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دسته بندی:
اینترنت اشیاء - Internet of Things
سال انتشار:
2022
عنوان انگلیسی مقاله:
Deep Q learning based secure routing approach for OppIoT networks
ترجمه فارسی عنوان مقاله:
رویکرد مسیریابی ایمن مبتنی بر یادگیری Q برای شبکه های OppIoT
منبع:
ScienceDirect- Elsevier- Internet of Things, 20 (2022) 100597: doi:10:1016/j:iot:2022:100597
نویسنده:
Nisha Kandhoul
چکیده انگلیسی:
Opportunistic IoT (OppIoT) networks are a branch of IoT where the human and machines
collaborate to form a network for sharing data. The broad spectrum of devices and ad-hoc
nature of connections, further alleviate the problem of network and data security. Traditional
approaches like trust based approaches or cryptographic approaches fail to preemptively secure
these networks. Machine learning (ML) approaches, mainly deep reinforcement learning (DRL)
methods can prove to be very effective in ensuring the security of the network as they
are profoundly capable of solving complex and dynamic problems. Deep Q-learning (DQL)
incorporates deep neural network in the Q learning process for dealing with high-dimensional
data. This paper proposes a routing approach for OppIoT, DQNSec, based on DQL for securing the
network against attacks viz. sinkhole, hello flood and distributed denial of service attack. The
actor–critic approach of DQL is utilized and OppIoT is modeled as a Markov decision process
(MDP). Extensive simulations prove the efficiency of DQNSec in comparison to other ML based
routing protocols, viz. RFCSec, RLProph, CAML and MLProph.
Keywords: OppIoT | Reinforcement learning | Deep learning | Deep Q-learning | Markov decision process | Sinkhole attack | Hello flood attack | Distributed denial of service attack
قیمت: رایگان
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