Autor: Rawski, Mariusz - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Network Topology Mutation as Moving Target Defense for Corporate Networks
Autorzy:: Rawski, Mariusz
Powiązania:: https://bibliotekanauki.pl/articles/227178.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: cybersecurity
MTD
SDN
NFV
Opis:: The paper introduces a topology mutation – the novel concept in Moving Target Defense (MTD). MTD is a new technique that represents a significant shift in cyber defense. Traditional cybersecurity techniques have primarily focused on the passive defense of static networks only. In MTD approach cyber attackers are confused by making the attack surface dynamic, and thus harder to probe and infiltrate. The emergence of Network Functions Virtualization (NFV) and Software-Defined Networking (SDN) technology has opened up new possibilities in network architecture management. The application of combined NFV and SDN technologies provides a unique platform for implementing MTD techniques for securing the network infrastructure by morphing the logical view of the network topology.
Źródło:: International Journal of Electronics and Telecommunications; 2019, 65, 4; 571-577
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

Artykuł

Skocz do pozycji: 2.

Tytuł:: Email Phishing Detection with BLSTM and Word Embeddings
Autorzy:: Wolert, Rafał
Rawski, Mariusz
Powiązania:: https://bibliotekanauki.pl/articles/27311939.pdf
Data publikacji:: 2023
Wydawca:: Polska Akademia Nauk. Czasopisma i Monografie PAN
Tematy:: phishing
BLSTM
word embeddings
Opis:: Phishing has been one of the most successful attacks in recent years. Criminals are motivated by increasing financial gain and constantly improving their email phishing methods. A key goal, therefore, is to develop effective detection methods to cope with huge volumes of email data. In this paper, a solution using BLSTM neural network and FastText word embeddings has been proposed. The solution uses preprocessing techniques like stop-word removal, tokenization, and padding. Two datasets were used in three experiments: balanced and imbalanced, whereas in the imbalanced dataset, the effect of maximum token size was investigated. Evaluation of the model indicated the best metrics: 99.12% accuracy, 98.43% precision, 99.49% recall, and 98.96% f1-score on the imbalanced dataset. It was compared to an existing solution that uses the DL model and word embeddings. Finally, the model and solution architecture were implemented as a browser plug-in.
Źródło:: International Journal of Electronics and Telecommunications; 2023, 69, 3; 485--491
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

Artykuł

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