Kulicke, and Julian Schutte. "An antivirus API for Android malware recognition." In 2013 8th International Conference on Malicious and Unwanted Software: "The Americas" (MALWARE). IEEE, 2013. http://dx.doi.org/10.1109/malware.2013.6703688.

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Stephen Fiske, Michael. "Visual Image Authentication." In Human Interaction and Emerging Technologies (IHIET-AI 2022) Artificial Intelligence and Future Applications. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100863.

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Malware plays a critical role in breaching computer systems. Cybersecurity research has primarily focused on malware detection. Detection methods are currently up against fundamental limits in theoretical computer science. The purpose of visual image authentication is to create Artificial Intelligence (AI) problems that are easy for humans to understand, yet also hinder malware from understanding or capturing a human user’s credentials or keys. Human visual intelligence still surpasses AI visual recognition systems, particularly when a problem is undefined, or visual occlusions or distracting
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