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Journal articles on the topic 'Malware fingerprint'

Author: Grafiati

Published: 28 June 2021

Last updated: 29 July 2025

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1

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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2

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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3

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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5

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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6

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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8

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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9

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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10

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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11

Srijita Bhattacharjee. "Enhancing System Security Through Signature-Based Ransomware Detection and Automated Data Backup: A Comprehensive Approach to Mitigating Ransomware Attacks." Advances in Nonlinear Variational Inequalities 28, no. 2 (2024): 136–52. http://dx.doi.org/10.52783/anvi.v28.1856.

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Attacks utilizing ransomware have developed into a major hacking hazard, causing enormous misfortunes in cash and information breaches in numerous areas. To bargain with this rising stress, we require a checking framework that works well and can be checked on. In this consider, we propose a way to discover ransomware that works with a programmed reinforcement framework to create it more secure from these sorts of dangers. Employing a huge library of known malware fingerprints, our strategy employments signature-based examination to discover and partitioned hurtful code. By comparing unused rec

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12

Sheikh, Abdul Manan, Md Rafiqul Islam, Mohamed Hadi Habaebi, Suriza Ahmad Zabidi, Athaur Rahman Bin Najeeb, and Adnan Kabbani. "Integrating Physical Unclonable Functions with Machine Learning for the Authentication of Edge Devices in IoT Networks." Future Internet 17, no. 7 (2025): 275. https://doi.org/10.3390/fi17070275.

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Edge computing (EC) faces unique security threats due to its distributed architecture, resource-constrained devices, and diverse applications, making it vulnerable to data breaches, malware infiltration, and device compromise. The mitigation strategies against EC data security threats include encryption, secure authentication, regular updates, tamper-resistant hardware, and lightweight security protocols. Physical Unclonable Functions (PUFs) are digital fingerprints for device authentication that enhance interconnected devices’ security due to their cryptographic characteristics. PUFs produce

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13

Sani, Ramadhan Rakhmat, Fauzi Adi Rafrastara, and Wildanil Ghozi. "Integrating Ensemble Learning and Information Gain for Malware Detection based on Static and Dynamic Features." Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, January 22, 2025. https://doi.org/10.22219/kinetik.v10i1.2051.

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The rapid advancement of malware poses a significant threat to devices, like personal computers and mobile phones. One of the most serious threats commonly faced is malicious software, including viruses, worms, trojan horses, and ransomware. Conventional antivirus software is becoming ineffective against the ever-evolving nature of malware, which can now take on various forms like polymorphic, metamorphic, and oligomorphic variants. These advanced malware types can not only replicate and distribute themselves, but also create unique fingerprints for each offspring. To address this challenge, a

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14

"Visual Detection for Android Malware using Deep Learning." Regular 10, no. 1 (2020): 152–56. http://dx.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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15

Esraa, Alshammari, H. Alhammami Alaa, and Hadi Ali. "Botnet Fingerprint Using Bro-IDS." October 1, 2020. https://doi.org/10.5281/zenodo.4130160.

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Abstract— Botnets or robot networks are one of the most serious and widespread attacks in the network’s era. This attack is used to enforce the control of the computers by injecting a small code into the computer to become one of the bots in the robot networks. Then the new bot will be linked with the supervisor or botmaster to get the instruction and commands that need to perform. Command and Control server (C&C) is the botmaster which sends the tasks to their connected bots. The motivations of using and launching such attacks are diverse from DDoS to spam, attacking IRC chat,

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16

Nappa, Antonio, Aaron Úbeda-Portugués, Panagiotis Papadopoulos, Matteo Varvello, Juan Tapiador, and Andrea Lanzi. "Scramblesuit: An effective timing side-channels framework for malware sandbox evasion." Journal of Computer Security, August 18, 2022, 1–26. http://dx.doi.org/10.3233/jcs-220005.

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Online malware scanners are one of the best weapons in the arsenal of cybersecurity companies and researchers. A fundamental part of such systems is the sandbox that provides an instrumented and isolated environment (virtualized or emulated) for any user to upload and run unknown artifacts and identify potentially malicious behaviors. The provided API and the wealth of information in the reports produced by these services have also helped attackers test the efficacy of numerous techniques to make malware hard to detect. The most common technique used by malware for evading the analysis system

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17

Kumar, Saurabh, Debadatta Mishra, Biswabandan Panda, and Sandeep Kumar Shukla. "InviSeal: A Stealthy Dynamic Analysis Framework for Android Systems." Digital Threats: Research and Practice, October 12, 2022. http://dx.doi.org/10.1145/3567599.

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With wide adaptation of open-source Android into mobile devices by different device vendors, sophisticated malware are developed to exploit security vulnerabilities. As comprehensive security analysis on physical devices are impractical and costly, emulator driven security analysis has gained popularity in recent times. Existing dynamic analysis frameworks suffer from two major issues: (i) they do not provide foolproof anti-emulation-detection measures even for fingerprint-based attacks, and (ii) lack efficient cross-layer profiling capabilities. In this work, we present InviSeal, a comprehens

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18

Ullah, Farhan, Gautam Srivastava, and Shamsher Ullah. "A malware detection system using a hybrid approach of multi-heads attention-based control flow traces and image visualization." Journal of Cloud Computing 11, no. 1 (2022). http://dx.doi.org/10.1186/s13677-022-00349-8.

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AbstractAndroid is the most widely used mobile platform, making it a prime target for malicious attacks. Therefore, it is imperative to effectively circumvent these attacks. Recently, machine learning has been a promising solution for malware detection, which relies on distinguishing features. While machine learning-based malware scanners have a large number of features, adversaries can avoid detection by using feature-related expertise. Therefore, one of the main tasks of the Android security industry is to consistently propose cutting-edge features that can detect suspicious activity. This s

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19

Srijita Bhattacharjee. "Identifying Ransomware Behaviour for Early Detection and Prevention: A Pre-Encryption Analysis Approach to Halt Cyber Invasions." Computer Fraud and Security, November 14, 2024, 59–66. https://doi.org/10.52710/cfs.35.

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Ransomware might be a kind of extortion in which digital documents are rendered inaccessible until a ransom is paid. Protecting against the growing number of ransomware attacks is seen as a difficult undertaking due to the necessity for knowledge on newly discovered malware and constantly developing families or variants. As a result, there is a need to investigate convincing techniques to detecting and reducing ransomware assaults by analysing their behavioural patterns prior to encryption. Using the Pre-attack API calls, these ransomwares may be assigned to recognised malware families. Discov

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20

Ikwu, Ruth, Luca Giommoni, Amir Javed, Pete Burnap, and Matthew Williams. "Digital fingerprinting for identifying malicious collusive groups on Twitter." Journal of Cybersecurity 9, no. 1 (2023). http://dx.doi.org/10.1093/cybsec/tyad014.

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Abstract Propagation of malicious code on online social networks (OSNs) is often a coordinated effort by collusive groups of malicious actors hiding behind multiple online identities (or digital personas). Increased interaction in OSN has made them reliable for the efficient orchestration of cyberattacks such as phishing click bait and drive-by downloads. URL shortening enables obfuscation of such links to malicious websites and massive interaction with such embedded malicious links in OSN guarantees maximum reach. These malicious links lure users to malicious endpoints where attackers can exp

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21

Vasilellis, Efstratios, Thanos Katsiolis, Dimitris Gritzalis, George Stergiopoulos, and Christina Sotiriou. "The sound of malware: an audio fingerprinting malware detection method." International Journal of Information Security 24, no. 3 (2025). https://doi.org/10.1007/s10207-025-01073-5.

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Abstract The increasing complexity of Android malware has increased the need for efficient detection methods. Researchers have introduced new frameworks for analyzing Android malware in response to the growing threat of malicious applications. Traditional static analysis methods, which are widely used, are susceptible to obfuscation and can be bypassed easily. However, although dynamic analysis is more resilient, it is computationally intensive and costly to implement. In this paper, we introduce MalWave, a novel approach that uses audio signal processing to detect Android malware by convertin

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22

Huber, Robert, Daniel Belles, Tim Bücher, Leonard Franke, Hussam Amrouch, and Uli Lemmer. "Integrated CPU Monitoring Using 2D Temperature Sensor Arrays Directly Printed on Heat Sinks." Advanced Materials Technologies, March 8, 2024. http://dx.doi.org/10.1002/admt.202301631.

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AbstractIn today's digital world, the demand for computer security and system reliability is a crucial element. Monitoring the CPU temperature during operation provides valuable insights but is currently limited to the embedded on‐chip sensors. The implementation of an extra security layer based on temperature monitoring can detect anomalies in an early stage, identify malware, and help mitigate attacks. The approach of integrating more on‐chip temperature sensors into the silicon is avoided due to space, power limitations, and cost constraints. However, the field of printed electronics and se

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23

Celdrán, Alberto Huertas, Pedro Miguel Sánchez Sánchez, Miguel Azorín Castillo, Gérôme Bovet, Gregorio Martínez Pérez, and Burkhard Stiller. "Intelligent and behavioral-based detection of malware in IoT spectrum sensors." International Journal of Information Security, July 29, 2022. http://dx.doi.org/10.1007/s10207-022-00602-w.

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AbstractThe number of Cyber-Physical Systems (CPS) available in industrial environments is growing mainly due to the evolution of the Internet-of-Things (IoT) paradigm. In such a context, radio frequency spectrum sensing in industrial scenarios is one of the most interesting applications of CPS due to the scarcity of the spectrum. Despite the benefits of operational platforms, IoT spectrum sensors are vulnerable to heterogeneous malware. The usage of behavioral fingerprinting and machine learning has shown merit in detecting cyberattacks. Still, there exist challenges in terms of (i) designing

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24

M.SUJITHRA1, AND DR G. PADMAVATHI. "BIOMETRIC SYSTEM PENETRATION IN RESOURCE CONSTRAINED MOBILE DEVICE." March 7, 2013. https://doi.org/10.5121/ijbb.2013.3104.

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International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 DOI : 10.5121/ijbb.2013.3104 35 BIOMETRIC SYSTEM PENETRATION IN RESOURCE CONSTRAINED MOBILE DEVICE M.SUJITHRA1 AND DR G. PADMAVATHI 2 1Assistant Professor, Department of Computer Technology & Applications, Coimbatore Institute of Technology, Email: sujisrinithi@gmail.com 2 Professor& Head, Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, Coimbatore, Tamil Nadu, India. ABSTRACT Over the past few years, the usage of mobile devices to access dat

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