Relevant bibliographies by topics / Malware fingerprint

Contents

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

Academic literature on the topic 'Malware fingerprint'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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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 (December 7, 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 allow to easily fingerprint these services as analysis environments; the lower the unlikeability of these features, the easier for us (and thus for malware) to fingerprint the analysis service they belong to. Finally, we propose a method for these analysis services to counter or at least mitigate our proposal.
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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 (December 8, 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 aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.
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Białczak, Piotr, and Wojciech Mazurczyk. "Hfinger: Malware HTTP Request Fingerprinting." Entropy 23, no. 5 (April 23, 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 of the request such as URI, protocol information, headers, and payload, providing a concise request representation that preserves the extracted information in a form interpretable by a human analyst. For the developed solution, we have performed an extensive experimental evaluation using real-world data sets and we also compared Hfinger with the most related and popular existing tools such as FATT, Mercury, and p0f. The conducted effectiveness analysis reveals that on average only 1.85% of requests fingerprinted by Hfinger collide between malware families, what is 8–34 times lower than existing tools. Moreover, unlike these tools, in default mode, Hfinger does not introduce collisions between malware and benign applications and achieves it by increasing the number of fingerprints by at most 3 times. As a result, Hfinger can effectively track and hunt malware by providing more unique fingerprints than other standard tools.
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"Visual Detection for Android Malware using Deep Learning." Regular 10, no. 1 (November 10, 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 network to identify Android application as benign or as malware. Finally, the proposed model succeeds to detect the entire 200 android applications (100 benign applications and 100 malware applications) with an accuracy of over 99% as shown in experimental results.
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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 programu Avast pre proces hlbšej detekcie škodlivého softvéru. Detekcia škodlivého správania sa začína analýzou a abstrakciou vzorcov z grafu správania. Izolované vzory môžu efektívnejšie identifikovať dynamicky sa meniaci malware. Grafy správania sú uložené v databáze grafov Neo4j a každý deň sú zachytené tisíce z nich. Cieľom tejto práce bolo navrhnúť algoritmus na identifikáciu správania škodlivého softvéru s dôrazom na rýchlosť skenovania a jasnosť identifikovaných vzorcov správania. Identifikácia škodlivého správania spočíva v nájdení najdôležitejších vlastností natrénovaných klasifikátorov a následnej extrakcie podgrafu pozostávajúceho iba z týchto dôležitých vlastností uzlov a vzťahov medzi nimi. Následne je navrhnuté pravidlo pre hodnotenie extrahovaného podgrafu. Diplomová práca prebehla v spolupráci so spoločnosťou Avast Software s.r.o.
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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, 589–99. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67180-2_57.

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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, 348–68. Berlin, Heidelberg: 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), 100–108. Philadelphia, PA: 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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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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