Готові списки джерел за темами / Fileless malware

Добірка наукової літератури з теми "Fileless malware"

Автор: Grafiati
Опубліковано: 19 червня 2021
Оновлено: 1 лютого 2022

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Зміст

  1. Статті в журналах
  2. Дисертації
  3. Частини книг
  4. Тези доповідей конференцій

Статті в журналах з теми "Fileless malware":

1

Mansfield-Devine, Steve. "Fileless attacks: compromising targets without malware." Network Security 2017, no. 4 (April 2017): 7–11. http://dx.doi.org/10.1016/s1353-4858(17)30037-5.

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2

Handaya, W. B. T., M. N. Yusoff, and A. Jantan. "Machine learning approach for detection of fileless cryptocurrency mining malware." Journal of Physics: Conference Series 1450 (February 2020): 012075. http://dx.doi.org/10.1088/1742-6596/1450/1/012075.

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3

Qiao, Yanchen, Bin Zhang, Weizhe Zhang, Arun Kumar Sangaiah, and Hualong Wu. "DGA Domain Name Classification Method Based on Long Short-Term Memory with Attention Mechanism." Applied Sciences 9, no. 20 (October 9, 2019): 4205. http://dx.doi.org/10.3390/app9204205.

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Анотація:
Currently, many cyberattacks use the Domain Generation Algorithm (DGA) to generate random domain names, so as to maintain communication with the Communication and Control (C&C) server. Discovering DGA domain names in advance could help to detect attacks and response in time. However, in recent years, the General Data Protection Regulation (GDPR) has been promulgated and implemented, and the method of DGA classification based on the context information, such as the WHOIS (the information about the registered users or assignees of the domain name) , is no longer applicable. At the same time, acquiring the DGA algorithm by reversing malware samples encounters the problem of no malware samples for various reasons, such as fileless malware. We propose a DGA domain name classification method based on Long Short-Term Memory (LSTM) with attention mechanism. This method is oriented to the character sequence of the domain name, and it uses the LSTM combined with attention mechanism to construct the DGA domain name classifier to achieve the rapid classification of domain names. The experimental results show that the method has a good classification result.
4

Choi, Sunoh. "Malicious PowerShell Detection Using Attention against Adversarial Attacks." Electronics 9, no. 11 (November 2, 2020): 1817. http://dx.doi.org/10.3390/electronics9111817.

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Анотація:
Currently, hundreds of thousands of new malicious files are created daily. Existing pattern-based antivirus solutions face difficulties in detecting such files. In addition, malicious PowerShell files are currently being used for fileless attacks. To prevent these problems, artificial intelligence-based detection methods have been suggested. However, methods that use a generative adversarial network (GAN) to avoid AI-based detection have been proposed recently. Attacks that use such methods are called adversarial attacks. In this study, we propose an attention-based filtering method to prevent adversarial attacks. Using the attention-based filtering method, we can obtain restored PowerShell data from fake PowerShell data generated by GAN. First, we show that the detection rate of the fake PowerShell data generated by GAN in an existing malware detector is 0%. Subsequently, we show that the detection rate of the restored PowerShell data generated by attention-based filtering is 96.5%.
5

Vala Khushali. "A Review on Fileless Malware Analysis Techniques." International Journal of Engineering Research and V9, no. 05 (May 9, 2020). http://dx.doi.org/10.17577/ijertv9is050068.

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6

Sudhakar and Sushil Kumar. "An emerging threat Fileless malware: a survey and research challenges." Cybersecurity 3, no. 1 (January 14, 2020). http://dx.doi.org/10.1186/s42400-019-0043-x.

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7

Menendez, Hector David. "Malware: The Never-Ending Arm Race." Open Journal of Cybersecurity, September 7, 2021, 1–25. http://dx.doi.org/10.46723/ojc.1.1.3.

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Анотація:
"Antivirus is death"' and probably every detection system that focuses on a single strategy for indicators of compromise. This famous quote that Brian Dye --Symantec's senior vice president-- stated in 2014 is the best representation of the current situation with malware detection and mitigation. Concealment strategies evolved significantly during the last years, not just like the classical ones based on polimorphic and metamorphic methodologies, which killed the signature-based detection that antiviruses use, but also the capabilities to fileless malware, i.e. malware only resident in volatile memory that makes every disk analysis senseless. This review provides a historical background of different concealment strategies introduced to protect malicious --and not necessarily malicious-- software from different detection or analysis techniques. It will cover binary, static and dynamic analysis, and also new strategies based on machine learning from both perspectives, the attackers and the defenders.

Дисертації з теми "Fileless malware":

1

Lingaas, Türk Jakob. "Living off the Land Binaries with Virtual Machines." Thesis, Högskolan i Halmstad, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44842.

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Анотація:
As the threat of ransomware increases, the ever-growing demand for more efficient cybersecurityimplementations invite cybercriminals to find new methods of bypassing these counter measures.One method for bypassing potential antivirus software is to use the binaries already present on thevictim device, causing them damage by using trusted binaries which does not trigger windowsdefender (or similar antivirus measures).This thesis attempts to use virtual machines as a living of the land binary. By utilizing the virtualenvironment of Windows iso images within a hypervisor, the attacker can download and execute abinary without being stopped by the bare metal host’s IDS or IPS. As the attacker controls the virtualenvironment, they can disable Windows Defender within the virtual machine and acquire theransomware without the upper layer of IDS or IPS even noticing, meaning they also remain stealthyfor a persistent engagement. The attacker would then proceed to use the share folder functionalityof the hypervisor and target a directory with sensitive files, before executive the binary within thevirtual machine. To the bare metal host, it would look like a hypervisor process is affecting the fileswithin the shared folder, which does not raise any alarms. However, what is actually happening is theransomware of the attacker’s choice has encrypted the files of the target directory (or mounteddrive, depending on method used), and can now continue to the next directory (or drive).The results of this work showed that virtual machines can be used for living off the land binariesattacks by utilizing either the shared folder functionality of a specific hypervisor, or by mounting adrive to a virtual machine. The experiments were proven to work within their own parameters,assuming certain requirements are fulfilled for the attack to be doable. Defenders can tweak IDS andIPS policies to limit or warn when a user access or changes partitions or limiting the accessibility forthe hypervisors native to the machine.

Частини книг з теми "Fileless malware":

1

Mohanta, Abhijit, and Anoop Saldanha. "Fileless, Macros, and Other Malware Trends." In Malware Analysis and Detection Engineering, 721–67. Berkeley, CA: Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6193-4_20.

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2

Tarek, Radah, Saadi Chaimae, and Chaoui Habiba. "Runtime API Signature for Fileless Malware Detection." In Advances in Intelligent Systems and Computing, 645–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39445-5_47.

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3

Saad, Sherif, Farhan Mahmood, William Briguglio, and Haytham Elmiligi. "JSLess: A Tale of a Fileless Javascript Memory-Resident Malware." In Information Security Practice and Experience, 113–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34339-2_7.

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Тези доповідей конференцій з теми "Fileless malware":

1

Afreen, Asad, Moosa Aslam, and Saad Ahmed. "Analysis of Fileless Malware and its Evasive Behavior." In 2020 International Conference on Cyber Warfare and Security (ICCWS). IEEE, 2020. http://dx.doi.org/10.1109/iccws48432.2020.9292376.

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2

Botacin, Marcus, André Grégio, and Marco Antonio Zanata Alves. "Near-Memory & In-Memory Detection of Fileless Malware." In MEMSYS 2020: The International Symposium on Memory Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3422575.3422775.

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3

Sanjay, B. N., D. C. Rakshith, R. B. Akash, and Dr Vinay V. Hegde. "An Approach to Detect Fileless Malware and Defend its Evasive mechanisms." In 2018 3rd International Conference on Computational Systems and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2018. http://dx.doi.org/10.1109/csitss.2018.8768769.

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