E-viri
Recenzirano
-
Khan, Izhar Ahmed; Moustafa, Nour; Razzak, Imran; Tanveer, M.; Pi, Dechang; Pan, Yue; Ali, Bakht Sher
Future generation computer systems, February 2022, 2022-02-00, Letnik: 127Journal Article
The Internet of Medical Things (IoMT) is increasingly replacing the traditional healthcare systems. However, less focus has been paid to their security against cyber-threats in the implementation of the IoMT and its networks. One of the key reasons can be the challenging task of optimizing typical security solutions to the IoMT networks. And despite the rising admiration of machine learning and deep learning methods in the cyber-security domain (e.g., a threat detection system), most of these methods are acknowledged as a black-box model. The explainable AI (XAI) has become progressively vital to understand the employed learning models to improve trust level and empower security experts to interpret the prediction decisions. The authors propose a highly efficient model named XSRU-IoMT, for effective and timely detection of sophisticated attack vectors in IoMT networks. The proposed model is developed using novel bidirectional simple recurrent units (SRU) using the phenomenon of skip connections to eradicate the vanishing gradient problem and achieve a fast training process in recurrent networks. We also explore the concepts of XAI to improve trust level by providing explanations of the predictive decisions and enabling humans and security experts to understand the causal reasoning and underlying data evidence. The evaluation results on the ToN_IoT dataset demonstrate the effectiveness and superiority of the proposed XSRU-IoMT model as compared to the state-of-the-art compelling detection models, suggesting its usefulness as a viable deployment model in real-IoMT networks. •The concepts of XAI are explored enabling security experts to interpret underlying data evidence.•The analysis of importance of features is explored for better threat and intrusion discovery.•A novel bidirectional SRU model using skip connections, is proposed for security of IoMT networks.•This technique is capable to alleviate vanishing gradient problem and having fast training time.•Highly effectual in detecting several kinds of cyber-threats against IoMT-driven SHS.
Avtor
![loading ... loading ...](themes/default/img/ajax-loading.gif)
Vnos na polico
Trajna povezava
- URL:
Faktor vpliva
Dostop do baze podatkov JCR je dovoljen samo uporabnikom iz Slovenije. Vaš trenutni IP-naslov ni na seznamu dovoljenih za dostop, zato je potrebna avtentikacija z ustreznim računom AAI.
Leto | Faktor vpliva | Izdaja | Kategorija | Razvrstitev | ||||
---|---|---|---|---|---|---|---|---|
JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
Baze podatkov, v katerih je revija indeksirana
Ime baze podatkov | Področje | Leto |
---|
Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
---|
Vir: Osebne bibliografije
in: SICRIS
To gradivo vam je dostopno v celotnem besedilu. Če kljub temu želite naročiti gradivo, kliknite gumb Nadaljuj.