BRIOT: Design of Low Cost IOT Embedded System in Cyber Security
Keywords:
Behavior Rules, Cyber-Physical Systems, IoT, SpecificationBased Intrusion DetectionAbstract
We propose a light-way of specification-based misbehavior detection
technique to detect misbehavior efficiently and effectively for IoT
embedded cyber physical system. “Why should I protect my private
network? I’ve got no critical information on my computer, no sensitive
data”. Are your emails really public? Don’t you have some photos you
don’t want to upload to Facebook, because they’re private. Do you
really don’t care if you computer is hijacked and used to attack other
PCs or act as a spam server? I don’t think you’re so careless but maybe
you think, that setting up a secure network environment is expensive
and really difficult. The key concept of our approach is to model a system with which misbehavior of an IoT device manifested as a result of
attacks exploiting the vulnerability exposed may be detected through
automatic model, we will see how to create a network gateway with
a firewall, DHCP and DNS server, and a Network Intrusion Detection
System (NIDS), entirely based on a Raspberry Pi.
References
Berthier R, Sanders WH. Specification-based Intrusion
Detection for Advanced Metering Infrastructures,” 17th
IEEE Pacific Rim Int.Symp. Dependable Computing, pp.
-193, 2011.
Bezemskij A, Loukas G, Anthony RJ. Behaviour based
anomaly detection of cyber-physical attacks on a robotic
vehicle. IEEE Symposium on Cyberspace and Security.
; 1-8.
Chen IR, Bao F, Guo J. Trust-based Service Management
for Social Internet of Things Systems. IEEE Transactions
on Dependable and Secure Computing 2016; 13(6):684-
DaSilva A et al. Decentralized intrusion detection in
wireless sensor networks,” 1st ACM inter. workshop
on quality of service & security in wireless and mobile
networks. 2005; 16–23.
Hanna S. A path to securing billions of insecure devices,”https://www.trustedcomputinggroup.org/wpcontent/uploads/Trusted-Computing-for-IoT-ESC-2015_final.pdf. 2015.
Hong J, Liu CC, Govindarasu M. Integrated Anomaly
Detection for Cyber Security of the Substations. IEEE
Trans. Smart Grid 2014; 5(4); 1643-1653.
Huda S et al. Defending unknown attacks on cyber-physical systems by semi-supervised approach and availableunlabeled data,” Information Sciences, vol. 379, 2017,
pp. 211-228.
Ioannis K, Dimitriou T, Freiling F. Towards intrusion
detection in wireless sensor networks 13th European
Wireless Conference, 2007.
Jokar P, Nicanfar H, VCM. Leung, “Specification-based
Intrusion Detection for Home Area Networks in Smart
Grids,” IEEE Int. Conf. on Smart Grid Communications,
Kosek AM. Contextual anomaly detection for cyber-physical security in smart grids based on an artificial neural network model. IEEE Workshop on CyberPhysical Security and Resilience in Smart Grids, 2016.
