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Relevant bibliographies by topics / FILE-LESS MALWARE

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Dissertations / Theses
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Academic literature on the topic 'FILE-LESS MALWARE'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

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Journal articles on the topic "FILE-LESS MALWARE"

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Sanjana S N and Pavan A C. "Unveiling The Detection of File Less Malware from Dark Web: A Stealthy Arsenal for Web Application Exploitation." International Research Journal on Advanced Engineering and Management (IRJAEM) 2, no. 06 (2024): 2088–91. http://dx.doi.org/10.47392/irjaem.2024.0306.

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The proliferation of fileless malware poses a significant threat to web application security, especially when sourced from the dark web. This paper presents a novel detection tool designed to identify and mitigate fileless malware sourced from the dark web, specifically targeting web applications. Leveraging advanced anomaly detection and behavioral analysis techniques, the tool monitors real-time traffic and user interactions, enabling the early detection of malicious activities. Additionally, the tool integrates seamlessly with threat intelligence sources, enhancing its ability to recognize

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Madhavi Satish Avhankar. "A Comprehensive Survey on Polymorphic Malware Analysis: Challenges, Techniques, and Future Directions." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 2765–76. https://doi.org/10.52783/cana.v32.4554.

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Since the beginning of computing, malicious software has changed dramatically, becoming more complex and elusive. The increase in ransomware attacks has brought attention to the serious risks that malware poses, affecting not only individuals but also organizations, governments, and vital infrastructure like transportation networks and hospitals. Mitigating these dangers requires early identification of harmful behaviour, yet detecting new and unknown malware is still quite difficult. Static and dynamic analysis are the two main types of malware analysis approaches. Dynamic analysis watches ho

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Tariq, Anna, and Arshad Mehmood. "A Systematic Literature Review on AI-Based Methods for Malware Detection." Asian Bulletin of Big Data Management 5, no. 1.1 (2025): 1–23. https://doi.org/10.62019/m1m8ja69.

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Recent breakthroughs in artificial intelligence have improved malware detection, allowing systems to discover new threats by recognizing unexpected patterns that go beyond established signatures. AI-driven detection is critical in cybersecurity, and it covers malware detection, intrusion detection, and phishing prevention. This review investigates AI-based malware detection studies from (2015-2024) with a focus on machine learning and deep learning techniques. It emphasizes improvements in dealing with complex malware, including polymorphic and file less variants, while also highlighting obsta

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Et. al., Dr Ronak Panchal,. "A Review On Protection Against Fileless Malware Attacks Using Gateway." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 7302–7. http://dx.doi.org/10.17762/turcomat.v12i10.5620.

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File less attacks cause immense problems for the users of the computer system as these attacks cannot be easily detected by the system users and remain invisible while cursing tremendous amounts of damage to the system. These attacks can even lead to monetary losses of the users while making banking transactions and hence leads to the loss of personal data. Cyber security is being provided by providing protection in income traffic which is done by the scanning of the data that is being taken from the online resources. There are different types of malware present, and hence security level shoul

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Zhang, Yunchun, Jiaqi Jiang, Chao Yi, et al. "A Robust CNN for Malware Classification against Executable Adversarial Attack." Electronics 13, no. 5 (2024): 989. http://dx.doi.org/10.3390/electronics13050989.

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Deep-learning-based malware-detection models are threatened by adversarial attacks. This paper designs a robust and secure convolutional neural network (CNN) for malware classification. First, three CNNs with different pooling layers, including global average pooling (GAP), global max pooling (GMP), and spatial pyramid pooling (SPP), are proposed. Second, we designed an executable adversarial attack to construct adversarial malware by changing the meaningless and unimportant segments within the Portable Executable (PE) header file. Finally, to consolidate the GMP-based CNN, a header-aware loss

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Ilić, Slaviša, Milan Gnjatović, Brankica Popović, and Nemanja Maček. "A pilot comparative analysis of the Cuckoo and Drakvuf sandboxes: An end-user perspective." Vojnotehnicki glasnik 70, no. 2 (2022): 372–92. http://dx.doi.org/10.5937/vojtehg70-36196.

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Introduction/purpose: This paper reports on a pilot comparative analysis of the Cuckoo and Drakvuf sandboxes. These sandboxes are selected as the subjects of the analysis because of their popularity in the professional community and their complementary approaches to analyzing malware behavior. Methods: Both sandboxes were set up with basic configurations and confronted with the same set of malware samples. The evaluation was primarily conducted with respect to the question of to what extent a sandbox is helpful to the human analyst in malware analysis. Thus, only the information available in W

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Sivaraju, S. S. "An Insight into Deep Learning based Cryptojacking Detection Model." Journal of Trends in Computer Science and Smart Technology 4, no. 3 (2022): 175–84. http://dx.doi.org/10.36548/jtcsst.2022.3.006.

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To autonomously identify cyber threats is a non-trivial research topic. One area where this is most apparent is in the evolution of evasive cyber assaults, which are becoming better at masking their existence and obscuring their attack methods (for example, file-less malware). Particularly stealthy Advanced Persistent Threats may hide out in the system for a long time without being spotted. This study presents a novel method, dubbed CapJack, for identifying illicit bitcoin mining activity in a web browser by using cutting-edge CapsNet technology. Thus far, it is aware that deep learning framew

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Saputra, Heru, Deris Stiawan, and Hadipurnawan Satria. "Malware Detection in Portable Document Format (PDF) Files with Byte Frequency Distribution (BFD) and Support Vector Machine (SVM)." Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 9, no. 4 (2024): 1144–53. https://doi.org/10.26555/jiteki.v9i4.27559.

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Portable Document Format (PDF) files as well as files in several other formats such as (.docx, .hwp and .jpg) are often used to conduct cyber attacks. According to VirusTotal, PDF ranks fourth among document files that are frequently used to spread malware in 2020. Malware detection is challenging partly because of its ability to stay hidden and adapt its own code and thus requiring new smarter methods to detect. Therefore, outdated detection and classification methods become less effective. Nowadays, one of such methods that can be used to detect PDF files infected with malware is a machine l

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Yang, Pin, Huiyu Zhou, Yue Zhu, Liang Liu, and Lei Zhang. "Malware Classification Based on Shallow Neural Network." Future Internet 12, no. 12 (2020): 219. http://dx.doi.org/10.3390/fi12120219.

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The emergence of a large number of new malicious code poses a serious threat to network security, and most of them are derivative versions of existing malicious code. The classification of malicious code is helpful to analyze the evolutionary trend of malicious code families and trace the source of cybercrime. The existing methods of malware classification emphasize the depth of the neural network, which has the problems of a long training time and large computational cost. In this work, we propose the shallow neural network-based malware classifier (SNNMAC), a malware classification model bas

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Pawar, Harsh. "Windows Bypass and Multilayer Security." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30828.

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Firstly, the abstract delves into the diverse array of bypass techniques employed by adversaries, ranging from traditional methods such as password cracking and privilege escalation to more advanced tactics like file less malware and code injection. Understanding these tactics is crucial for defenders to anticipate and counteract potential threats effectively. Secondly, the abstract highlights the limitations of singular security measures and advocates for the adoption of multilayer security strategies. By implementing a combination of preventive, detective, and corrective controls, organizati

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Dissertations / Theses on the topic "FILE-LESS MALWARE"

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ANAND, HIMANSHU. "FILE-LESS MALWARE DETECTION." Thesis, 2022. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19105.

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Today, Everything is present digitally on our computer system and every organisation uses the computer for its daily work, Nearly 50 billion devices are currently connected to the Internet. Every device which is connected to the internet is vulnerable to cyberattack, to protect them from any attack multiple techniques are introduced like, Anomaly-based detection, Specification-based detection and Signature-based detection but with the evolution, in cybersecurity measures, the threat has also evolved with time, especially in the field of malware. Typically, malware is based on the file s

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Book chapters on the topic "FILE-LESS MALWARE"

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Diwaker, Ritesh, and Deepak Asrani. "Multimedia Security in Audio Signal." In Artificial intelligence and Multimedia Data Engineering. BENTHAM SCIENCE PUBLISHERS, 2023. http://dx.doi.org/10.2174/9789815196443123010008.

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The security of Digital media has been varying continuously due to advanced malware attacks. Multimedia security has become one of the major concerns since new technologies are introduced. The proposed paper applied the watermarking technique in digital audio signals in which unique data is inserted in one-dimensional data in such a way that it must not affect the major information of the audio signal. The hybrid decomposition scheme has been applied to the audio data in order to extract features in terms of energy bands. The data is kept hidden in a low significant energy band that contains l

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Conference papers on the topic "FILE-LESS MALWARE"

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Zakaria, Mohamed, Mohamed S. Mohamed, Sherif Hussein, and Gouda I. Salama. "Obfuscated File-less Malware Threats: Recent Analysis and Detection Approaches Based on Memory Forensics." In 2025 15th International Conference on Electrical Engineering (ICEENG). IEEE, 2025. https://doi.org/10.1109/iceeng64546.2025.11031292.

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Varghese, Reeya B., Sakthi Vinayak, A. Thirisha, and S. Sageengrana. "Event logs and memory dump analysis for file-less Malware detection using deep neural networks." In INTERNATIONAL CONFERENCE ON COGNITIVE COMPUTING AND ARTIFICIAL INTELLIGENCE (ICCCAI - 2024). AIP Publishing, 2025. https://doi.org/10.1063/5.0265161.

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