TY - GEN
T1 - IoT vulnerability data crawling and analysis
AU - Shiaeles, Stavros
AU - Kolokotronis, Nicholas
AU - Bellini, Emanuele
PY - 2019/8/29
Y1 - 2019/8/29
N2 - Internet of Things (IoT) is a whole new ecosystem comprised of heterogeneous connected devices -i.e. computers, laptops, smart-phones and tablets as well as embedded devices and sensors-that communicate to deliver capabilities making our living, cities, transport, energy, and many other areas more intelligent. The main concerns raised from the IoT ecosystem are the devices poor support for patching/updating and the poor on-board computational power. A number of issues stem from this: inherent vulnerabilities and the inability to detect and defend against external attacks. Also, due to the nature of their operation, the devices tend to be rather open to communication, which makes attacks easy to spread once reaching a network. The aim of this research is to investigate if it is possible to extract useful results regarding attacks' trends and be able to predict them, before it is too late, by crawling Deep/Dark and Surface web. The results of this work show that is possible to find the trend and be able to act proactively in order to protect the IoT ecosystem.
AB - Internet of Things (IoT) is a whole new ecosystem comprised of heterogeneous connected devices -i.e. computers, laptops, smart-phones and tablets as well as embedded devices and sensors-that communicate to deliver capabilities making our living, cities, transport, energy, and many other areas more intelligent. The main concerns raised from the IoT ecosystem are the devices poor support for patching/updating and the poor on-board computational power. A number of issues stem from this: inherent vulnerabilities and the inability to detect and defend against external attacks. Also, due to the nature of their operation, the devices tend to be rather open to communication, which makes attacks easy to spread once reaching a network. The aim of this research is to investigate if it is possible to extract useful results regarding attacks' trends and be able to predict them, before it is too late, by crawling Deep/Dark and Surface web. The results of this work show that is possible to find the trend and be able to act proactively in order to protect the IoT ecosystem.
KW - Crawling
KW - Dark web
KW - Deep web
KW - IoT
KW - Trends
KW - Vulnerabilities
UR - http://www.scopus.com/inward/record.url?scp=85072784431&partnerID=8YFLogxK
UR - http://www.wikicfp.com/cfp/servlet/event.showcfp?copyownerid=90704&eventid=81483
UR - https://pearl.plymouth.ac.uk/
U2 - 10.1109/SERVICES.2019.00028
DO - 10.1109/SERVICES.2019.00028
M3 - Conference contribution
SN - 978-1-7281-3852-7
T3 - 2019 IEEE World Congress on Services (SERVICES)
SP - 78
EP - 83
BT - 2019 IEEE World Congress on Services (SERVICES)
A2 - Chang, Carl K.
A2 - Chen, Peter
A2 - Goul, Michael
A2 - Oyama, Katsunori
A2 - Reiff-Marganiec, Stephan
A2 - Sun, Yanchun
A2 - Wang, Shangguang
A2 - Wang, Zhongjie
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 IEEE World Congress on Services
Y2 - 8 July 2019 through 13 July 2019
ER -