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Статті в журналах з теми "Classification des logiciels malveillants"
Carli, D., H. M. Seidling, C. Lovis, and P. Bonnabry. "Classification et recensement des aides à la décision implémentées dans les logiciels de prescription informatisée des établissements suisses." Le Pharmacien Hospitalier et Clinicien 47, no. 1 (March 2012): 59. http://dx.doi.org/10.1016/j.phclin.2011.12.249.
Повний текст джерелаABAKAR MOUSSA, Moustapha. "industrie 4.0 et la transformation numérique de la comptabilité." Journal of Academic Finance 12, no. 1 (June 23, 2021): 53–64. http://dx.doi.org/10.59051/joaf.v12i1.400.
Повний текст джерелаGirard, Chloé. "Pour une approche info-documentaire mixte numérique/non-numérique." Balisages, no. 1 (February 24, 2020). http://dx.doi.org/10.35562/balisages.292.
Повний текст джерелаДисертації з теми "Classification des logiciels malveillants"
Puodzius, Cassius. "Data-driven malware classification assisted by machine learning methods." Electronic Thesis or Diss., Rennes 1, 2022. https://ged.univ-rennes1.fr/nuxeo/site/esupversions/3dabb48c-b635-46a5-bcbe-23992a2512ec.
Повний текст джерелаHistorically, malware (MW) analysis has heavily resorted to human savvy for manual signature creation to detect and classify MW. This procedure is very costly and time consuming, thus unable to cope with modern cyber threat scenario. The solution is to widely automate MW analysis. Toward this goal, MW classification allows optimizing the handling of large MW corpora by identifying resemblances across similar instances. Consequently, MW classification figures as a key activity related to MW analysis, which is paramount in the operation of computer security as a whole. This thesis addresses the problem of MW classification taking an approach in which human intervention is spared as much as possible. Furthermore, we steer clear of subjectivity inherent to human analysis by designing MW classification solely on data directly extracted from MW analysis, thus taking a data-driven approach. Our objective is to improve the automation of malware analysis and to combine it with machine learning methods that are able to autonomously spot and reveal unwitting commonalities within data. We phased our work in three stages. Initially we focused on improving MW analysis and its automation, studying new ways of leveraging symbolic execution in MW analysis and developing a distributed framework to scale up our computational power. Then we concentrated on the representation of MW behavior, with painstaking attention to its accuracy and robustness. Finally, we fixed attention on MW clustering, devising a methodology that has no restriction in the combination of syntactical and behavioral features and remains scalable in practice. As for our main contributions, we revamp the use of symbolic execution for MW analysis with special attention to the optimal use of SMT solver tactics and hyperparameter settings; we conceive a new evaluation paradigm for MW analysis systems; we formulate a compact graph representation of behavior, along with a corresponding function for pairwise similarity computation, which is accurate and robust; and we elaborate a new MW clustering strategy based on ensemble clustering that is flexible with respect to the combination of syntactical and behavioral features
Calvet, Joan. "Analyse Dynamique de Logiciels Malveillants." Phd thesis, Université de Lorraine, 2013. http://tel.archives-ouvertes.fr/tel-00922384.
Повний текст джерелаThierry, Aurélien. "Désassemblage et détection de logiciels malveillants auto-modifiants." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0011/document.
Повний текст джерелаThis dissertation explores tactics for analysis and disassembly of malwares using some obfuscation techniques such as self-modification and code overlapping. Most malwares found in the wild use self-modification in order to hide their payload from an analyst. We propose an hybrid analysis which uses an execution trace derived from a dynamic analysis. This analysis cuts the self-modifying binary into several non self-modifying parts that we can examine through a static analysis using the trace as a guide. This second analysis circumvents more protection techniques such as code overlapping in order to recover the control flow graph of the studied binary. Moreover we review a morphological malware detector which compares the control flow graph of the studied binary against those of known malwares. We provide a formalization of this graph comparison problem along with efficient algorithms that solve it and a use case in the software similarity field
Pektaş, Abdurrahman. "Behavior based malware classification using online machine learning." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM065/document.
Повний текст джерелаRecently, malware, short for malicious software has greatly evolved and became a major threat to the home users, enterprises, and even to the governments. Despite the extensive use and availability of various anti-malware tools such as anti-viruses, intrusion detection systems, firewalls etc., malware authors can readily evade these precautions by using obfuscation techniques. To mitigate this problem, malware researchers have proposed various data mining and machine learning approaches for detecting and classifying malware samples according to the their static or dynamic feature set. Although the proposed methods are effective over small sample set, the scalability of these methods for large data-set are in question.Moreover, it is well-known fact that the majority of the malware is the variant of the previously known samples. Consequently, the volume of new variant created far outpaces the current capacity of malware analysis. Thus developing malware classification to cope with increasing number of malware is essential for security community. The key challenge in identifying the family of malware is to achieve a balance between increasing number of samples and classification accuracy. To overcome this limitation, unlike existing classification schemes which apply machine learning algorithm to stored data, i.e., they are off-line, we proposed a new malware classification system employing online machine learning algorithms that can provide instantaneous update about the new malware sample by following its introduction to the classification scheme.To achieve our goal, firstly we developed a portable, scalable and transparent malware analysis system called VirMon for dynamic analysis of malware targeting Windows OS. VirMon collects the behavioral activities of analyzed samples in low kernel level through its developed mini-filter driver. Secondly we set up a cluster of five machines for our online learning framework module (i.e. Jubatus), which allows to handle large scale of data. This configuration allows each analysis machine to perform its tasks and delivers the obtained results to the cluster manager.Essentially, the proposed framework consists of three major stages. The first stage consists in extracting the behavior of the sample file under scrutiny and observing its interactions with the OS resources. At this stage, the sample file is run in a sandboxed environment. Our framework supports two sandbox environments: VirMon and Cuckoo. During the second stage, we apply feature extraction to the analysis report. The label of each sample is determined by using Virustotal, an online multiple anti-virus scanner framework consisting of 46 engines. Then at the final stage, the malware dataset is partitioned into training and testing sets. The training set is used to obtain a classification model and the testing set is used for evaluation purposes .To validate the effectiveness and scalability of our method, we have evaluated our method on 18,000 recent malicious files including viruses, trojans, backdoors, worms, etc., obtained from VirusShare, and our experimental results show that our method performs malware classification with 92% of accuracy
Lemay, Frédérick. "Instrumentation optimisée de code pour prévenir l'exécution de code malicieux." Thesis, Université Laval, 2012. http://www.theses.ulaval.ca/2012/29030/29030.pdf.
Повний текст джерелаKhoury, Raphaël. "Détection du code malicieux : système de type à effets et instrumentation du code." Thesis, Université Laval, 2005. http://www.theses.ulaval.ca/2005/23250/23250.pdf.
Повний текст джерелаThe purpose of this thesis is twofold. In the first place it presents a comparative study of the advantages and drawbacks of several approaches to insure software safety and security. It then focuses more particularly on combining static analyses and dynamic monitoring in order to produce a more powerful security architecture. The first chapters of the thesis present an analytical review of the various static, dynamic and hybrid approaches that can be used to secure a potentially malicious code. The advantages and drawbacks of each approach are thereby analyzed and the field of security properties that can be enforced by using it are identified. The thesis then focuses on the possibility of combining static and dynamic analysis through a new hybrid approach. This approach consists in a code instrumentation, that only alters those parts of a program where it is necessary to do so to insure the respect of a user-defined security policy expressed in a set of modal μ-calculus properties. this instrumentation is guided by a static analysis based on a type and effect system. The effects represent the accesses made to pretested system ressources.
Palisse, Aurélien. "Analyse et détection de logiciels de rançon." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S003/document.
Повний текст джерелаThis phD thesis takes a look at ransomware, presents an autonomous malware analysis platform and proposes countermeasures against these types of attacks. Our countermeasures are real-time and are deployed on a machine (i.e., end-hosts). In 2013, the ransomware become a hot subject of discussion again, before becoming one of the biggest cyberthreats beginning of 2015. A detailed state of the art for existing countermeasures is included in this thesis. This state of the art will help evaluate the contribution of this thesis in regards to the existing current publications. We will also present an autonomous malware analysis platform composed of bare-metal machines. Our aim is to avoid altering the behaviour of analysed samples. A first countermeasure based on the use of a cryptographic library is proposed, however it can easily be bypassed. It is why we propose a second generic and agnostic countermeasure. This time, compromission indicators are used to analyse the behaviour of process on the file system. We explain how we configured this countermeasure in an empiric way to make it useable and effective. One of the challenge of this thesis is to collate performance, detection rate and a small amount of false positive. To finish, results from a user experience are presented. This experience analyses the user's behaviour when faced with a threat. In the final part, I propose ways to enhance our contributions but also other avenues that could be explored
Lespérance, Pierre-Luc. "Détection des variations d'attaques à l'aide d'une logique temporelle." Thesis, Université Laval, 2006. http://www.theses.ulaval.ca/2006/23481/23481.pdf.
Повний текст джерелаBeaucamps, Philippe. "Analyse de Programmes Malveillants par Abstraction de Comportements." Phd thesis, Institut National Polytechnique de Lorraine - INPL, 2011. http://tel.archives-ouvertes.fr/tel-00646395.
Повний текст джерелаTa, Thanh Dinh. "Modèle de protection contre les codes malveillants dans un environnement distribué." Thesis, Université de Lorraine, 2015. http://www.theses.fr/2015LORR0040/document.
Повний текст джерелаThe thesis consists in two principal parts: the first one discusses the message for- mat extraction and the second one discusses the behavioral obfuscation of malwares and the detection. In the first part, we study the problem of “binary code coverage” and “input message format extraction”. For the first problem, we propose a new technique based on “smart” dynamic tainting analysis and reverse execution. For the second one, we propose a new method using an idea of classifying input message values by the corresponding execution traces received by executing the program with these input values. In the second part, we propose an abstract model for system calls interactions between malwares and the operating system at a host. We show that, in many cases, the behaviors of a malicious program can imitate ones of a benign program, and in these cases a behavioral detector cannot distinguish between the two programs
Книги з теми "Classification des logiciels malveillants"
Inc, ebrary, ed. Malware analyst's cookbook and dvd: Tools and techniques for fighting malicious code. Indianapolis, Ind: Wiley Pub., Inc, 2011.
Знайти повний текст джерелаBowden, Mark. Worm: The first digital world war. New York: Grove, 2013.
Знайти повний текст джерелаPaget, François. Vers & virus: Classification, lutte anti-virale et perspectives. Paris: Dunod, 2005.
Знайти повний текст джерелаPayandeh, B. User's guide to FEXPERT: Forest ecosystem classification made easy. [Sault Ste. Marie, Ont.]: Great Lakes Forestry Centre, 1995.
Знайти повний текст джерелаDunham, Ken, Shane Hartman, and Manu Quintans. Android Malware and Analysis. Taylor & Francis Group, 2014.
Знайти повний текст джерелаMorales, Jose Andre, Tim Strazzere, Ken Dunham, Shane Hartman, and Manu Quintans. Android Malware and Analysis. Auerbach Publishers, Incorporated, 2014.
Знайти повний текст джерелаMorales, Jose Andre, Tim Strazzere, Ken Dunham, Shane Hartman, and Manu Quintans. Android Malware and Analysis. Auerbach Publishers, Incorporated, 2014.
Знайти повний текст джерелаIntelligent Text Categorization And Clustering. Springer, 2008.
Знайти повний текст джерелаЧастини книг з теми "Classification des logiciels malveillants"
HONBA HONBA, Cédric. "Sémiotique et archive d’images." In Corpus audiovisuels, 125–46. Editions des archives contemporaines, 2022. http://dx.doi.org/10.17184/eac.5705.
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