[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118387-en":3,"doc-seo-118387-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118387,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Identifying Suspicious Behavior of Network Devices Using Machine Learning Methods","This bachelor thesis investigates how machine learning techniques can detect suspicious behavior in network devices. Driven by the growing complexity of cyber threats, it aims to identify abnormal activities that may signal security risks or malicious network behavior. The work studies and evaluates clustering and related unsupervised approaches, including feature selection and anomaly-oriented analysis. It further applies suitable visualization methods to project data into lower dimensions and to display detected anomalies against the remaining normal behavior.","Bachelor Project  \nCzech Technical University in Prague  \nFaculty of Electrical Engineering, Department of Telecommunications  \nIdentifying suspicious behavior of network devices using machine learning methods  \nKatsiaryna Zubaryk  \nSupervisor: Ing. Pavel Bezpalec, Ph.D.  \nMay 2023  \nBACHELOR‘S THESIS ASSIGNMENT  \nI. Personal and study details  \nStudent's name: Zubaryk Katsiaryna Personal ID number: 491881  \nFaculty / Institute: Faculty of Electrical Engineering  \nDepartment / Institute: Department of Telecommunications Engineering Study program: Electronics and Communications  \nII. Bachelor’s thesis details  \nBachelor’s thesis title in English:  \nIdentifying Suspicious Behavior of Network Devices Using Machine Learning Methods  \nBachelor’s thesis title in Czech:  \nIdentifikace podezřelého chování síťového zařízení pomocí metod strojového učení  \nGuidelines:  \nAnalyse security incident detection and prevention systems. Design the coding of analysed incidents into an appropriate data space.  \nPerform a search for data clustering methods suitable for the proposed space. Assess their suitability for anomaly detection and apply one method in combination with the proposed metrics on the specified data. Find suitable visualization techniques to project the data into a lower dimension and use them to display the detected anomalies against the remaining data.  \nBibliography / sources:  \n[1] AGGARWAL, CHARU C. a CHANDAN K. REDDY, ed. Data clustering: algorithms and applications. Boca Raton, Fla.: CRC Press, c2014, xxvi, 622 s. Chapman & Hall/CRC data mining and knowledge discovery series. ISBN 978-1-4665-5821-2.  \n[2] AXELSSON, Stefan. (2000) . Intrusion Detection Systems: A Survey and Taxonomy.  \n[3] KOHOUT, J. , ŠKARDA, Č . , SHCHERBIN, K. , KOPP, M. , & BRABEC, J. (2021) . A framework for comprehensible multi-modal detection of cyber threats.  \nName and workplace of bachelor’s thesis supervisor:  \nIng. Pavel Bezpalec, Ph.D. Department of Telecommunications Engineering FEE  \nName and workplace of second bachelor’s thesis supervisor or consultant:  \nDate of bachelor’s thesis assignment: 01.03.2023 Deadline for bachelor thesis submission: 20.05.2022 Assignment valid until: 16.02.2025  \nIng. Pavel Bezpalec, Ph. D. Head of department’s signature prof. Mgr. Petr Páta, Ph. D.  \nSupervisor’s signature Dean’s signature  \nIII. Assignment receipt  \nThe student acknowledges that the bachelor’s thesis is an individual work. The student must produce her thesis without the assistance of others,  \nwith the exception of provided consultations. Within the bachelor’s thesis, the author must state the names of consultants and include a list of references.  \n.  \nDate of assignment receipt Student’s signature  \niii  \nAbstract  \nThe purpose of this thesis is to investigate the application of machine learning techniques to detect suspicious behavior of network devices. With the increasing complexity of cyber threats, it has become imperative to develop effective methods to detect abnormal activities that could potentially indicate security or malicious network activities. This research focuses on the study of machine learning algorithmsand approaches to detect and classify suspicious behavior of network devices.  \nKeywords: Clustering, Unsupervised, feature selection    \nKeywords:  \nSupervisor: Ing. Pavel Bezpalec, Ph.D. Technická 2, Praha  \nAbstrakt  \nCílem této práce je prozkoumat použití technik strojového učení k detekci podezřelého chování síťových zařízení . S rostoucí složitostí kybernetických hrozeb se stalo nezbytným vyvinout účinné metody pro detekci abnormálních aktivit, které by mohly potenciálně indikovat bezpečnostní nebo škodlivé aktivity v síti. Tento výzkum se zaměřuje na studium algoritmů a přístupů strojového učení k detekci aklasifikaci podezřelého chování síťových zařízení .  \nKlíčová slova: Clusterind, učení bezdohledu, výběr prvků . . .  \nKlíčová slova:  \nPřeklad názvu: Identifikace podezřelého chování sítového zařízení pomocí metod ","cbCaif7bWutoutqA","https://ap.wps.com/l/cbCaif7bWutoutqA","pdf",2048372,1,66,"English","en",105,"# Introduction\n## Unsupervised identification\n## Supervised identification\n## Selected identification for thesis\n# Introduction to data analysis for unsupervised identification\n## Programming language\n## Description of the dataset\n## Data analysis\n## Visualisation technique\n# Feature selection\n## Correlation matrix\n# Clustering\n## Distances\n## K-means clustering\n## Hierarchical Agglomerative clustering\n## Visualisation technique\n# Conclusion\n# Bibliography\n# Appendix","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To apply machine learning techniques to detect suspicious behavior of network devices by finding abnormal activities that may indicate security or malicious network behavior.\"},{\"question\":\"Which machine learning approach is emphasized for identification?\",\"answer\":\"Unsupervised identification is a key focus, including techniques such as clustering to separate normal and suspicious patterns.\"},{\"question\":\"How does the thesis validate and interpret detected anomalies?\",\"answer\":\"It uses feature selection and anomaly detection within clustering methods, then employs visualization techniques to project data into lower dimensions and display detected anomalies relative to remaining data.\"}]","Identifying Suspicious Behavior of Network Devices Using Machine Learning Methods | 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