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Browsing by Subject "network security"

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  • thumbnail.journal.alt
    Item type:Thesis, Access status: Restricted ,
    Bezpieczeństwo sieci w systemach typu Linux/Unix
    (Data obrony: 2017-01-24) Stypuła, Jan
    Wydział Geologii, Geofizyki i Ochrony Środowiska
    W pracy omówiono w podstawowym zakresie kwestie bezpieczeństwa sieciowego. W pierwszej kolejności przedstawiono najpopularniejsze narzędzia używane przez administratorów. Następnie wyjaśniono zasadę działania ataków od strony teoretycznej oraz pokazano sposób ich praktycznego wykonania. Były to między innymi ARP Spoof Attack, SSLStrip Attack oraz DoS Attack. Dodatkowo poruszone zostały kwestie związanie z ochroną przed atakami oraz podniesieniem poziomu zabezpieczeń.
  • thumbnail.journal.alt
    Item type:Article, Access status: Open Access ,
    Gramian angular field transformation-based intrusion detection
    (Wydawnictwa AGH, 2022) Terzi, Duygu Sinanc
    Cyber threats are increasing progressively in their frequency, scale, sophistication, and cost. The advancement of such threats has raised the need to enhance intelligent intrusion-detection systems. In this study, a different perspective has been developed for intrusion detection. Gramian angular fields were adapted to encode network traffic data as images. Hereby, a way to reveal bilateral feature relationships and benefit from the visual interpretation capability of deep-learning methods has been opened. Then, image-encoded intrusions were classified as binary and multi-class using convolutional neural networks. The obtained results were compared to both conventional machine-learning methods and related studies. According to the results, the proposed approach surpassed the success of traditional methods and produced success rates that were close to the related studies. Despite the use of complex mechanisms such as feature extraction, feature selection, class balancing, virtual data generation, or ensemble classifiers in related studies, the proposed approach is fairly plain – involving only data-image conversion and classification. This shows the power of simply changing the problem space.