Relevant bibliographies by topics / Malware

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Malware'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 January 2025

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Journal articles on the topic "Malware"

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Sehrawat, Sahil, and Dr Dinesh Singh. "Malware and Malware Detection Techniques: A Survey." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3947–53. http://dx.doi.org/10.22214/ijraset.2022.43287.

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Abstract: Malicious software is a kind of software or codes which took some: private data, information from the PC framework, its tasks is to do only malicious objectives to the PC framework, without authorization of the PC clients. The effect of malicious software are worsen to the client. Malicious software i.e malwares are programs that are made to mischief, hinder or harm PCs, organizations and different assets related with it. Malwares are moved in PCs without the information on its proprietor. Presently malicious program is a serious threat. It is created to harm the PC framework and some of them are spread over the associated framework in the organization or web association. Analysts are making great efforts in malware framework field with compelling malware detection techniques to safeguard PC framework. Two essential methodologies have been proposed for it for example signature-based and heuristic-based detection. These methodologies distinguish known malware precisely yet can't distinguish the new, obscure malware. Recently various analysts have proposed malware identification framework utilizing data mining and machine learning strategies to distinguish between obscure and non – obscure malwares. In this paper, an detailed examination has been led on the present status of malware infection and work done for finding it. Keywords: PC framework, malicious software, heuristic-based , signature – based , zero -day malware , obscure malware
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Krishna, T. Shiva Rama. "Malware Detection using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1847–53. http://dx.doi.org/10.22214/ijraset.2021.35426.

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Malicious software or malware continues to pose a major security concern in this digital age as computer users, corporations, and governments witness an exponential growth in malware attacks. Current malware detection solutions adopt Static and Dynamic analysis of malware signatures and behaviour patterns that are time consuming and ineffective in identifying unknown malwares. Recent malwares use polymorphic, metamorphic and other evasive techniques to change the malware behaviour’s quickly and to generate large number of malwares. Since new malwares are predominantly variants of existing malwares, machine learning algorithms are being employed recently to conduct an effective malware analysis. This requires extensive feature engineering, feature learning and feature representation. By using the advanced MLAs such as deep learning, the feature engineering phase can be completely avoided. Though some recent research studies exist in this direction, the performance of the algorithms is biased with the training data. There is a need to mitigate bias and evaluate these methods independently in order to arrive at new enhanced methods for effective zero-day malware detection. To fill the gap in literature, this work evaluates classical MLAs and deep learning architectures for malware detection, classification and categorization with both public and private datasets. The train and test splits of public and private datasets used in the experimental analysis are disjoint to each other’s and collected in different timescales. In addition, we propose a novel image processing technique with optimal parameters for MLAs and deep learning architectures. A comprehensive experimental evaluation of these methods indicate that deep learning architectures outperform classical MLAs. Overall, this work proposes an effective visual detection of malware using a scalable and hybrid deep learning framework for real-time deployments. The visualization and deep learning architectures for static, dynamic and image processing-based hybrid approach in a big data environment is a new enhanced method for effective zero-day malware detection.
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Joshi, Sakshi, and Santosh Mahagaonkar. "MALWARE DETECTION USING MACHINE LEARNING TECHNIQUES." International Journal of Engineering Applied Sciences and Technology 7, no. 5 (September 1, 2022): 86–91. http://dx.doi.org/10.33564/ijeast.2022.v07i05.014.

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Malware attacks have become serious and crucial issue now a days, as it can affect victim in many ways. Hence detecting malware at early stage is an essential aspect in the security of computer systems. Existing malware system contains a traditional antivirus detection method that depends on signature-based and behavioral methods. Traditional methods of malware detection are not that effective and cannot detect unknown malwares. In recent years machine learning is coming out as an emerging and challenging field in malware detection. Proposed method implements machine learning and deep learning technique for detecting malware. This is achieved using machine learning algorithm, Support Vector Machine and deep learning concept using Convolutional Neural Networks where in malwares are represented as images. The study compares the performance of conventional, machine learning-based, and deep learningbased malware detection techniques. Proposed method implemented for malware detection using Convolutional Neural Networks with malware images is more secure compare to dynamic based method as binary malware files are converted to images and images are never executed also it can reduce drawbacks of traditional signature based method at some extent.
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Marcello, Mario. "Collecting Malware in Swiss German University with Low Energy and Cost Computer." Journal of Applied Information, Communication and Technology 5, no. 2 (October 28, 2018): 85–91. http://dx.doi.org/10.33555/ejaict.v5i2.57.

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The malware spreads massively in Indonesia. The security in Information Technology doesn’t seem to become a top priority for Indonesian. The use of pirated software is still high, although it is the biggest threat and entrance for the malwares to attacks. This paper shows how to collect a spreading malware in a system to know the malware trends that exist. So, the owner may know the malware trends inside his system and he can countermeasure the attacks. To collect the malwares, I use the Dionaea, the honeypot to collect malware and implement it to Raspberry Pi. Raspberry Pi is a small, low cost and low energy consumption computer. By using Raspberry Pi to collect malware, we can minimize budget, save the energy and space.
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Wang, Shuo, Jian Wang, Yafei Song, and Song Li. "Malicious Code Variant Identification Based on Multiscale Feature Fusion CNNs." Computational Intelligence and Neuroscience 2021 (December 14, 2021): 1–10. http://dx.doi.org/10.1155/2021/1070586.

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The increasing volume and types of malwares bring a great threat to network security. The malware binary detection with deep convolutional neural networks (CNNs) has been proved to be an effective method. However, the existing malware classification methods based on CNNs are unsatisfactory to this day because of their poor extraction ability, insufficient accuracy of malware classification, and high cost of detection time. To solve these problems, a novel approach, namely, multiscale feature fusion convolutional neural networks (MFFCs), was proposed to achieve an effective classification of malware based on malware visualization utilizing deep learning, which can defend against malware variants and confusing malwares. The approach firstly converts malware code binaries into grayscale images, and then, these images will be normalized in size by utilizing the MFFC model to identify malware families. Comparative experiments were carried out to verify the performance of the proposed method. The results indicate that the MFFC stands out among the recent advanced methods with an accuracy of 98.72% and an average cost of 5.34 milliseconds on the Malimg dataset. Our method can effectively identify malware and detect variants of malware families, which has excellent feature extraction capability and higher accuracy with lower detection time.
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Bavishi, Ujaliben Kalpesh, and Bhavesh Madanlal Jain. "Malware Analysis." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 12 (January 3, 2018): 27. http://dx.doi.org/10.23956/ijarcsse.v7i12.507.

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Malware, also known as malicious software affects the user’s computer system or mobile devices by exploiting the system’s vulnerabilities. It is a major threat to the security of the computer systems. Some of the types of malwares that are most commonly used are viruses, trojans, worms, etc. Nowadays, there is a widespread use of malware which allows malware author to get sensitive information like bank details, contact information which is a serious threat in the world. Most of the malwares are spread through internet because of its frequent use which can destroy large systems piercing through network. Hence, in this paper, we focus on analyzing malware using different tools which can analyze the malware in a restricted environment. Since many malware authors uses self-modifying code and obfuscation, it is very difficult for the traditional antivirus software to detect the malware which identifies that it is under scan and it can change its execution sequence. So, in order to address the shortcomings of the traditional antivirus software, we will be discussing some of the analysis tools which runs analysis on the malware in an effective manner and helps us to analyze the malware which can help us to protect our system’s information.
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Ismail, Ismahani, Sulaiman Mohd Nor, and Muhammad Nadzir Marsono. "Stateless Malware Packet Detection by Incorporating Naive Bayes with Known Malware Signatures." Applied Computational Intelligence and Soft Computing 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/197961.

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Malware detection done at the network infrastructure level is still an open research problem ,considering the evolution of malwares and high detection accuracy needed to detect these threats. Content based classification techniques have been proven capable of detecting malware without matching for malware signatures. However, the performance of the classification techniques depends on observed training samples. In this paper, a new detection method that incorporates Snort malware signatures into Naive Bayes model training is proposed. Through experimental work, we prove that the proposed work results in low features search space for effective detection at the packet level. This paper also demonstrates the viability of detecting malware at the stateless level (using packets) as well as at the stateful level (using TCP byte stream). The result shows that it is feasible to detect malware at the stateless level with similar accuracy to the stateful level, thus requiring minimal resource for implementation on middleboxes. Stateless detection can give a better protection to end users by detecting malware on middleboxes without having to reconstruct stateful sessions and before malwares reach the end users.
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Zhang, Yu, and Feng Xia. "A Self-Relocation Based Method for Malware Detection." Applied Mechanics and Materials 220-223 (November 2012): 2688–93. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2688.

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Malware (malicious software) is software designed to disrupt computer operation, gather sensitive information, or gain unauthorized access to a computer system. Most malwares propagate themselves throughout the Internet by self-relocation. Self-relocation is a built-in module in most malwares that gets the base address of the code to correctly infect the other programs. Since most legitimate computer programs do not need the self-relocate module, the detection of malware with self-relocation module can be viewed as a promising approach for malware detection. This paper presents a self-relocation based method for both known and previously unknown malwares. The experiments indicate that the proposed approach has better ability to detect known and unknown malwares than other methods.
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Srivastava, Prerna, and Mohan Raj. "Feature extraction for enhanced malware detection using genetic algorithm." International Journal of Engineering & Technology 7, no. 2.8 (March 19, 2018): 444. http://dx.doi.org/10.14419/ijet.v7i2.8.10479.

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The use of internet has affected almost every field today. With the increase in use of internet, the number of malwares affecting the systems has also increased to a great deal. A number of techniques have been developed by the researchers in order to detect these malwares. The Malware Detection consists of two parts, the analysis part and the detection part. Malwares analysis can be categorized into Static analysis, Dynamic analysis and Hybrid Analysis. The Detection techniques can broadly be classified into Signature based techniques and Behaviour based techniques. A brief introduction of Malware Detection techniques is addressed here. The process of Feature Extraction plays a very important role in determining the efficiency and accuracy of the Malware Detection process. It aims at determining the subset of features that helps better differentiate between the malicious and benign files. We aim to provide a Feature Extraction process based on Genetic process that can be used for Malware Detection.
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John Oluwafemi Ogun. "Advancements in automated malware analysis: evaluating the efficacy of open-source tools in detecting and mitigating emerging malware threats to US businesses." International Journal of Science and Research Archive 12, no. 2 (August 30, 2024): 1958–64. http://dx.doi.org/10.30574/ijsra.2024.12.2.1488.

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Malware, short for malicious software, represents a significant and evolving threat to computer systems, targeting individuals, corporations, and governments globally. This paper explores the multifaceted nature of malware, which includes viruses, worms, Trojans, and more, and delves into how they compromise systems by disrupting services, stealing sensitive data, and denying access. Modern malware is increasingly sophisticated, evading traditional detection methods and posing challenges to cybersecurity professionals. This review outlines key methodologies in malware analysis, including MARE (Malware Analysis Reverse Engineering) and SAMA (Systematic Approach to Malware Analysis), which offer systematic frameworks for understanding and mitigating malware threats. Additionally, the paper highlights the challenges of malware analysis, such as the complexity of advanced malware variants and the limitations of current detection techniques. By examining the types of malwares, from ransomware to keyloggers, and discussing the signs of an attack, the paper underscores the importance of ongoing research and the development of more robust analytical tools. The insights provided aim to enhance the preparedness of IT professionals in combating emerging threats, emphasizing the necessity of a comprehensive understanding of malware behavior for effective defense strategies.
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Dissertations / Theses on the topic "Malware"

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Alzarooni, K. M. A. "Malware variant detection." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1347243/.

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Malware programs (e.g., viruses, worms, Trojans, etc.) are a worldwide epidemic. Studies and statistics show that the impact of malware is getting worse. Malware detectors are the primary tools in the defence against malware. Most commercial anti-malware scanners maintain a database of malware patterns and heuristic signatures for detecting malicious programs within a computer system. Malware writers use semantic-preserving code transformation (obfuscation) techniques to produce new stealth variants of their malware programs. Malware variants are hard to detect with today's detection technologies as these tools rely mostly on syntactic properties and ignore the semantics of malicious executable programs. A robust malware detection technique is required to handle this emerging security threat. In this thesis, we propose a new methodology that overcomes the drawback of existing malware detection methods by analysing the semantics of known malicious code. The methodology consists of three major analysis techniques: the development of a semantic signature, slicing analysis and test data generation analysis. The core element in this approach is to specify an approximation for malware code semantics and to produce signatures for identifying, possibly obfuscated but semantically equivalent, variants of a sample of malware. A semantic signature consists of a program test input and semantic traces of a known malware code. The key challenge in developing our semantics-based approach to malware variant detection is to achieve a balance between improving the detection rate (i.e. matching semantic traces) and performance, with or without the e ects of obfuscation on malware variants. We develop slicing analysis to improve the construction of semantic signatures. We back our trace-slicing method with a theoretical result that shows the notion of correctness of the slicer. A proof-of-concept implementation of our malware detector demonstrates that the semantics-based analysis approach could improve current detection tools and make the task more di cult for malware authors. Another important part of this thesis is exploring program semantics for the selection of a suitable part of the semantic signature, for which we provide two new theoretical results. In particular, this dissertation includes a test data generation method that works for binary executables and the notion of correctness of the method.
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Král, Benjamin. "Forenzní analýza malware." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385910.

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This master's thesis describes methodologies used in malware forensic analysis including methods used in static and dynamic analysis. Based on those methods a tool intended to be used by Computer Security Incident Response Teams (CSIRT) is designed to allow fast analysis and decisions regarding malware samples in security incident investigations. The design of this tool is thorougly described in the work along with the tool's requirements on which the tool design is based on. Based on the design a ForensIRT tool is implemented and then used to analyze a malware sample Cridex to demonstrate its capabilities. Finally the analysis results are compared to those of other comparable available malware forensics tools.
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Park, Sean. "Neural malware detection." Thesis, Federation University Australia, 2019. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173759.

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At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.
Doctor of Philosophy
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Iqbal, Muhammad Shahid, and Muhammad Sohail. "Runtime Analysis of Malware." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2930.

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Context: Every day increasing number of malwares are spreading around the world and infecting not only end users but also large organizations. This results in massive security threat for private data and expensive computer resources. There is lot of research going on to cope up with this large amount of malicious software. Researchers and practitioners developed many new methods to deal with them. One of the most effective methods used to capture malicious software is dynamic malware analysis. Dynamic analysis methods used today are very time consuming and resource greedy. Normally it could take days or at least some hours to analyze a single instance of suspected software. This is not good enough especially if we look at amount of attacks occurring every day. Objective: To save time and expensive resources used to perform these analyses, AMA: an automated malware analysis system is developed to analyze large number of suspected software. Analysis of any software inside AMA, results in a detailed report of its behavior, which includes changes made to file system, registry, processes and network traffic consumed. Main focus of this study is to develop a model to automate the runtime analysis of software which provide detailed analysis report and evaluation of its effectiveness. Methods: A thorough background study is conducted to gain the knowledge about malicious software and their behavior. Further software analysis techniques are studied to come up with a model that will automate the runtime analysis of software. A prototype system is developed and quasi experiment performed on malicious and benign software to evaluate the accuracy of the newly developed system and generated reports are compared with Norman and Anubis. Results: Based on thorough background study an automated runtime analysis model is developed and quasi experiment performed using implemented prototype system on selected legitimate and benign software. The experiment results show AMA has captured more detailed software behavior then Norman and Anubis and it could be used to better classify software. Conclusions: We concluded that AMA could capture more detailed behavior of the software analyzed and it will give more accurate classification of the software. We also can see from experiment results that there is no concrete distinguishing factors between general behaviors of both types of software. However, by digging a bit deep into analysis report one could understand the intensions of the software. That means reports generated by AMA provide enough information about software behavior and can be used to draw correct conclusions.
+46 736 51 83 01
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Carlin, Domhnall. "Dynamic analyses of malware." Thesis, Queen's University Belfast, 2018. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.766287.

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This thesis examines machine learning techniques for detecting malware using dynamic runtime opcodes. Previous work in the field has faltered on inadequately sized and poorly sampled datasets. A novel run-trace dataset is presented, the largest in the literature to date. Using this dataset, malware detection using opcode analysis is shown to be not only feasible, but highly accurate at short run-lengths and without computationally-expensive sequencing analysis. Second, unsupervised learning is used to investigate the effects of anti-virus (AV) labels on detection rates. AV labels offer an English-language description of the malware type, whereas it is found that using an assembly language description is more beneficial in malware triaging. Third, the machine learning techniques are applied to ransomware run-traces, which has not been explored in the literature to date. This offers four further novel contributions: examination of dynamic API calls vs opcode traces in ransomware; run-lengths necessary to detect ransomware accurately; creation of a logical feature reduction algorithm to minimise computational expense in machine learning; the first model in the literature which can differentiate between benign encryption (zipping) and malicious encryption. Lastly, the computational costs of 23 machine learning algorithms are investigated with respect to the run trace dataset. In the literature, researchers discuss the explosion of malware, yet opcode analyses have used fixed-size datasets, with no deference to how this model will cope with retraining on escalating datasets. The cost of retraining and testing updatable and non-updatable classifiers, both parallelised and non-parallelised, is examined with simulated escalating datasets. Lastly, a model is proposed and examined to mitigate the disadvantages of the most successful classifiers for future work.
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Huang, Alex Yangyang. "Towards robust malware detection." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119758.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 45-48).
A central challenge of malware detection using machine learning methods is the presence of adversarial variants, small changes to detectable malware that allow it to evade a model (i.e. be classified as benign). We take inspiration from adversarial variant generation methods in the continuous-valued image domain to introduce methods for malware in the binary domain. We incorporate these methods in the training of hardened models towards the goal of robustness against adversarial variants. Additionally, we provide visualization tools for analysis of hardened models. Our tools illustrate the difference in loss behavior between models trained with different methods, the effect of adversarial learning on the loss landscape of a model, and the effect of adversarial learning on the decision map of a model. The adversarial learning framework and the visualization tools in combination allow for the creation and understanding of robust models.
by Alex Yangyang Huang.
M. Eng.
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O'Kane, P. C. "Detection of obfuscated malware." Thesis, Queen's University Belfast, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680235.

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A cyber war exists between anti-malware researchers and malware writers. At the heart of this war rages a weapons race that has existed for decades, originating the 19805, with the arrival of the first computer virus. Obfuscation is one of the latest strategies employed by malware writers to camouflage the tell-tale signs of malware and thereby undermine anti-malware software making malware analysis difficult for anti-malware researchers.The the motivation for this research is, therefore, to find a malware detection strategy that is immune to the obfuscation methods used by the malware writers. One approach is to use program run-time traces (dynamic analysis) to perform N~gram analysis. N-gram analysis is the investigation of a program structure using bytes, charactersor text strings. The research presented in this thesis uses dynamic analysis to investigate malwaredetection using a Support Vector Machine (SVM) approach based on N-gram analysis. The key challenges addressed in this research are: Configuration of a host environment that can trace both benign and malicious software programs; SVM configuration using cross~validation to provide a robust classifier; the challenge of feature selection and feature reduction is addressed by first applying a feature filter and then presenting the reduced feature set to the SVM for feature selection. Several filtering methods are investigated and the findings have identified a suitable filter based on Eigenvectors. The final challenge associated with dynamic analysis is the length of time a program has to be run to ensure a correct classification. This is addressed in this research by investigating 14 different program run-lengths The findings show that obfuscated (packed and polymorphic) malware can be detected using a Support Vector Machine classifier with features extracted from program run-length traces.
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Калайчев, Г. В. "Microsoft malware prediction competition." Thesis, ХНУРЕ, 2020. http://openarchive.nure.ua/handle/document/12127.

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Основна мета цієї роботи - показати способи підготовки обсягу даних, побудова класифікаційної моделі на величезному наборі даних та оцінка отриманої моделі на тестових даних. Початкова проблема, яка була вирішена в цій роботі, була взята з Microsoft Malware Prediction Competition з сайту Kaggle. Ця проблема відповідає меті, оскільки навчальний набір даних містить різні типи функцій для попередньої обробки та 9 мільйонів рядків.
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Khoda, Mahbub. "Robust Mobile Malware Detection." Thesis, Federation University Australi, 2020. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/176412.

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The increasing popularity and use of smartphones and hand-held devices have made them the most popular target for malware attackers. Researchers have proposed machine learning-based models to automatically detect malware attacks on these devices. Since these models learn application behaviors solely from the extracted features, choosing an appropriate and meaningful feature set is one of the most crucial steps for designing an effective mobile malware detection system. There are four categories of features for mobile applications. Previous works have taken arbitrary combinations of these categories to design models, resulting in sub-optimal performance. This thesis systematically investigates the individual impact of these feature categories on mobile malware detection systems. Feature categories that complement each other are investigated and categories that add redundancy to the feature space (thereby degrading the performance) are analyzed. In the process, the combination of feature categories that provides the best detection results is identified. Ensuring reliability and robustness of the above-mentioned malware detection systems is of utmost importance as newer techniques to break down such systems continue to surface. Adversarial attack is one such evasive attack that can bypass a detection system by carefully morphing a malicious sample even though the sample was originally correctly identified by the same system. Self-crafted adversarial samples can be used to retrain a model to defend against such attacks. However, randomly using too many such samples, as is currently done in the literature, can further degrade detection performance. This work proposed two intelligent approaches to retrain a classifier through the intelligent selection of adversarial samples. The first approach adopts a distance-based scheme where the samples are chosen based on their distance from malware and benign cluster centers while the second selects the samples based on a probability measure derived from a kernel-based learning method. The second method achieved a 6% improvement in terms of accuracy. To ensure practical deployment of malware detection systems, it is necessary to keep the real-world data characteristics in mind. For example, the benign applications deployed in the market greatly outnumber malware applications. However, most studies have assumed a balanced data distribution. Also, techniques to handle imbalanced data in other domains cannot be applied directly to mobile malware detection since they generate synthetic samples with broken functionality, making them invalid. In this regard, this thesis introduces a novel synthetic over-sampling technique that ensures valid sample generation. This technique is subsequently combined with a dynamic cost function in the learning scheme that automatically adjusts minority class weight during model training which counters the bias towards the majority class and stabilizes the model. This hybrid method provided a 9% improvement in terms of F1-score. Aiming to design a robust malware detection system, this thesis extensively studies machine learning-based mobile malware detection in terms of best feature category combination, resilience against evasive attacks, and practical deployment of detection models. Given the increasing technological advancements in mobile and hand-held devices, this study will be very useful for designing robust cybersecurity systems to ensure safe usage of these devices.
Doctor of Philosophy
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Cortellazzi, Jacopo. "Code transplantation for adversarial malware." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/17288/.

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In the nefarious fight against attackers, a wide range of smart algorithms have been introduced, in order to block and even prevent new families of malware before their appearance. Machine learning, for instance, recently gained a lot of attention thanks to its ability to use generalization to possibly detect never-before-seen attacks or variants of a known one. During the past years, a lot of works have tested the strength of machine learning in the cybersecurity field, exploring its potentialities and weaknesses. In particular, various studies highlighted its robustness against adversarial attacks, proposing strategies to mitigate them . Unfortunately, all these findings have focused in testing their own discoveries just operating on the dataset at feature layer space, which is the virtual data representation space, without testing the current feasibility of the attack at the problem space level, modifying the current adversarial sample . For this reason, in this dissertation, we will introduce PRISM, a framework for executing an adversarial attack operating at the problem space level. Even if this framework focuses only on Android applications, the whole methodology can be generalized on other platforms, like Windows, Mac or Linux executable files. The main idea is to successfully evade a classifier by transplanting chunks of code, taken from a set of goodware to a given malware. Exactly as in medicine, we have a donor who donates organs and receivers who receive them, in this case, goodware applications are our donors, the organs are the needed code and the receiver is the targeted malware. In the following work we will discuss about concepts related to a wide variety of topics, ranging from machine learning, due to the target classifier, to static analysis, due to the possible countermeasures considered, to program analysis, due to the extraction techniques adopter, ending in mobile application, because the target operating system is Android.
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Books on the topic "Malware"

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Gritzalis, Dimitris, Kim-Kwang Raymond Choo, and Constantinos Patsakis, eds. Malware. Cham: Springer Nature Switzerland, 2025. http://dx.doi.org/10.1007/978-3-031-66245-4.

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Krieg, Christian, Adrian Dabrowski, Heidelinde Hobel, Katharina Krombholz, and Edgar Weippl. Hardware Malware. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-031-02338-5.

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Christodorescu, Mihai, Somesh Jha, Douglas Maughan, Dawn Song, and Cliff Wang, eds. Malware Detection. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-44599-1.

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Jiang, Xuxian, and Yajin Zhou. Android Malware. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7394-7.

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Jiang, Xuxian. Android Malware. New York, NY: Springer New York, 2013.

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Almomani, Iman, Leandros A. Maglaras, Mohamed Amine Ferrag, and Nick Ayres, eds. Cyber Malware. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-34969-0.

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Yin, Heng, and Dawn Song. Automatic Malware Analysis. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-5523-3.

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Davis, Michael A. Hacking exposed malware & rootkits: Malware & rootkits security secrets & solutions. New York: McGraw Hill, 2010.

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Sean, Bodmer, and LeMasters Aaron, eds. Hacking exposed malware & rootkits: Malware & rootkits security secrets & solutions. New York: McGraw Hill, 2010.

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Shane, Hartman, Morales Jose Andre, Quintans Manu, and Strazzere Tim, eds. Android malware and analysis. Boca Raton: CRC Press, Taylor & Francis Group, 2014.

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Book chapters on the topic "Malware"

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Aebi, Daniel. "Malware." In Praxishandbuch Sicherer IT-Betrieb, 109–42. Wiesbaden: Gabler Verlag, 2004. http://dx.doi.org/10.1007/978-3-322-90469-0_6.

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Prasad, Ramjee, and Vandana Rohokale. "Malware." In Springer Series in Wireless Technology, 67–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31703-4_5.

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Kappes, Martin. "Malware." In Netzwerk- und Datensicherheit, 95–105. Wiesbaden: Springer Fachmedien Wiesbaden, 2013. http://dx.doi.org/10.1007/978-3-8348-8612-5_6.

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Rawal, Bharat S., Gunasekaran Manogaran, and Alexender Peter. "Malware." In Cybersecurity and Identity Access Management, 103–16. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2658-7_6.

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Huber, Edith. "Malware." In Cybercrime, 75–98. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-26150-4_6.

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Kappes, Martin. "Malware." In Netzwerk- und Datensicherheit, 115–26. Wiesbaden: Springer Fachmedien Wiesbaden, 2022. http://dx.doi.org/10.1007/978-3-658-16127-9_6.

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Jiang, Xuxian, and Yajin Zhou. "Introduction." In Android Malware, 1–2. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7394-7_1.

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Jiang, Xuxian, and Yajin Zhou. "A Survey of Android Malware." In Android Malware, 3–20. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7394-7_2.

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Jiang, Xuxian, and Yajin Zhou. "Case Studies." In Android Malware, 21–29. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7394-7_3.

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Jiang, Xuxian, and Yajin Zhou. "Discussion." In Android Malware, 31–32. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-7394-7_4.

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Conference papers on the topic "Malware"

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Liu, Yanni, Ayong Ye, Yuanhuang Liu, and Wenting Lu. "Few-Shot Malware Classification using Malware Variant and Model Augmentation." In 2024 International Conference on Networking and Network Applications (NaNA), 399–404. IEEE, 2024. http://dx.doi.org/10.1109/nana63151.2024.00073.

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Chen, Shang-Wen, Tzu-Hsien Chuang, Chin-Wei Tien, and Chih-Wei Chen. "An Experience in Enhancing Machine Learning Classifier Against Low-Entropy Packed Malwares." In 8th International Conference on Computer Science and Information Technology (CoSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110406.

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Both benign applications and malwares would take packing for their different purposes to conceal the real part of the program processes. According to recent research reports, existing machine learning (ML) approach-based malware detection engines are difficult to effectively classify the packed malwares, especially when they are in low entropy packed. Recently, we counted and found that the ratio of low-entropy packed ransomware is extremely high. This would cause a high error rate of the result on currently used ML approaches. Thus, we propose a new method to extract entropy-related features and use a stack model to build up an ML malware engine to effectively detect low-entropy packed malwares. We evaluate our method by using over 15,000 malware samples collected from VirusTotal and compare the result to related researches. This experience reports our adopted model and features can significantly lower the error rate of low-entropy packed detection from 11% to 1%.
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Barros, Pedro H., Gabriel N. Gomes, Leonardo B. Barbosa, and Heitor S. Ramos. "Zero^2-SMELL: Uma nova abordagem de aprendizado zero-shot para detectar vulnerabilidades de dia zero." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbseg.2021.17309.

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Um dos problemas de segurança mais relevantes é inferir se um programa tem intenções maliciosas (software malware). Mesmo que o antivírus seja uma das abordagens mais populares para detecção de malware, novos tipos de malware são lançados em um ritmo acelerado, tornando a maioria das técnicas para detectá-los rapidamente obsoletas. Portanto, o antivírus normalmente falha em detectar novos malwares até que sua assinatura seja incorporada ao banco de dados. No entanto, novas técnicas para identificar malwares desconhecidos são necessárias para proteger os sistemas mesmo no dia zero do lançamento de um malware. O aprendizado few-shot é uma abordagem que consiste em usar alguns poucos exemplos de cada classe durante o treinamento de um modelo. Um caso interessante desse tipo de abordagem é a classificação de objetos que ainda não foram usados no conjunto de treinamento, ou seja, aprendizagem Zero-shot (ZSL). No presente trabalho, propomos um novo método de ZSL para classificar malware usando representação visual. Propomos um novo espaço de representação para calcular a similaridade entre pares de objetos, denominado espaço-S. Avaliamos nossa proposta em conjuntos de dados do mundo real compostos de exemplos de malware. Nossa proposta atingiu 81 na medida Recall@K e supera outros métodos em uma proporção de 14.81% em um modelo de classificação para vulnerabilidades de dia zero.
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Barros, Pedro H., Gabriel N. Gomes, Leonardo B. Barbosa, and Heitor S. Ramos. "Zero^2-SMELL: Uma nova abordagem de aprendizado zero-shot para detectar vulnerabilidades de dia zero." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbseg.2021.17309.

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Um dos problemas de segurança mais relevantes é inferir se um programa tem intenções maliciosas (software malware). Mesmo que o antivírus seja uma das abordagens mais populares para detecção de malware, novos tipos de malware são lançados em um ritmo acelerado, tornando a maioria das técnicas para detectá-los rapidamente obsoletas. Portanto, o antivírus normalmente falha em detectar novos malwares até que sua assinatura seja incorporada ao banco de dados. No entanto, novas técnicas para identificar malwares desconhecidos são necessárias para proteger os sistemas mesmo no dia zero do lançamento de um malware. O aprendizado few-shot é uma abordagem que consiste em usar alguns poucos exemplos de cada classe durante o treinamento de um modelo. Um caso interessante desse tipo de abordagem é a classificação de objetos que ainda não foram usados no conjunto de treinamento, ou seja, aprendizagem Zero-shot (ZSL). No presente trabalho, propomos um novo método de ZSL para classificar malware usando representação visual. Propomos um novo espaço de representação para calcular a similaridade entre pares de objetos, denominado espaço-S. Avaliamos nossa proposta em conjuntos de dados do mundo real compostos de exemplos de malware. Nossa proposta atingiu 81 na medida Recall@K e supera outros métodos em uma proporção de 14.81% em um modelo de classificação para vulnerabilidades de dia zero.
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Sonco, Leonardo Karling, Angelo Nogueira, Diego Kreutz, and Rodrigo Brandão Masilha. "Uma GUI para hackers do bem aprenderem sobre malwares sintéticos." In Escola Regional de Engenharia de Software, 109–17. Sociedade Brasileira de Computação, 2024. https://doi.org/10.5753/eres.2024.4293.

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Hackers do Bem precisam se manter atualizados para enfrentar as técnicas emergentes usadas por Hackers do Mal. Entre essas técnicas, está o uso de aprendizado profundo para criar malwares Android que imitam o comportamento de aplicativos legítimos, com o objetivo de enganar antivírus enquanto exploram novas vulnerabilidades. Nesse cenário, desenvolvemos o Malware DataLab, uma plataforma dedicada ao ensino de técnicas de aprendizado profundo, com o objetivo de ampliar datasets de malwares Android utilizando dados sintéticos. Este trabalho apresenta a interface gráfica do MalSynGen, a ferramenta de geração de dados tabulares sintéticos do Malware DataLab. Uma avaliação experimental preliminar demonstra o impacto positivo da proposta.
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Silva, Gabriel H. N. Espindola da, Gilberto Fernandes Junior, and Bruno Bogaz Zarpelão. "Impacto de ataques de evasão e eficácia da defesa baseada em treinamento adversário em detectores de malware." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais, 829–35. Sociedade Brasileira de Computação - SBC, 2024. http://dx.doi.org/10.5753/sbseg.2024.240800.

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Algoritmos de aprendizado de máquina (AM) podem ajudar na detecção de programas maliciosos, ou malwares, ao identificar padrões de comportamento deles. No entanto, os modelos de AM são vulneráveis a ataques de aprendizado de máquina adversário (AMA), permitindo que malwares sejam classificados como benignos. Este trabalho investiga o impacto dos ataques dFGSM (Deterministic Fast Gradient Sign Method), rFGSM (Randomic Fast Gradient Sign Method), BGA (Bit Gradient Ascent), BCA (Bit Coordinate Ascent) e Grosse contra detectores de malware e a eficácia da defesa baseada em treinamento adversário. Os resultados mostraram que ataques de alta intensidade (dFGSM, rFGSM, BGA) impactam significativamente a acurácia dos detectores, mesmo com treinamento adversário.
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Vaduva, Janalexandru, Vladraul Pasca, Iuliamaria Florea, and Razvan Rughinis. "APPLICATIONS OF MACHINE LEARNING IN MALWARE DETECTION." In eLSE 2019. Carol I National Defence University Publishing House, 2019. http://dx.doi.org/10.12753/2066-026x-19-110.

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In an ecosystem where education is done through software interaction, the security of those systems is one key aspect which should not trouble the educators nor the children and students that interact with them. The article addresses a problem that is growing every day, new malware samples, which steal data [10], encrypt data and ask for a ransom [11], get remote access to a personal computer [12], use computer resources to mine cryptocurrencies [13]. There are presented malware analysis reports that meticulously describe malicious software's behaviour and help security professionals to mitigate the risk. The need for security brings to light new methods to protect people's devices, like using machine learning or artificial intelligence. Random forest and neural network algorithms are implemented and the results are very encouraging, the accuracy in both cases is over 95%. In the last few years, because of the increasing computer resources and the more frequent usage of cloud services, these machine learning algorithms perform better and have caught the attention of many researchers in this area. The objectives of the project include the study of malicious software in order to find mitigation strategies, important characteristics of malware families that are used to generalize the problem of ransomware and malware detection. Also, it's presented the usage of open source sandbox to capture malware's behaviour and to use the results as the input for machine learning algorithms, and the study of different algorithms which can be used in classification problems. The current work can be a strong baseline to develop more advanced and useful algorithms, using millions of samples as the input dataset.
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Eren, Maksim E., Manish Bhattarai, Kim Rasmussen, Boian S. Alexandrov, and Charles Nicholas. "MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware." In 2023 IEEE International Conference on Intelligence and Security Informatics (ISI). IEEE, 2023. http://dx.doi.org/10.1109/isi58743.2023.10297217.

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Testoni, Gustavo A., Marcos Paulo L. A. Pereira, Sérgio S. Cardoso, Clayton E. das Chagas, and Ronaldo R. Goldschimidt. "EB-CyberDef: Um Ambiente Integrado de Defesa Cibernética para Apoio à Detecção e ao Combate de Comportamentos Maliciosos no Tráfego de Redes de Computadores." In Simpósio Brasileiro de Segurança da Informação e de Sistemas Computacionais. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sbseg_estendido.2022.224128.

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Diante do crescente volume de ataques maliciosos ocorridos na Internet, dois dos principais problemas enfrentados pela área de Segurança da Informação atualmente são a detecção e o combate de malwares. Embora diversas soluções estejam sendo desenvolvidas para lidar com esses problemas, a maioria delas trata de forma mutuamente exclusiva cada um desses dois problemas ou se concentra apenas no tratamento de um único tipo de malware. Neste contexto, o presente artigo tem por objetivo propor o EB-CyberDef, um ambiente integrado de defesa cibernética para apoiar a detecção e o combate de comportamentos maliciosos no tráfego de redes de computadores. O ambiente proposto e configurável e pode ser usado na identificação e na mitigação dos efeitos de diferentes tipos de malware. Resultados de experimentos preliminares sobre a capacidade do EB-CyberDef em detectar diferentes famílias de botnets ilustram o funcionamento e o potencial do prototipo em desenvolvimento.
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Mohd Shaid, Syed Zainudeen, and Mohd Aizaini Maarof. "Malware behavior image for malware variant identification." In 2014 International Symposium on Biometrics and Security Technologies (ISBAST). IEEE, 2014. http://dx.doi.org/10.1109/isbast.2014.7013128.

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Reports on the topic "Malware"

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Porras, Phillip, Hassen Saidi, and Vinod Yegneswaran. Malware Pandemics. Fort Belvoir, VA: Defense Technical Information Center, September 2010. http://dx.doi.org/10.21236/ada531166.

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Pearce, Lauren. Windows Internals and Malware Behavior: Malware Analysis Day 3. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1457289.

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Peppers, Joseph. Creating a Malware Analysis Lab and Basic Malware Analysis. Ames (Iowa): Iowa State University, January 2018. http://dx.doi.org/10.31274/cc-20240624-440.

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Pearce, Lauren. Covert Malware Launching and Data Encoding: Malware Analysis Day 5. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1457292.

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Pearce, Lauren. Research Topics in Malware. Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1329850.

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Pearce, Lauren. 2018 CyberFire East: Malware Analysis. Office of Scientific and Technical Information (OSTI), October 2018. http://dx.doi.org/10.2172/1475328.

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Pearce, Lauren. Incident Response and Malware Analysis. Office of Scientific and Technical Information (OSTI), November 2018. http://dx.doi.org/10.2172/1481123.

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Pearce, Lauren. Malware Analysis in a Nutshell. Office of Scientific and Technical Information (OSTI), October 2016. http://dx.doi.org/10.2172/1330062.

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Pearce, Lauren. Malware Analysis Report for SolarWinds.Orion.Core.BusinessLayer.dll. Office of Scientific and Technical Information (OSTI), August 2021. http://dx.doi.org/10.2172/1813804.

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Alexandrov, Boian, and Maksim Eren. Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking. Office of Scientific and Technical Information (OSTI), October 2021. http://dx.doi.org/10.2172/1826495.

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